Tumor-driven stromal reprogramming in the pre-metastatic lymph node

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Lymph nodes are continuously exposed to such signals through the flow of afferent lymph, allowing the potential reprograming of lymphoid tissue stroma in support of metastases or immunosuppression. The objective of this study was therefore to better characterize tumor-driven transcriptomic changes occurring to specific stromal populations within the tumor-draining lymph node. Methods We utilize single cell RNA sequencing of dissociated LN tissue extracted from tumor-bearing and naïve mice to profile the reprograming of tissue stroma within the pre-metastatic lymph node. Results Resulting data provides transcriptomic evidence of tumor-induced imprinting on marginal reticular cells (MRCs) and floor lymphatic endothelial cells (fLECs) populating the subcapsular sinus. These alterations appear to be unique to the tumor-draining LN and are not observed during inflammatory antigenic challenge. Notably, MRCs exhibit characteristics reminiscent of early desmoplastic CAF differentiation, fLECs engage distinct chemoattractant pathways thought to facilitate recruitment of circulating cancer cells, and both stromal populations exhibit signs of metabolic reprograming and immune-modulating potential. Conclusions Cumulatively, these findings build upon existing literature describing pre-metastatic niche formation and offer several promising avenues for future exploration. 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F1000Research 2024, 13 :223 ( https://doi.org/10.12688/f1000research.145171.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] Michelle Piquet 1 , David A Ruddy 1 , Viviana Cremasco 2 , Jonathan Chang https://orcid.org/0000-0001-6894-7127 2 Michelle Piquet 1 , David A Ruddy 1 , Viviana Cremasco 2 , Jonathan Chang https://orcid.org/0000-0001-6894-7127 2 PUBLISHED 27 Mar 2024 Author details Author details 1 Oncology Innovative Targets and Technologies, Novartis, Cambridge, MA, 02139, USA 2 Oncology Translational Research, Novartis, Cambridge, MA, 02139, USA Michelle Piquet Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing David A Ruddy Roles: Conceptualization, Resources, Supervision, Writing – Review & Editing Viviana Cremasco Roles: Conceptualization, Resources, Supervision, Writing – Review & Editing Jonathan Chang Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Oncology gateway. This article is included in the Cell & Molecular Biology gateway. This article is included in the Advances in Fibroblast Research collection. Abstract Background Metastatic dissemination is critically reliant on the formation of a receptive niche, a process which is thought to rely on signals derived from the primary tumor. Lymph nodes are continuously exposed to such signals through the flow of afferent lymph, allowing the potential reprograming of lymphoid tissue stroma in support of metastases or immunosuppression. The objective of this study was therefore to better characterize tumor-driven transcriptomic changes occurring to specific stromal populations within the tumor-draining lymph node. Methods We utilize single cell RNA sequencing of dissociated LN tissue extracted from tumor-bearing and naïve mice to profile the reprograming of tissue stroma within the pre-metastatic lymph node. Results Resulting data provides transcriptomic evidence of tumor-induced imprinting on marginal reticular cells (MRCs) and floor lymphatic endothelial cells (fLECs) populating the subcapsular sinus. These alterations appear to be unique to the tumor-draining LN and are not observed during inflammatory antigenic challenge. Notably, MRCs exhibit characteristics reminiscent of early desmoplastic CAF differentiation, fLECs engage distinct chemoattractant pathways thought to facilitate recruitment of circulating cancer cells, and both stromal populations exhibit signs of metabolic reprograming and immune-modulating potential. Conclusions Cumulatively, these findings build upon existing literature describing pre-metastatic niche formation and offer several promising avenues for future exploration. READ ALL READ LESS Keywords Tumor Draining Lymph Node, scRNAseq, metastasis, Stromal Cell, Fibroblast, Endothelial Cell, pre-metastatic Niche Corresponding Author(s) Jonathan Chang ( [email protected] ) Close Corresponding author: Jonathan Chang Competing interests: All authors are employees of Novartis. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Piquet M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Piquet M, Ruddy DA, Cremasco V and Chang J. Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.12688/f1000research.145171.1 ) First published: 27 Mar 2024, 13 :223 ( https://doi.org/10.12688/f1000research.145171.1 ) Latest published: 07 Oct 2025, 13 :223 ( https://doi.org/10.12688/f1000research.145171.3 )  There is a newer version of this article available. Suppress this message for one day. Introduction Accumulation of genetic changes underpinning malignant transformation is the trigger point of cancer initiation. 1 , 2 However, these cell intrinsic alterations alone are insufficient for successful growth and progression of solid tumors. Rather, survival of the nascent tumor requires a co-evolved and permissive microenvironmental niche, wherein tissue stroma must be coopted to provide the structural, metabolic, and immune evasive support network necessary for tumor growth and eventual metastatic dissemination. 1 – 3 The therapeutic potential of exploiting this interdependence has spurred significant effort to unravel the complexities of the tumor microenvironment, though the processes by which normal tissues are rendered tumor-permissive remain incompletely understood. Specific microenvironmental requirements likewise exist for the formation of secondary tumor growth, as the engraftment and survival of circulating cancer cells is equally reliant on the existence of a receptive niche. Reflective of these exigencies, fewer than 0.01% of circulating tumor cells are thought to successfully establish tumor metastases. 4 Moreover, the site of eventual metastatic engraftment is non-random, with specific organotropic patterns varying by cancer indication – an observation which serves as the basis of Stephen Paget’s longstanding “seed and soil” hypothesis. 5 – 7 Mounting evidence now suggests that successful metastasis additionally follows a reprograming of stromal elements at these distant sites, mediated by secretion of factors and extracellular vesicles originating from the primary tumor. 8 – 13 The resulting modifications form a more favorable microenvironment, termed the premetastatic niche (PMN). 9 Decoding these microenvironmental alterations and identifying the affected cell types may open a window to the requisite conditions for early tumor cell engraftment, growth, and immune evasion, and potentially reveal new points of therapeutic intervention. However, in-depth study of these processes may be experimentally challenging given variability in exact timing and location of metastases. More predictable is the spread of cancer cells to the regional lymphatic lymph node (LN), which are frequently the first site of tumor metastasis in most forms of carcinoma. 14 This prevalence of metastatic spread to the draining LN is almost certainly a factor of direct exposure to tumor-derived signals, as such signals pool within the afferent lymphatics before invariably draining to the sentinel node. 15 – 19 Moreover, lymphangiogenesis and increased lymph flow is believed to be an essential step that precedes LN metastasis. 15 , 20 , 21 While exposure to afferent lymphatic flow facilitates the requisite acquisition of antigens and tissue signals for functional adaptive immunity, drainage of tumor-derived factors has the potential to modulate these responses, thereby conditioning the pre-metastatic LN in favor of tumor support and immune suppression. 22 Indeed, recent reports have demonstrated tumor-induced alterations to LN fibroblastic reticular cells (FRCs) – a stromal population which comprises the essential infrastructure supporting lymphocyte homeostasis and coordinating immune interactions. 23 – 25 Changes to FRCs of the tumor draining LN (tdLN) included increased activation and proliferation, disrupted homeostatic chemokine production driving aberrant immune cell localization, and altered matrix production. However, while the observed reprograming of LN stroma has a putative role in metastasis support and immune suppression, many of the described alterations overlap with the stromal response program associated with general inflammation and infection. 26 In this regard, the degree to which these previously reported findings are in fact unique or specific to tumor-derived factors requires further examination. Here we expand on previous exploration of tumor-induced reprogramming through single cell transcriptomic analysis of stroma-enriched samples, enabling a more robust exploration of subset-specific transcriptional shifts. We demonstrated exquisite specificity in stromal reprograming associated with the premetastatic tdLN, wherein select subsets of FRCs and lymphatic endothelial cells (LECs) populating the LN subcapsular sinus (SCS) are differentially affected by tumor-derived signals. Amongst these findings, we confirm functional alterations reflective of matrix remodeling, identify engagement of distinct axes of chemotaxis, and identify transcriptional signs of altered metabolism and immunomodulatory potential. Importantly, these alterations were additionally compared to LN stromal responses following antigenic challenge in the absence of live tumor cells, allowing confirmation of the tumor-specificity of these response programs. Ultimately, we believe these findings help to broaden our understanding of pre-metastatic communication and tissue imprinting, and may direct future experimental exploration of metastatic niche dependencies with potential for therapeutic intervention. Methods Ethics statement All animal work was approved and performed in accordance with the guidelines from the Institutional Care and Use Committee (IACUC) at Novartis Institutes for BioMedical Research (Protocol 20 IMO 035) and in compliance with the Guide for the Care and Use of Laboratory Animals. All efforts were made to ameliorate any potential suffering of animals. Mice Experiments were performed in eight-week-old, sex-matched C57Bl/6 mice purchased from Charles River Laboratories. Mice were maintained under specific pathogen-free conditions in accordance with institutional and National Institute of Health guidelines. Cell lines MC38 cells were received from NCI under MTA# 38699-15, expanded, frozen and collected in the NIBR cell line repository. Cell lines were maintained in Dulbecco’s Modified Eagle Medium (Gibco, 11965-092) containing 10% heat-inactivated FBS (VWR, 1500-500), 1% Penicillin-Streptomycin (Gibco, 15140-122), and 1% L-glutamine (Gibco, 25030-81) for one week prior to implant. Prior to inoculation into recipient animals, cell lines were tested and found to be free of mycoplasma and viral contamination in the IMPACT VIII PCR assay panel (IDEXX BioResearch, Missouri). Tumor models The present study used tissues collected from naïve mice (n=4), mice bearing live MC38 tumors (n=3), or fixed MC38 tumor cells (n=3). Mice were randomly assigned to treatment groups. In preparation for inoculation, MC38 cells were detached using 0.25% trypsin, washed, and resuspended in sterile PBS prior to implantation. For the “fixed cell” control treatment condition, MC38 cells were additionally incubated in 4% paraformaldehyde in sterile PBS for 20 minutes at a concentration of 10 6 cells/mL before washing and resuspension in sterile PBS. For both conditions, 5 × 10 5 cells were implanted subcutaneously on the animal’s lower-right flank. Animals are anesthetized by isoflurane inhalation prior to treatment. Mice were monitored for tumor growth, changes in body weight and condition 3 times per week. Mice were euthanized by CO 2 inhalation on day 12 after treatment, and both the tumor-draining and contralateral non-draining inguinal lymph nodes were collected. No animals were excluded from analysis. Enzymatic digestion of lymph nodes Single-cell suspensions of LNs were prepared for analysis by flow cytometry or single cell RNAseq. LNs were dissected and incubated at 37 °C in RPMI containing 0.1 mg ml −1 DNase I (Invitrogen), 0.2 mg ml −1 collagenase P (Roche) and 0.8 mg ml −1 dispase (Roche) for 50–60 min, as previously described. Liberated cells in suspension were collected into cold medium containing 2% fetal bovine serum and 5 mM EDTA every 15–20 min and replaced with fresh digestion medium. Following complete digestion of the LN, cells were passed through a 70-μm cell strainer, washed and resuspended in cold PBS. Flow cytometric analysis Single-cell suspensions were resuspended in flow cytometry buffer (PBS, 2% FBS, and 2 mmol/L EDTA). Cells were blocked with Fc block and then stained with different fluorochrome-conjugated antibodies for 20 min on ice. The following antibodies were used (clone, fluorophore, concentration): anti-CD45 (30-F11, BUV396, 1:500), anti-CD31 (390, Pacific Blue, 1:300), and anti-PDPN (8.1.1, APC, 1:300). CountBright Absolute Counting Beads (Invitrogen, C36950) were included for quantification of cell counts. Samples were analyzed on a BD flow cytometry Fortessa instrument and analyzed with FlowJo. Raw data available. 27 Stromal enrichment Single cells obtained form LN digestion were resuspended in mouse FC block (Miltenyi #130-092-575). Biotinylated PDPN (8.1.1, 1:100) antibody was used for positive selection, using the EasySep Selection Kit (Stemcell Technologies #18559). Cells were then washed in cold PBS, counted, and resuspended at 10 6 cells/mL for sequencing. Single-cell RNA sequencing and data pre-processing The 10x Genomics Chromium Single Cell 3’ Reagents v3 kit (10× Genomics, Pleasanton, CA) was used under standard conditions and volumes to process cell suspensions for 3’ transcriptional profiling. The volumes of cell suspensions were calculated to achieve a target cell recovery of 6000 cells for all dissociated samples, following the manufacturer’s guidelines. The resultant purified cDNAs were quantified on an Agilent Tapestation (Agilent, Santa Clara, CA) using High Sensitivity D5000 ScreenTapes and Reagents. The final single cell 3’ libraries were quantified using an Agilent Tapestation using High Sensitivity D1000 ScreenTapes and Reagents. Following this, the libraries were diluted to a concentration of 10 nM in Qiagen Elution Buffer, subjected to denaturation, and loaded onto an Illumina MiSeq at 6 μM, utilizing the MiSeq Reagent Kit v3 (Illumina, San Diego, CA) to assess sample quality and achieve loading normalization for the HiSeq4000. The normalized libraries were loaded onto an Illumina cBOT at a concentration of 160 picomolar and sequenced on a HiSeq4000. The sequencing process included a 28-base-pair first read, followed by two 8-base-pair index reads, and concluded with a 91-base-pair second read, all by using 2 HiSeq4000 SBS kits at 50 cycles each. All sequence intensity files were generated using the Illumina Real Time Analysis software. The resulting intensity files were then subjected to demultiplexing and aligned to the mouse genome, version mm10, using the 10x Genomics CellRanger v3.0.1 software package. Single-cell RNA-Seq data quality control and processing All computational analyses and visualizations were conducted within the R programming environment, using version 3.6.3. The Seurat package (v3.2.3) was employed for quality control and downstream analysis of single-cell RNA-seq data. 28 The count matrices were then subjected to comprehensive analysis using the Seurat workflow. Cells that exhibited low quality characteristics, defined as those with fewer than 200 expressed genes, more than 6000 expressed genes, or exceeding 30% mitochondrial content, were excluded from the dataset. Following this, the count matrices were merged and normalized using SCTransform. Principal component analysis was executed, focusing on 2000 highly variable genes. To select the appropriate number of principal components (PCs), the Elbow plot heuristic was used. A shared nearest neighbor (SNN) graph was constructed using the first 20 PCs through Seurat’s FindNeighbors function, and cell clustering was performed using a Louvain algorithm with FindClusters. A resolution parameter of 0.8 was chosen to generate an extensive collection of cell clusters, effectively capturing diverse cell types. Cluster markers were identified using Seurat’s FindAllMarkers function. Dimensionality reduction was visualized using the uniform manifold approximation and projection (UMAP) method. Clustering and analysis of fLEC, MRC, BRC The dataset was methodically divided into subsets, specifically isolating floor lymphatic endothelial cells (fLEC), marginal reticular cells (MRC), and b-zone reticular cells (BRC). These subsets underwent a re-clustering process using 15 principal components (PCs) at a resolution parameter of 0.3, with the optimal number of PCs determined using the Elbow heuristic. Differential gene expression and pathway enrichment analyses Differential gene expression analyses were conducted on each subset of cells with an average log-fold change above 0.25, as part of the Seurat workflow. This analysis compared Live Draining vs. Live Non-Draining cells. Pathway enrichment analyses were executed using the ClusterProfiler package (v3.14) and its enrichGO function, using Biological Process, Cellular Component, and Molecular Function ontologies. 29 Pathway enrichment was computed for all cluster marker genes within fLEC, MRC, and BRC subsets, specifically focusing on those with an avg_logFC > 0.25 and an adjusted p-value < 0.001. Significantly enriched pathways (adjusted p-value < 0.05) were selected and visually represented using ggplot2. Finally, to effectively illustrate the significance of the top hits derived from the list of differentially expressed genes, violin plots were employed as a visual tool. Results and discussion Tumor-induced shifts in LN stromal composition and transcriptome Healthy tissue stroma is not thought to be inherently permissive to the growth of malignant cells, and the reprogramming of these tissues is believed to represent a requisite step to successful growth and progression of the tumor. Normal tissue fibroblasts, for instance, have long been known to exhibit cancer-restrictive characteristics, and it is therefore likely that the functional attributes of fibroblasts must first be modulated – coopted by the cancer to support rather than restrict growth and invasiveness. 30 – 32 The necessity of such microenvironmental reprogramming likely extends not only to the establishment of the primary tumor, but metastatic lesions as well. Successful engraftment of circulating cancer cells requires a supportive niche, and thus metastatic cancers must orchestrate a second iteration of this reprogramming process within distant tissues. 8 – 13 By design, lymph nodes are strategically positioned to intercept tissue-derived signals and antigen. Established tumors, however, appear to coopt the lymphatic infrastructure to facilitate metastatic dissemination. In this context, lymphatic drainage offers an efficient means of pre-conditioning tissue stroma for metastatic niche formation, and functions as a direct channel for the distribution of signals which may in fact subvert anti-tumor immunity. Indeed, efficiency of lymphatic drainage is positively correlated with metastatic potential, and lymphangiogenesis is a hallmark of metastatic niche formation. 33 In the pre-metastatic lymph node, expansion of both endothelium and FRCs has been documented. However, stromal expansion in LNs is not a tumor-specific response program. Indeed, subcutaneous delivery of fixed tumor cells is sufficient to elicit LN swelling and expansion of FRCs, LECs, and BECs at a level comparable to that observed with live tumor growth ( Figure 1A ). To explore more deeply the functional alterations occurring within the stromal compartment that specifically correlated with live tumor growth, we performed single cell RNA sequencing of cells isolated from draining and non-draining LNs in mice inoculated with either live or fixed MC38 cancer cells. We additionally enriched for PDPN-expressing LECs and FRCs to enable robust subset-level analyses. Within the resultant dataset, we identified 7 distinct clusters of fibroblastic cells and 4 clusters of lymphatic endothelial cells, each of which was annotated based on well-established identity markers ( Figure 1B, E ). 34 – 37 Figure 1. Single cell RNA sequencing of the tumor-draining lymph node: Mice received either live or fixed MC38 cancer cells by subcutaneous injection. Tumor draining and contralateral non-draining inguinal lymph nodes were excised and enzymatically digested for analysis. A) Quantification of cell numbers assessed by flow cytometry (n=5 mice per condition). B) UMAP visualization of total lymph node fibroblasts, colored by cluster identify. C, D) Quantification of the change in population proportion (amongst fibroblast subsets), along with total number of differentially expressed genes. C. dLN vs ndLN of mice growing live tumors. D. dLN vs ndLN of mice receiving fixed tumor cells. E) UMAP visualization of total lymph node lymphatic endothelial cells, colored by cluster identify. F, G) Quantification of the change in population proportion (amongst endothelial subsets), along with total number of differentially expressed genes. F. dLN vs ndLN of mice growing live tumors. G. dLN vs ndLN of mice receiving fixed tumor cells. H) Schematic representation of lymph node architecture and anticipated location of metastases engraftment. Inset depicts a micrometastasis surrounded by the following stromal subsets of interest: fLECs, MRCs, and BRCs. The seeding of micrometastases within tumor-draining lymph nodes predictably occurs within the subcapsular sinus (SCS), where tissues are most readily exposed to lymph borne materials and migrating cancer cells ( Figure 1H ). 38 , 39 Interestingly, we demonstrate here that unique cellular constituents of this specific microanatomical compartment were selectively impacted by the upstream tumor ( Figure 1C, F ). Marginal reticular cells (MRCs) and floor lymphatic endothelial cells (fLECs) exhibited the greatest degree of transcriptional alteration in the tumor-draining LN relative to those of contralateral non-draining LNs. Perivascular cells (PRCs), though not localized to the SCS, likewise exhibited significant transcriptomic changes, which may reflect support of vascular expansion. Unexpectedly, we also found that the overall cellular representation of these populations, along with B-zone reticular cells (BRCs), were disproportionately increased in the tumor-draining LN ( Figure 1C, F ). This appears to differ from the pattern of stromal expansion that occurs in response to normal antigenic challenge. While exposure to fixed tumor cells elicited stromal activation and expansion – as previously demonstrated - this proliferative response seemed to occur proportionately amongst fibroblast and endothelial subsets and involved comparatively fewer transcriptomic alterations ( Figure 1D, G ). Ultimately, we predict that selective induction of transcriptional changes and disproportionate expansion of stromal subsets comprising the SCS in the tdLN reflects the establishment of a niche supportive of metastatic engraftment and survival. MRC and BRCs exhibit elevated ECM production reminiscent of desmoplastic CAFs While the exact microenvironmental preferences of primary and secondary lesions may differ, and the means by which these alterations are driven may be distinct, there are almost certainly commonalities in tumor-induced stromal reprogramming. For instance, the aberrant production of extracellular matrix, characteristic of the desmoplastic response, is broadly observed across cancer indications. These responses are largely driven by conversion of resident fibroblast populations to tumor-supportive cancer associated fibroblasts (CAFs). Within the LN SCS, resident fibroblast populations include primarily MRCs that underly the sinus endothelium, and a proportion of BRCs that populate the interfollicular space. As both populations exhibited shifts specific to the tdLN, we re-clustered each population and performed differential transcriptomic analyses to identify tumor-specific changes ( Figure 2A-D ). Amongst these changes, MRCs most notably exhibited increases in a broad spectrum of ECM components and matrix remodeling enzymes reminiscent of desmoplastic CAFs of the primary tumor, including upregulation of fibronectin, a critical ECM protein involved in development, wound healing, and cancer metastasis ( Figure 2A,B ). 40 Interestingly, PDAC-derived exosomes were recently reported to induce elevated fibronectin expression in the pre-metastatic liver - a process which was found to be essential for niche formation. 41 Figure 2. Transcriptomic features of MRCs, BRCs, and fLECs in the tumor-draining lymph node. A) ( top ) UMAP visualization of MRCs, colored by treatment condition. ( bottom ) Pathways significantly enriched in MRCs of live-tumor-draining LNs relative to non-draining LNs. B) ( top ) Volcano plots of differentially expressed genes between MRCs of draining vs non-draining LNs. Select genes of interest are highlighted. ( bottom ) Violin plots of select genes differentially expressed in MRCs of the live-tumor draining LN. Expression is shown across all four experimental conditions; live draining, live non-draining, fixed draining, and fixed non-draining. C) UMAP visualization of BRCs, colored by treatment condition. D) Volcano plots of differentially expressed genes between BRCs of draining vs non-draining LNs. Select genes of interest are highlighted. E) ( top ) UMAP visualization of fLECs, colored by treatment condition. ( bottom ) Pathways significantly enriched in fLECs of live-tumor-draining LNs relative to non-draining LNs. F) ( top ) Volcano plots of differentially expressed genes between fLECs of draining vs non-draining LNs. Select genes of interest are highlighted. ( bottom ) Violin plots of select genes differentially expressed in fLECs of the live-tumor draining LN. Expression is shown across all four experimental conditions; live draining, live non-draining, fixed draining, and fixed non-draining. G) Dotplot displaying expression of transcripts upregulated across both MRCs and fLECs in the live tdLN. The size of the dot represents percentage of cells expressing transcript, while the color indicates average level of expression within the population. In addition to ECM proteins, matrix metalloproteases (MMPs) likewise play a pivotal role in cancer progression and metastatic dissemination through matrix remodeling. 42 , 43 Along these lines, we observed increases in both MMP2 and MMP9 specifically in MRCs of the tdLN ( Figure 2B ). While it is unclear from transcriptomic signature alone how the structure of the ECM is specifically remodeled in the SCS, or the extent to which this may impact the nascent metastasis, expression of both has previously been linked to niche formation and vascular remodeling in pre-metastatic tissues and are thus of particular interest. 9 , 44 , 45 Interestingly, previous reports have noted significant tumor-induced enlargement of the collagen-rich LN conduits – a branched network of fibers which normally functions as a transport system for small soluble materials through the LN cortex and paracortex. 25 In this context, Increased conduit thickness correlated with greater transport of larger, normally size-excluded molecules, and the authors speculated that these alterations may result in abnormal distribution of tumor-derived materials to deeper areas of the LN parenchyma. This is notably counter to the effects observed during acute inflammation-associated LN expansion. Under such conditions, LN conduits are also structurally disrupted, but size exclusivity of molecular transport through the network is nevertheless maintained. 46 , 47 This distinction is particularly intriguing, as size exclusivity of LN conduits is believed to be a function of plvap-lined gating channels that traverse the sinus-lining floor LECs (fLECs), rather than an intrinsic feature of conduit size itself. 48 Lymph-borne molecules small enough to pass through these trans-endothelial channels subsequently gain access to the conduit network. We thus speculate that matrisomal dysregulation surrounding fLECs and the underlying MRCs, such as those we find describe within this dataset, may in fact account for, or contribute to, this distinct loss of regulated conduit access in tdLNs. BRCs likewise exhibited upregulation of some ECM-related transcripts in the tdLN, however the overall extent of differentially expressed genes in this subset was markedly less expansive ( Figure 2C, D ). Nevertheless, we note that BRC representation within the dataset was significantly greater in the tdLN, possibly reflecting a greater proliferative response without significant functional divergence from baseline properties of BRCs in a resting LN. As BRCs are essential to the homeostatic maintenance of naïve B cells within the LN, proliferative expansion of the BRC subset may functionally mirror the known preferential accumulation of B cells in tumor draining nodes. 49 , 50 In turn, accumulation of B cells within the tdLN has been shown to drive pro-metastatic lymphangiogenesis, and thus expansion of the B cell support network may be an indirect, but requisite factor in support of this process. 21 fLECs engage specific chemotactic cues within the premetastatic LN niche Cell recruitment, migration and positioning into and within the LN is a highly regulated process, guided by an array of precisely coordinated chemotactic gradients. Principal among these chemotactic cues are the homeostatic chemokines Ccl19, Ccl21, and Cxcl13, each of which is primarily produced by subsets of LN FRCs. 51 – 54 Previous studies characterizing changes in the tdLN have highlighted a transcriptional downregulation of these cues within 3 days of tumor implantation, and subsequent loss of normal immune cell compartmentalization. Such perturbations would seemingly be detrimental to the initiation of adaptive immune responses, which depends on precise cellular positioning. Indeed, genetic mouse models of homeostatic chemokine disruption exhibit impaired immunity. However, it is notable that all forms of inflammation and antigenic challenge, not just exposure to tumor-derived factors, can trigger transient downregulation of these factors. 55 – 61 As such, the immunological consequences of chemokine downregulation following tumor inoculation remain unclear. Within our dataset, however, we did not observe significant transcriptional alterations to Ccl19, Ccl21, or Cxcl13 amongst any stromal subset (data not shown). This may be reflective of the later timepoint at which we collected our data, and the transient nature of these fluctuations. While we do not observe dysregulation of these FRC-associated chemokines, re-clustering and differential analysis of the fLEC population did in fact reveal engagement of pathways related to leukocyte migration ( Figure 2E, F ). In particular, we observed dramatic increases in the inflammation-associated chemokines Cxcl9, Cxcl10 and Ccl20 ( Figure 2F ). These changes were exclusively depicted within the fLEC population, suggesting specific chemoattraction of cell types expressing the corresponding cognate receptors to the SCS. Interestingly, the Ccl20/Ccr6 axis has been well explored in the context of metastatic colorectal cancer, wherein CCr6-expressing cancer cells are recruited to the liver by Ccl20-expressing periportal stromal cells. 62 Expression of Ccl20 by floor LECs in the tdLN may be similarly utilized by migrating cancer cells in lymphatics. Critically, Ccl20 is absent on neighboring ceiling LECs, allowing the formation of a differential gradient of chemokine across the LN SCS. Analogous chemotactic gradients of Ccl19 and Ccl21 across the SCS have been experimentally demonstrated as critical for the recruitment of migrating dendritic cells into the LN. 63 By contrast, Cxcl9 and Cxcl10 are interferon induced chemokines that are most well described in the recruitment and activation of antigen experienced, Cxcr3-expressing T and NK cells. 64 , 65 Expression of these chemokines is therefore consistent with the broader interferon response signature of exhibited by fLECs of the tdLN ( Figure 2E ). In the context of cancer, these chemokines are notable for their role in recruitment of tumor-infiltrating lymphocytes and are thought to be important for driving immunologically “hot” tumor phenotypes. 66 Expression of Cxcl9 and Cxcl10 within the LN is additionally known to support positioning of memory T cells to the interfollicular region and is critically important for antiviral immune responses. 67 , 68 Cumulatively, these observations may implicate expression of Cxcl9/Cxcl10 by fLECs as a beneficial means to direct antigen responsive immune cells to site of future metastatic engraftment. On the other hand, Cxcr3 may also be expressed by migrating cancer cells, and the Cxcl9/Cxcl10/Cxcr3 signaling axis has in fact been implicated in the growth and metastasis of tumors to various organs, including the LN. 69 – 72 Thus, it remains unclear whether increased expression of these chemokines in the premetastatic LN favor metastasis engraftment or anti-tumor immunity. Stromal expression ofIL33 is increased in the pre-metastatic niche While MRCs and fLECs exhibited unique subset-specific responses in the tdLN, we also noted a handful of upregulated transcriptional elements common across both populations ( Figure 2G ). Importantly, upregulation of each was observed only in the draining LN of mice bearing live tumors but did not occur in the LNs of mice inoculated with fixed tumor cells ( Figure 2G ). As such, we believe these genes to be uniquely induced in response to tumor-derived signals. Exposure to factors produced by an upstream tumor may elicit responses that skew immune education, and thus in this context, the striking increase in IL33 – a cytokine with numerous immunological effects that are both context and cell-type specific - was particularly intriguing. 73 Notably, IL33 is a known driver of ST2+ T-reg expansion and may have an important role in the induction of Treg-mediated tolerance. 74 – 77 In the context of cancer metastasis, ST2-expressing Tregs have been shown to support tumor development, and in vivo inhibition of IL-33 signaling has been shown to disrupt formation of metastases in mouse models. 74 Additionally, elevated IL33 expression has been described in fibroblasts of lung metastases, wherein signaling is thought to contribute to a supportive niche through skewing immunity towards a Th2 response. 78 Tumor-induced IL33 expression across stromal subsets in the LN SCS may therefore have important implications for suppression of anti-tumor responses. Along these lines, recent reports have indeed suggested that metastatic spread to the LN can elicit immunological tolerance through the preferential induction of antigen-specific Tregs. 79 The functional consequence of this redirected immune response is thus believed to be favorable for subsequent metastatic outgrowth in distant organs. By contrast, it is also notable that, under infectious conditions, IL33 secretion by LN fibroblasts has been shown to be a stress response program critical for CD8 T cell immunity. 80 It is thus possible that IL33 upregulation in tdLNs may alternatively engage both anti-tumor immunity or immune suppressive elements, the balance of which may be a critical determinant of tumor progression and metastatic spread. Interestingly, a recent report has also implicated IL-33 signaling as a driver of fibroblast-to-CAF differentiation in oral squamous cell carcinoma. 81 In this context, IL-33 was found to be upregulated and stabilized by signaling from a novel lncRNA (termed Lnc-CAF), which resulted in acquisition of an activated CAF phenotype and subsequently supported tumor growth. Critically, Lnc-CAF was found to be distributed via tumor cell-derived exosomes, offering a plausible means by which this pathway might mediate preconditioning of LN stroma. Cumulatively, these studies suggest that IL-33 may function not only as a modulator of immunity in the metastatic microenvironment, but as a driving factor in the stromal acquisition of CAF-like features and niche formation. Metabolic reprogramming in LN stroma The upregulation of LDHA and PKM transcript was likewise common across both MRCs and fLECs ( Figure 2G ). This is particularly noteworthy within the context of niche formation and metastasis, as both these genes encode metabolic enzymes associated with the switch to aerobic glycolysis and have long been associated with metabolic adaptation in cancer cells. 82 , 83 Preferential utilization of aerobic glycolysis, even in the presence of sufficient oxygen, is termed the Warburg Effect, and is characteristic of cancer cell metabolism. 84 Despite several decades of research, a full functional account of this metabolic preference remains incompletely understood. However, disruption of this process by targeting of LDHA drives beneficial anti-tumor effects. 85 – 88 Notably, Riedel et al. recently demonstrated that inhibition of LDHA could reverse tumor-directed FRC activation phenotypes, suggesting a putative metabolic switch in stroma that may be similar to that of cancer cells. 24 The authors proposed a model wherein elevated levels of lactate produced in the primary tumor drains to the sentinel lymph node, modulating FRC function and conditioning the pre-metastatic niche. Supportive of this hypothesis, they observed higher expression of LDHA near lymphatic vessels of the primary tumor, and elevated levels of lactate in the draining LN. By contrast, our data suggest that LDHA is in fact intrinsically upregulated in both MRCs and fLECs as well, potentially further contributing to the regional build-up of lactate. Whether this response is secondary to the drainage of lactate from the primary tumor, or rather a response to other yet unidentified signals, remains unknown. However, a recent demonstration that CAFs from the primary tumor can in fact impart Warburg-like metabolism via exosomal transfer of metabolites offers an intriguing alternative whereby CAF-derived exosomes passing through lymphatic drainage may likewise contribute to induction of metabolic preconditioning at the site of future LN metastasis. 89 Nevertheless, both the means by which LDHA/PKM are upregulated and the metabolic and functional consequences of these alterations bear further exploration. Concluding remarks Metastatic progression is the greatest cause of mortality across most cancer indications, and thus developing new means of clinical intervention in this process is clearly of paramount importance. We believe the findings discussed herein provide an important resource for understanding the early impact of cancer-associated signals on metastatic niche formation. Additionally, we provide comparative analysis against LN stroma following antigenic challenge with fixed cancer cells, allowing us to discern changes that relate to factors actively produced by tumors from generalized inflammatory response programs. Transcriptomic changes discussed herein thus represent tumor-specific responses that may prove to be attractive targets for preventative or therapeutic intervention. However, further experimental exploration will be required to establish causal relationships between these transcriptional changes and metastatic success rate. Additionally, the specific identity of secreted factors driving LN preconditioning are not explored in this study - though paired analyses of primary tumors and tdLN might prove informative in this regard. Ultimately, we suggest that continued exploration along these lines may critically enable the identification of novel pressure points against which transformative therapeutic or preventative intervention may be directed. Accession numbers Gene Expression Omnibus at NCBI: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [Mus musculus (house mouse)]. Accession number GSE248905; http://identifiers.org/geo: GSE248905 Data availability Underlying data Open Science Framework: Underlying data for ‘Tumor-driven stromal reprogramming in the pre-metastatic lymph node’, https://www.doi.org/10.17605/OSF.IO/WDV3H . 27 This project contains the following underlying data: • Data File: Tumor dLN_A1_A01.fcs • Data File: Tumor dLN_A2_A02.fcs • Data File: Tumor dLN_A3_A03.fcs • Data File: Tumor dLN_A4_A04.fcs • Data File: Tumor dLN_A5_A05.fcs • Data File: Tumor NdLN_B1_B01.fcs • Data File: Tumor NdLN_B2_B02.fcs • Data File: Tumor NdLN_B3_B03.fcs • Data File: Tumor NdLN_B4_B04.fcs • Data File: Tumor NdLN_B5_B05.fcs • Data File: Killed dLN_C1_C01.fcs • Data File: Killed dLN_C2_C02.fcs • Data File: Killed dLN_C3_C03.fcs • Data File: Killed dLN_C4_C04.fcs • Data File: Killed dLN_C5_C05.fcs • Data File: Killed NdLN_D1_D01.fcs • Data File: Killed NdLN_D2_D02.fcs • Data File: Killed NdLN_D3_D03.fcs • Data File: Killed NdLN_D4_D04.fcs • Data File: Killed NdLN_D5_D05.fcs • Mouse FCS data key.xlsArrive Author Checklist.pdf Reporting guidelines Open Science Framework: ARRIVE checklist for ‘Tumor-driven stromal reprogramming in the pre-metastatic lymph node’, https://www.doi.org/10.17605/OSF.IO/WDV3H . 27 Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 27 Mar 2024 ADD YOUR COMMENT Comment Author details Author details 1 Oncology Innovative Targets and Technologies, Novartis, Cambridge, MA, 02139, USA 2 Oncology Translational Research, Novartis, Cambridge, MA, 02139, USA Michelle Piquet Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing David A Ruddy Roles: Conceptualization, Resources, Supervision, Writing – Review & Editing Viviana Cremasco Roles: Conceptualization, Resources, Supervision, Writing – Review & Editing Jonathan Chang Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests All authors are employees of Novartis. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 07 Oct 2025, 13:223 https://doi.org/10.12688/f1000research.145171.3 version 2 Revised Published: 10 Apr 2025, 13:223 https://doi.org/10.12688/f1000research.145171.2 version 1 Published: 27 Mar 2024, 13:223 https://doi.org/10.12688/f1000research.145171.1 Copyright © 2024 Piquet M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Piquet M, Ruddy DA, Cremasco V and Chang J. Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.12688/f1000research.145171.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 27 Mar 2024 Views 0 Cite How to cite this report: McGinnis C. Reviewer Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.159080.r296538 ) The direct URL for this report is: https://f1000research.com/articles/13-223/v1#referee-response-296538 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 14 Sep 2024 Christopher McGinnis , Department of Pathology, Stanford University, Gladstone-UCSF Institute of Genomic Immunology, Parker Institute for Cancer Immunotherapy, San Francisco, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.159080.r296538 In this study by Piquet et al, the Authors use single-cell RNA-sequencing (scRNA-seq) to compare lymph node (LN) stromal cell transcriptional profiles in mice bearing live and fixed tumors to disentangle changes in LN stromal cells related to pro-metastatic remodeling ... Continue reading READ ALL In this study by Piquet et al, the Authors use single-cell RNA-sequencing (scRNA-seq) to compare lymph node (LN) stromal cell transcriptional profiles in mice bearing live and fixed tumors to disentangle changes in LN stromal cells related to pro-metastatic remodeling mechanisms from changes linked to antigenic challenge. The authors make the following claims: Development of the LN pre-metastatic niche involves changes in the proportions and gene expression profiles of specific subtypes of fibroblasts and endothelial cells comprising the subcapsular sinus, in addition to other LN cell populations (e.g., b-zone reticular cells and perivascular cells). MRCs in the live tdLN are induced into a desmoplastic CAF-like state associated with upregulation of ECM components and remodeling genes such as fibronectin and Mmp2/9. Authors speculate that tumor-reprogrammed MRCs contribute to altered regulation of LN conduit access. fLECs increase expression of pro-inflammatory cytokines (e.g., Ccl20 and Cxcl9/10) in tdLNs, raising the question of whether TILs/NK cells or Cxcr3+ metastatic tumor cells (or both) are preferentially recruited to the subcapsular sinus. MRCs and fLECs enact a coordinate gene expression program in the live tdLN associated with increased Il33 and aerobic glycolysis genes, providing new insights into stromal cell signaling and metabolism mechanisms in the LN metastatic niche. Major Comments Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The MC38 tumor model is not thought to robustly metastasize (e.g., only ~50% of orthotopically-transplanted tumors form LN metastases; Greenlee & King, 2022), especially after subcutaneous injection. Thus, it is unclear whether the signatures described in the paper truly represent the pre-metastatic niche LN. Without adequately addressing this concern, the authors need to recontextualize their observations to focus on the effects of primary tumor-mediated reprogramming on the tdLN rather than the pre-metastatic niche. “Within the resultant dataset, we identified 7 distinct clusters of fibroblastic cells and 4 clusters of lymphatic endothelial cells, each of which was annotated based on well-established identity markers” – Authors should include either reference to which markers were used or, better yet, a heatmap or dotplot showing expression levels for marker genes in each annotated cell type. “Perivascular cells (PRCs), though not localized to the SCS, likewise exhibited significant transcriptomic changes, which may reflect support of vascular expansion” – While entirely possible, it is unclear how the observed increase in the numbers of DEGs suggests support of vascular expansion. Authors should analyze more deeply the genes that are differentially expressed (e.g., GSEA, targeted analysis of known proangiogenic factors expressed by PRCs, etc.) before making this claim. The Authors make many claims throughout the manuscript about proliferative expansion of certain fibroblast and endothelial cell subsets in tumor-draining lymph nodes, but do not show any direct evidence of this increased proliferation. Since annotating proliferative cells via Mki67 and Hells expression in scRNA-seq data is possible, I recommend the Authors compare the proportions of proliferative subsets in all experimental groups to test/strengthen these claims. The Authors do not describe how they handled the removal of doublets during scRNA-seq analysis. The presence of doublets could significantly confound data interpretation – particularly in instances where the underlying cell type distributions between samples are known to differ, as in this case – and, thus, needs to be addressed to ensure the observations are not artefactual in nature. In the Methods section, the Authors state that “The present study used tissues collected from naïve mice (n=4), mice bearing live MC38 tumors (n=3), or fixed MC38 tumor cells (n=3)” and then “No animals were excluded from analysis.” However, the described scRNA-seq analyses do not incorporate insights gained from the naïve mouse samples. Comparing naïve and fixed tumor samples could provide key insight for distinguishing the effects of live tumors on the tdLN. Moreover, including comparisons between naïve and fixed tumor samples would be critical for pinpointing the observed effects that are specifically due to antigenic challenge. Alternatively, if naïve samples were not used, the Methods section should be edited to clarify this point. In Fig. 2G, the authors show genes that are specifically enriched in tdLN MRCs and fLECs. How were these genes identified? Is Il33 expression between tdLNs isolated from mice harboring live and fixed tumors statistically-significantly different? The up-regulation between tdLN and ndLN is clear in both live and fixed tumor settings, but tdLNs in fixed tumor samples also increase expression of Il33 compared to ndLNs, suggesting that this observation may not be live tumor-specific. Minor Comments Authors state “In the pre-metastatic lymph node, expansion of both endothelium and FRCs has been documented” – true, but authors should cite appropriate literature references. How did authors verify that MC38 tumor cells were successfully fixed after 4% PFA exposure? Fig. 1A: Authors should clarify in the legend the reference point they used to calculate relative cell count. Imagining it was the average of the non-draining LN, but should be explicit to avoid confusion. Authors should also clarify how each cell type was identified using their flow cytometry panel. Fig. 1B and E: Authors do not ever define some of the acronyms used here (e.g., TRC, FDC) Page 7: “In this context, Increased conduit thickness…” – ‘Increased’ should not be capitalized. Fig. 2B/E/F – authors should be explicit about what the volcano plot colors scheme to ensure clarity of interpretation. Also, are the p-values presented raw or adjusted? As far as I can tell, the Authors use increased expression of fibronectin by MRCs in the tdLN to support the claim that they are induced into a desmoplastic CAF-like state. This may indeed be the case, but more thorough analyses/discussions are needed to sufficiently support this claim. I would suggest leveraging publicly-available scRNA-seq data of desmoplastic CAFs (if such data exists) to assess similarly in transcriptional signatures. Alternatively, the language can be edited to lessen the claimed connection. “Indeed, genetic mouse models of homeostatic chemokine disruption exhibit impaired immunity.” Need reference. Should correctly note gene/protein identifiers (e.g., italics for gene names, proteins capitalized, etc.). The 30% mitochondrial gene expression threshold is quite high. Authors should interrogate whether any high pMito clusters were retained in analyses that could confound interpretation. On balance, I believe that the presented work represents a useful advance for the field, and I recommend its indexing, assuming that the concerns addressed above are satisfactorily addressed. Moreover, I would also like to specifically note that the manuscript is extremely well-written. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Cancer immunology, metastasis biology, single-cell genomics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT McGinnis C. Reviewer Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.159080.r296538 ) The direct URL for this report is: https://f1000research.com/articles/13-223/v1#referee-response-296538 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Apr 2025 Jonathan Chang , Oncology Translational Research, Novartis, Cambridge, 02139, USA 10 Apr 2025 Author Response (Major) Reviewer Comment 1: Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The ... Continue reading (Major) Reviewer Comment 1: Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The MC38 tumor model is not thought to robustly metastasize (e.g., only ~50% of orthotopically-transplanted tumors form LN metastases; Greenlee & King, 2022), especially after subcutaneous injection. Thus, it is unclear whether the signatures described in the paper truly represent the pre-metastatic niche LN. Without adequately addressing this concern, the authors need to recontextualize their observations to focus on the effects of primary tumor-mediated reprogramming on the tdLN rather than the pre-metastatic niche. Response: The reviewer raises a salient point in that subcutaneously implanted tumor models rarely in fact metastasize (at least within the timeframe at which the primary tumor remains within ethical growth limits). Orthotopically implanted tumors more readily metastasize, perhaps due to the native regional tissue environment of implantation. However, such models are often technically challenging and highly variable in terms of tumor growth kinetics. We opted to use subcutaneous MC38 tumors to minimize technical and experimental variance which might otherwise obscure the identification of transcriptional signatures influenced by tumor-derived factors. We feel the nature of multiple stromal alterations highlighted in this paper align well with previously proposed microenvironmental dependencies for tumor growth and metastasis highlighted within the broader literature, and it is for this reason that we contextualize these changes as “pre-metastatic”. However, we fully recognize that data herein do not constitute experimental proof that any or all of these transcriptional changes are in fact necessary for metastasis but rather offer these results as a resource for more definitive studies to follow. We agree that such experimental proof would necessitate the use of models for which lymph node metastasis can be explicitly documented following experimental perturbation. Such an effort is currently beyond the scope of this limited study. However, we do acknowledge that this is a critically important limitation, and discussion of this point has been included in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 2: “Within the resultant dataset, we identified 7 distinct clusters of fibroblastic cells and 4 clusters of lymphatic endothelial cells, each of which was annotated based on well-established identity markers” – Authors should include either reference to which markers were used or, better yet, a heatmap or dotplot showing expression levels for marker genes in each annotated cell type. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supplementary Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. (Major) Reviewer Comment 3: “Perivascular cells (PRCs), though not localized to the SCS, likewise exhibited significant transcriptomic changes, which may reflect support of vascular expansion” – While entirely possible, it is unclear how the observed increase in the numbers of DEGs suggests support of vascular expansion. Authors should analyze more deeply the genes that are differentially expressed (e.g., GSEA, targeted analysis of known proangiogenic factors expressed by PRCs, etc.) before making this claim. Response: This was a speculative comment due to the association of PRCs with LN vasculature, and the observed increase in BEC proliferation (fig1A). We have tempered this statement in the revised manuscript and included references to studies outlining potential function of PRCs. Importantly, we have also revised our discussion of PRC, in which we describe them as “not localized to the SCS”. Further review of the findings by Rodda et al. 2018 in which they describe localization of CD34+ stromal cells not only in the perivascular compartment, but also within the LN capsule. Transcriptional features identifying our PRC subset align with these CD34 SCs described by Rodda et al. and Sitnik et al. 2016. We have additionally included the full DEG list of PRCs in tdLN vs ndLN in the newly added Supplementary table 3. (Major) Reviewer Comment 4: The Authors make many claims throughout the manuscript about proliferative expansion of certain fibroblast and endothelial cell subsets in tumor-draining lymph nodes, but do not show any direct evidence of this increased proliferation. Since annotating proliferative cells via Mki67 and Hells expression in scRNA-seq data is possible, I recommend the Authors compare the proportions of proliferative subsets in all experimental groups to test/strengthen these claims. Response: Increased cell counts of each major stromal cell population (fibroblast, LEC, and BEC) within the dLN vs ndLN (seen in Fig1A) provides us some direct evidence of proliferative expansion of each of these broader populations. Within each stromal cell type, we then identify changes in the representation of subsets within in the scRNAseq dataset. For instance, “Fibroblasts” are presumed to have proliferated due to the >2 fold increase in cell numbers relative to matched non-draining LN (fig1A). Then, within all fibroblasts, there is then a higher proportion of MRC, PRC, and BRC in the dLN relative to the ndLN (Fig 1C), a differential which is not present in the control conditions of mice inoculated with fixed tumor cells (Fig 1D). This differential in subset representation is also visually evident in the newly added Supplementary figure 2. Unfortunately we do not have the capacity to measure absolute cell counts of individual subsets, and thus proliferative expansion is inferred from the combination of these above observations. Additionally, ki67 + cells are indeed present in our dataset, but constitute a small proportion of every cell population present. In our experience, ki67 has rarely been a reliable or quantitative measure of population expansion in tissues. (Major) Reviewer Comment 5: The Authors do not describe how they handled the removal of doublets during scRNA-seq analysis. The presence of doublets could significantly confound data interpretation – particularly in instances where the underlying cell type distributions between samples are known to differ, as in this case – and, thus, needs to be addressed to ensure the observations are not artefactual in nature. Response: We carefully assessed the quality control metrics of the dataset, including the number of RNA molecules (UMIs) and the number of genes detected per cell. All values fell within the expected range for single cells, as defined in Seurat’s documentation, and we did not observe any anomalies indicative of doublets. Based on these observations, we do not suspect the presence of doublets in our dataset at a level that would confound the interpretation of our results. This information has been added to the methods description in the revised manuscript. (Major) Reviewer Comment 6: In the Methods section, the Authors state that “The present study used tissues collected from naïve mice (n=4), mice bearing live MC38 tumors (n=3), or fixed MC38 tumor cells (n=3)” and then “No animals were excluded from analysis.” However, the described scRNA-seq analyses do not incorporate insights gained from the naïve mouse samples. Comparing naïve and fixed tumor samples could provide key insight for distinguishing the effects of live tumors on the tdLN. Moreover, including comparisons between naïve and fixed tumor samples would be critical for pinpointing the observed effects that are specifically due to antigenic challenge. Alternatively, if naïve samples were not used, the Methods section should be edited to clarify this point. Response: We appreciate the careful review and thoughtful suggestion. However naïve samples were not included in this assessment. The methods section was corrected to reflect the proper cohort used in this analysis. (Major) Reviewer Comment 7: In Fig. 2G, the authors show genes that are specifically enriched in tdLN MRCs and fLECs. How were these genes identified? Is Il33 expression between tdLNs isolated from mice harboring live and fixed tumors statistically-significantly different? The up-regulation between tdLN and ndLN is clear in both live and fixed tumor settings, but tdLNs in fixed tumor samples also increase expression of Il33 compared to ndLNs, suggesting that this observation may not be live tumor-specific. Response: The genes in Fig. 2G were identified by performing DEG analysis across dLN and ndLN of the live and fixed tumor condition. Genes which were commonly upregulated in both the MRC and fLEC of specifically the live tdLN subset were graphed in the bubble plot. The mean expression of IL33 in the dLN of mice receiving fixed tumor cells is overall slightly elevated over the corresponding ndLN, but did not reach statistical significance. A statistically significant change in expression was only seen in the dLN vs ndLN of live tumor-bearing mice. (Minor) Reviewer Comment 1: Authors state “In the pre-metastatic lymph node, expansion of both endothelium and FRCs has been documented” – true, but authors should cite appropriate literature references. Response: Citation has been added. (Minor) Reviewer Comment 2: How did authors verify that MC38 tumor cells were successfully fixed after 4% PFA exposure? Response: No tumor growth was observed in mice receiving fixed tumor cells at the experimental endpoint. (Minor) Reviewer Comment 3: Fig. 1A: Authors should clarify in the legend the reference point they used to calculate relative cell count. Imagining it was the average of the non-draining LN, but should be explicit to avoid confusion. Authors should also clarify how each cell type was identified using their flow cytometry panel. Response: Cell counts are presented as relative to the average of corresponding non-draining lymph nodes. Cell populations were identified by flow cytometry with the following markers: Fibroblastic Reticular (CD45-CD31-PDPN+), Lymphatic Endothelial (CD45-, CD31+, PDPN+), and Blood Endothelial (CD45-, CD31+, PDPN-). This information has been added to the figure legend in the revised manuscript. (Minor) Reviewer Comment 4: Fig. 1B and E: Authors do not ever define some of the acronyms used here (e.g., TRC, FDC) Response: The following definitions have been added to the text: Fibroblastic populations include T-zone Reticular Cells (TRC), B zone Reticular cells (BRC), Marginal Zone Reticular Cells (MRC), Follicular Dendritic Cells (FDC), Medulary Reticular cells (MedRC), Perivascular Reticular cells (PRC), and activated stromal cells (Act SC). Endothelial cell subsets include ceiling LECs (cLEC), floor LECs (fLEC), Medulary LECs (MedLEC), and Cortical LECs (CorLEC). (Minor) Reviewer Comment 5: Page 7: “In this context, Increased conduit thickness…” – ‘Increased’ should not be capitalized. Response: Typo has been corrected (Minor) Reviewer Comment 6: Fig. 2B/E/F – authors should be explicit about what the volcano plot colors scheme to ensure clarity of interpretation. Also, are the p-values presented raw or adjusted? Response: The cutoffs for highlighted genes include a log2 fold change of 0.5 or above (horizontal dashed lines) and adjusted p-value above 0.001 (indicated by the vertical dashed lines). This description is included in the figure legend of the revised manuscript. (Minor) Reviewer Comment 7: As far as I can tell, the Authors use increased expression of fibronectin by MRCs in the tdLN to support the claim that they are induced into a desmoplastic CAF-like state. This may indeed be the case, but more thorough analyses/discussions are needed to sufficiently support this claim. I would suggest leveraging publicly-available scRNA-seq data of desmoplastic CAFs (if such data exists) to assess similarly in transcriptional signatures. Alternatively, the language can be edited to lessen the claimed connection. Response: While we do single-out Fn1 in the text, we note that this is one of several genes differentially expressed in MRCs of the tumor dLN that relate to matrix deposition or tissue remodeling. Many others are additionally labeled in figure 2b. Additionally, both ECM organization and wound-healing are prominent in the pathway analysis as shown in Fig2a. For additional clarity, we have also included the full DEG list of MRC, BRC, fLEC, and PRC populations in the tdLN vs ndLN in Supplementary table 3 of the revised manuscript. We feel comfortable drawing the comparison to desmoplastic CAFs given that these indicators of altered matrix/remodeling arise within the context of the tumor-draining LN. However, to soften the claim, we have changed the wording of the section title from “reminiscent of desmoplastic CAFs” to instead read “reminiscent of the desmoplastic nature of CAFs”, as we acknowledge that ECM production/tissue remodeling are only one facet of CAF function. We do not want to make the claim that the transcriptional changes we see indicate that MRCs have become CAFs, but rather that they have taken on a key function typically associated with CAFs. (Minor) Reviewer Comment 8: “Indeed, genetic mouse models of homeostatic chemokine disruption exhibit impaired immunity.” Need reference. Response: Relevant references have been added. (Minor) Reviewer Comment 9: Should correctly note gene/protein identifiers (e.g., italics for gene names, proteins capitalized, etc.). Response: We have reformatted gene/protein names to match convention. (Minor) Reviewer Comment 10: The 30% mitochondrial gene expression threshold is quite high. Authors should interrogate whether any high pMito clusters were retained in analyses that could confound interpretation. Response: We carefully examined the distribution of mitochondrial gene expression across cells and found no evidence of high mitochondrial clusters that would indicate stressed or damaged cells. Additionally, downstream analyses, such as clustering and differential expression, did not reveal any confounding effects that could be attributed to mitochondrial gene expression. Based on these observations, we are confident that the retained cells are biologically relevant and do not introduce bias into our results. (Major) Reviewer Comment 1: Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The MC38 tumor model is not thought to robustly metastasize (e.g., only ~50% of orthotopically-transplanted tumors form LN metastases; Greenlee & King, 2022), especially after subcutaneous injection. Thus, it is unclear whether the signatures described in the paper truly represent the pre-metastatic niche LN. Without adequately addressing this concern, the authors need to recontextualize their observations to focus on the effects of primary tumor-mediated reprogramming on the tdLN rather than the pre-metastatic niche. Response: The reviewer raises a salient point in that subcutaneously implanted tumor models rarely in fact metastasize (at least within the timeframe at which the primary tumor remains within ethical growth limits). Orthotopically implanted tumors more readily metastasize, perhaps due to the native regional tissue environment of implantation. However, such models are often technically challenging and highly variable in terms of tumor growth kinetics. We opted to use subcutaneous MC38 tumors to minimize technical and experimental variance which might otherwise obscure the identification of transcriptional signatures influenced by tumor-derived factors. We feel the nature of multiple stromal alterations highlighted in this paper align well with previously proposed microenvironmental dependencies for tumor growth and metastasis highlighted within the broader literature, and it is for this reason that we contextualize these changes as “pre-metastatic”. However, we fully recognize that data herein do not constitute experimental proof that any or all of these transcriptional changes are in fact necessary for metastasis but rather offer these results as a resource for more definitive studies to follow. We agree that such experimental proof would necessitate the use of models for which lymph node metastasis can be explicitly documented following experimental perturbation. Such an effort is currently beyond the scope of this limited study. However, we do acknowledge that this is a critically important limitation, and discussion of this point has been included in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 2: “Within the resultant dataset, we identified 7 distinct clusters of fibroblastic cells and 4 clusters of lymphatic endothelial cells, each of which was annotated based on well-established identity markers” – Authors should include either reference to which markers were used or, better yet, a heatmap or dotplot showing expression levels for marker genes in each annotated cell type. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supplementary Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. (Major) Reviewer Comment 3: “Perivascular cells (PRCs), though not localized to the SCS, likewise exhibited significant transcriptomic changes, which may reflect support of vascular expansion” – While entirely possible, it is unclear how the observed increase in the numbers of DEGs suggests support of vascular expansion. Authors should analyze more deeply the genes that are differentially expressed (e.g., GSEA, targeted analysis of known proangiogenic factors expressed by PRCs, etc.) before making this claim. Response: This was a speculative comment due to the association of PRCs with LN vasculature, and the observed increase in BEC proliferation (fig1A). We have tempered this statement in the revised manuscript and included references to studies outlining potential function of PRCs. Importantly, we have also revised our discussion of PRC, in which we describe them as “not localized to the SCS”. Further review of the findings by Rodda et al. 2018 in which they describe localization of CD34+ stromal cells not only in the perivascular compartment, but also within the LN capsule. Transcriptional features identifying our PRC subset align with these CD34 SCs described by Rodda et al. and Sitnik et al. 2016. We have additionally included the full DEG list of PRCs in tdLN vs ndLN in the newly added Supplementary table 3. (Major) Reviewer Comment 4: The Authors make many claims throughout the manuscript about proliferative expansion of certain fibroblast and endothelial cell subsets in tumor-draining lymph nodes, but do not show any direct evidence of this increased proliferation. Since annotating proliferative cells via Mki67 and Hells expression in scRNA-seq data is possible, I recommend the Authors compare the proportions of proliferative subsets in all experimental groups to test/strengthen these claims. Response: Increased cell counts of each major stromal cell population (fibroblast, LEC, and BEC) within the dLN vs ndLN (seen in Fig1A) provides us some direct evidence of proliferative expansion of each of these broader populations. Within each stromal cell type, we then identify changes in the representation of subsets within in the scRNAseq dataset. For instance, “Fibroblasts” are presumed to have proliferated due to the >2 fold increase in cell numbers relative to matched non-draining LN (fig1A). Then, within all fibroblasts, there is then a higher proportion of MRC, PRC, and BRC in the dLN relative to the ndLN (Fig 1C), a differential which is not present in the control conditions of mice inoculated with fixed tumor cells (Fig 1D). This differential in subset representation is also visually evident in the newly added Supplementary figure 2. Unfortunately we do not have the capacity to measure absolute cell counts of individual subsets, and thus proliferative expansion is inferred from the combination of these above observations. Additionally, ki67 + cells are indeed present in our dataset, but constitute a small proportion of every cell population present. In our experience, ki67 has rarely been a reliable or quantitative measure of population expansion in tissues. (Major) Reviewer Comment 5: The Authors do not describe how they handled the removal of doublets during scRNA-seq analysis. The presence of doublets could significantly confound data interpretation – particularly in instances where the underlying cell type distributions between samples are known to differ, as in this case – and, thus, needs to be addressed to ensure the observations are not artefactual in nature. Response: We carefully assessed the quality control metrics of the dataset, including the number of RNA molecules (UMIs) and the number of genes detected per cell. All values fell within the expected range for single cells, as defined in Seurat’s documentation, and we did not observe any anomalies indicative of doublets. Based on these observations, we do not suspect the presence of doublets in our dataset at a level that would confound the interpretation of our results. This information has been added to the methods description in the revised manuscript. (Major) Reviewer Comment 6: In the Methods section, the Authors state that “The present study used tissues collected from naïve mice (n=4), mice bearing live MC38 tumors (n=3), or fixed MC38 tumor cells (n=3)” and then “No animals were excluded from analysis.” However, the described scRNA-seq analyses do not incorporate insights gained from the naïve mouse samples. Comparing naïve and fixed tumor samples could provide key insight for distinguishing the effects of live tumors on the tdLN. Moreover, including comparisons between naïve and fixed tumor samples would be critical for pinpointing the observed effects that are specifically due to antigenic challenge. Alternatively, if naïve samples were not used, the Methods section should be edited to clarify this point. Response: We appreciate the careful review and thoughtful suggestion. However naïve samples were not included in this assessment. The methods section was corrected to reflect the proper cohort used in this analysis. (Major) Reviewer Comment 7: In Fig. 2G, the authors show genes that are specifically enriched in tdLN MRCs and fLECs. How were these genes identified? Is Il33 expression between tdLNs isolated from mice harboring live and fixed tumors statistically-significantly different? The up-regulation between tdLN and ndLN is clear in both live and fixed tumor settings, but tdLNs in fixed tumor samples also increase expression of Il33 compared to ndLNs, suggesting that this observation may not be live tumor-specific. Response: The genes in Fig. 2G were identified by performing DEG analysis across dLN and ndLN of the live and fixed tumor condition. Genes which were commonly upregulated in both the MRC and fLEC of specifically the live tdLN subset were graphed in the bubble plot. The mean expression of IL33 in the dLN of mice receiving fixed tumor cells is overall slightly elevated over the corresponding ndLN, but did not reach statistical significance. A statistically significant change in expression was only seen in the dLN vs ndLN of live tumor-bearing mice. (Minor) Reviewer Comment 1: Authors state “In the pre-metastatic lymph node, expansion of both endothelium and FRCs has been documented” – true, but authors should cite appropriate literature references. Response: Citation has been added. (Minor) Reviewer Comment 2: How did authors verify that MC38 tumor cells were successfully fixed after 4% PFA exposure? Response: No tumor growth was observed in mice receiving fixed tumor cells at the experimental endpoint. (Minor) Reviewer Comment 3: Fig. 1A: Authors should clarify in the legend the reference point they used to calculate relative cell count. Imagining it was the average of the non-draining LN, but should be explicit to avoid confusion. Authors should also clarify how each cell type was identified using their flow cytometry panel. Response: Cell counts are presented as relative to the average of corresponding non-draining lymph nodes. Cell populations were identified by flow cytometry with the following markers: Fibroblastic Reticular (CD45-CD31-PDPN+), Lymphatic Endothelial (CD45-, CD31+, PDPN+), and Blood Endothelial (CD45-, CD31+, PDPN-). This information has been added to the figure legend in the revised manuscript. (Minor) Reviewer Comment 4: Fig. 1B and E: Authors do not ever define some of the acronyms used here (e.g., TRC, FDC) Response: The following definitions have been added to the text: Fibroblastic populations include T-zone Reticular Cells (TRC), B zone Reticular cells (BRC), Marginal Zone Reticular Cells (MRC), Follicular Dendritic Cells (FDC), Medulary Reticular cells (MedRC), Perivascular Reticular cells (PRC), and activated stromal cells (Act SC). Endothelial cell subsets include ceiling LECs (cLEC), floor LECs (fLEC), Medulary LECs (MedLEC), and Cortical LECs (CorLEC). (Minor) Reviewer Comment 5: Page 7: “In this context, Increased conduit thickness…” – ‘Increased’ should not be capitalized. Response: Typo has been corrected (Minor) Reviewer Comment 6: Fig. 2B/E/F – authors should be explicit about what the volcano plot colors scheme to ensure clarity of interpretation. Also, are the p-values presented raw or adjusted? Response: The cutoffs for highlighted genes include a log2 fold change of 0.5 or above (horizontal dashed lines) and adjusted p-value above 0.001 (indicated by the vertical dashed lines). This description is included in the figure legend of the revised manuscript. (Minor) Reviewer Comment 7: As far as I can tell, the Authors use increased expression of fibronectin by MRCs in the tdLN to support the claim that they are induced into a desmoplastic CAF-like state. This may indeed be the case, but more thorough analyses/discussions are needed to sufficiently support this claim. I would suggest leveraging publicly-available scRNA-seq data of desmoplastic CAFs (if such data exists) to assess similarly in transcriptional signatures. Alternatively, the language can be edited to lessen the claimed connection. Response: While we do single-out Fn1 in the text, we note that this is one of several genes differentially expressed in MRCs of the tumor dLN that relate to matrix deposition or tissue remodeling. Many others are additionally labeled in figure 2b. Additionally, both ECM organization and wound-healing are prominent in the pathway analysis as shown in Fig2a. For additional clarity, we have also included the full DEG list of MRC, BRC, fLEC, and PRC populations in the tdLN vs ndLN in Supplementary table 3 of the revised manuscript. We feel comfortable drawing the comparison to desmoplastic CAFs given that these indicators of altered matrix/remodeling arise within the context of the tumor-draining LN. However, to soften the claim, we have changed the wording of the section title from “reminiscent of desmoplastic CAFs” to instead read “reminiscent of the desmoplastic nature of CAFs”, as we acknowledge that ECM production/tissue remodeling are only one facet of CAF function. We do not want to make the claim that the transcriptional changes we see indicate that MRCs have become CAFs, but rather that they have taken on a key function typically associated with CAFs. (Minor) Reviewer Comment 8: “Indeed, genetic mouse models of homeostatic chemokine disruption exhibit impaired immunity.” Need reference. Response: Relevant references have been added. (Minor) Reviewer Comment 9: Should correctly note gene/protein identifiers (e.g., italics for gene names, proteins capitalized, etc.). Response: We have reformatted gene/protein names to match convention. (Minor) Reviewer Comment 10: The 30% mitochondrial gene expression threshold is quite high. Authors should interrogate whether any high pMito clusters were retained in analyses that could confound interpretation. Response: We carefully examined the distribution of mitochondrial gene expression across cells and found no evidence of high mitochondrial clusters that would indicate stressed or damaged cells. Additionally, downstream analyses, such as clustering and differential expression, did not reveal any confounding effects that could be attributed to mitochondrial gene expression. Based on these observations, we are confident that the retained cells are biologically relevant and do not introduce bias into our results. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Apr 2025 Jonathan Chang , Oncology Translational Research, Novartis, Cambridge, 02139, USA 10 Apr 2025 Author Response (Major) Reviewer Comment 1: Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The ... Continue reading (Major) Reviewer Comment 1: Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The MC38 tumor model is not thought to robustly metastasize (e.g., only ~50% of orthotopically-transplanted tumors form LN metastases; Greenlee & King, 2022), especially after subcutaneous injection. Thus, it is unclear whether the signatures described in the paper truly represent the pre-metastatic niche LN. Without adequately addressing this concern, the authors need to recontextualize their observations to focus on the effects of primary tumor-mediated reprogramming on the tdLN rather than the pre-metastatic niche. Response: The reviewer raises a salient point in that subcutaneously implanted tumor models rarely in fact metastasize (at least within the timeframe at which the primary tumor remains within ethical growth limits). Orthotopically implanted tumors more readily metastasize, perhaps due to the native regional tissue environment of implantation. However, such models are often technically challenging and highly variable in terms of tumor growth kinetics. We opted to use subcutaneous MC38 tumors to minimize technical and experimental variance which might otherwise obscure the identification of transcriptional signatures influenced by tumor-derived factors. We feel the nature of multiple stromal alterations highlighted in this paper align well with previously proposed microenvironmental dependencies for tumor growth and metastasis highlighted within the broader literature, and it is for this reason that we contextualize these changes as “pre-metastatic”. However, we fully recognize that data herein do not constitute experimental proof that any or all of these transcriptional changes are in fact necessary for metastasis but rather offer these results as a resource for more definitive studies to follow. We agree that such experimental proof would necessitate the use of models for which lymph node metastasis can be explicitly documented following experimental perturbation. Such an effort is currently beyond the scope of this limited study. However, we do acknowledge that this is a critically important limitation, and discussion of this point has been included in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 2: “Within the resultant dataset, we identified 7 distinct clusters of fibroblastic cells and 4 clusters of lymphatic endothelial cells, each of which was annotated based on well-established identity markers” – Authors should include either reference to which markers were used or, better yet, a heatmap or dotplot showing expression levels for marker genes in each annotated cell type. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supplementary Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. (Major) Reviewer Comment 3: “Perivascular cells (PRCs), though not localized to the SCS, likewise exhibited significant transcriptomic changes, which may reflect support of vascular expansion” – While entirely possible, it is unclear how the observed increase in the numbers of DEGs suggests support of vascular expansion. Authors should analyze more deeply the genes that are differentially expressed (e.g., GSEA, targeted analysis of known proangiogenic factors expressed by PRCs, etc.) before making this claim. Response: This was a speculative comment due to the association of PRCs with LN vasculature, and the observed increase in BEC proliferation (fig1A). We have tempered this statement in the revised manuscript and included references to studies outlining potential function of PRCs. Importantly, we have also revised our discussion of PRC, in which we describe them as “not localized to the SCS”. Further review of the findings by Rodda et al. 2018 in which they describe localization of CD34+ stromal cells not only in the perivascular compartment, but also within the LN capsule. Transcriptional features identifying our PRC subset align with these CD34 SCs described by Rodda et al. and Sitnik et al. 2016. We have additionally included the full DEG list of PRCs in tdLN vs ndLN in the newly added Supplementary table 3. (Major) Reviewer Comment 4: The Authors make many claims throughout the manuscript about proliferative expansion of certain fibroblast and endothelial cell subsets in tumor-draining lymph nodes, but do not show any direct evidence of this increased proliferation. Since annotating proliferative cells via Mki67 and Hells expression in scRNA-seq data is possible, I recommend the Authors compare the proportions of proliferative subsets in all experimental groups to test/strengthen these claims. Response: Increased cell counts of each major stromal cell population (fibroblast, LEC, and BEC) within the dLN vs ndLN (seen in Fig1A) provides us some direct evidence of proliferative expansion of each of these broader populations. Within each stromal cell type, we then identify changes in the representation of subsets within in the scRNAseq dataset. For instance, “Fibroblasts” are presumed to have proliferated due to the >2 fold increase in cell numbers relative to matched non-draining LN (fig1A). Then, within all fibroblasts, there is then a higher proportion of MRC, PRC, and BRC in the dLN relative to the ndLN (Fig 1C), a differential which is not present in the control conditions of mice inoculated with fixed tumor cells (Fig 1D). This differential in subset representation is also visually evident in the newly added Supplementary figure 2. Unfortunately we do not have the capacity to measure absolute cell counts of individual subsets, and thus proliferative expansion is inferred from the combination of these above observations. Additionally, ki67 + cells are indeed present in our dataset, but constitute a small proportion of every cell population present. In our experience, ki67 has rarely been a reliable or quantitative measure of population expansion in tissues. (Major) Reviewer Comment 5: The Authors do not describe how they handled the removal of doublets during scRNA-seq analysis. The presence of doublets could significantly confound data interpretation – particularly in instances where the underlying cell type distributions between samples are known to differ, as in this case – and, thus, needs to be addressed to ensure the observations are not artefactual in nature. Response: We carefully assessed the quality control metrics of the dataset, including the number of RNA molecules (UMIs) and the number of genes detected per cell. All values fell within the expected range for single cells, as defined in Seurat’s documentation, and we did not observe any anomalies indicative of doublets. Based on these observations, we do not suspect the presence of doublets in our dataset at a level that would confound the interpretation of our results. This information has been added to the methods description in the revised manuscript. (Major) Reviewer Comment 6: In the Methods section, the Authors state that “The present study used tissues collected from naïve mice (n=4), mice bearing live MC38 tumors (n=3), or fixed MC38 tumor cells (n=3)” and then “No animals were excluded from analysis.” However, the described scRNA-seq analyses do not incorporate insights gained from the naïve mouse samples. Comparing naïve and fixed tumor samples could provide key insight for distinguishing the effects of live tumors on the tdLN. Moreover, including comparisons between naïve and fixed tumor samples would be critical for pinpointing the observed effects that are specifically due to antigenic challenge. Alternatively, if naïve samples were not used, the Methods section should be edited to clarify this point. Response: We appreciate the careful review and thoughtful suggestion. However naïve samples were not included in this assessment. The methods section was corrected to reflect the proper cohort used in this analysis. (Major) Reviewer Comment 7: In Fig. 2G, the authors show genes that are specifically enriched in tdLN MRCs and fLECs. How were these genes identified? Is Il33 expression between tdLNs isolated from mice harboring live and fixed tumors statistically-significantly different? The up-regulation between tdLN and ndLN is clear in both live and fixed tumor settings, but tdLNs in fixed tumor samples also increase expression of Il33 compared to ndLNs, suggesting that this observation may not be live tumor-specific. Response: The genes in Fig. 2G were identified by performing DEG analysis across dLN and ndLN of the live and fixed tumor condition. Genes which were commonly upregulated in both the MRC and fLEC of specifically the live tdLN subset were graphed in the bubble plot. The mean expression of IL33 in the dLN of mice receiving fixed tumor cells is overall slightly elevated over the corresponding ndLN, but did not reach statistical significance. A statistically significant change in expression was only seen in the dLN vs ndLN of live tumor-bearing mice. (Minor) Reviewer Comment 1: Authors state “In the pre-metastatic lymph node, expansion of both endothelium and FRCs has been documented” – true, but authors should cite appropriate literature references. Response: Citation has been added. (Minor) Reviewer Comment 2: How did authors verify that MC38 tumor cells were successfully fixed after 4% PFA exposure? Response: No tumor growth was observed in mice receiving fixed tumor cells at the experimental endpoint. (Minor) Reviewer Comment 3: Fig. 1A: Authors should clarify in the legend the reference point they used to calculate relative cell count. Imagining it was the average of the non-draining LN, but should be explicit to avoid confusion. Authors should also clarify how each cell type was identified using their flow cytometry panel. Response: Cell counts are presented as relative to the average of corresponding non-draining lymph nodes. Cell populations were identified by flow cytometry with the following markers: Fibroblastic Reticular (CD45-CD31-PDPN+), Lymphatic Endothelial (CD45-, CD31+, PDPN+), and Blood Endothelial (CD45-, CD31+, PDPN-). This information has been added to the figure legend in the revised manuscript. (Minor) Reviewer Comment 4: Fig. 1B and E: Authors do not ever define some of the acronyms used here (e.g., TRC, FDC) Response: The following definitions have been added to the text: Fibroblastic populations include T-zone Reticular Cells (TRC), B zone Reticular cells (BRC), Marginal Zone Reticular Cells (MRC), Follicular Dendritic Cells (FDC), Medulary Reticular cells (MedRC), Perivascular Reticular cells (PRC), and activated stromal cells (Act SC). Endothelial cell subsets include ceiling LECs (cLEC), floor LECs (fLEC), Medulary LECs (MedLEC), and Cortical LECs (CorLEC). (Minor) Reviewer Comment 5: Page 7: “In this context, Increased conduit thickness…” – ‘Increased’ should not be capitalized. Response: Typo has been corrected (Minor) Reviewer Comment 6: Fig. 2B/E/F – authors should be explicit about what the volcano plot colors scheme to ensure clarity of interpretation. Also, are the p-values presented raw or adjusted? Response: The cutoffs for highlighted genes include a log2 fold change of 0.5 or above (horizontal dashed lines) and adjusted p-value above 0.001 (indicated by the vertical dashed lines). This description is included in the figure legend of the revised manuscript. (Minor) Reviewer Comment 7: As far as I can tell, the Authors use increased expression of fibronectin by MRCs in the tdLN to support the claim that they are induced into a desmoplastic CAF-like state. This may indeed be the case, but more thorough analyses/discussions are needed to sufficiently support this claim. I would suggest leveraging publicly-available scRNA-seq data of desmoplastic CAFs (if such data exists) to assess similarly in transcriptional signatures. Alternatively, the language can be edited to lessen the claimed connection. Response: While we do single-out Fn1 in the text, we note that this is one of several genes differentially expressed in MRCs of the tumor dLN that relate to matrix deposition or tissue remodeling. Many others are additionally labeled in figure 2b. Additionally, both ECM organization and wound-healing are prominent in the pathway analysis as shown in Fig2a. For additional clarity, we have also included the full DEG list of MRC, BRC, fLEC, and PRC populations in the tdLN vs ndLN in Supplementary table 3 of the revised manuscript. We feel comfortable drawing the comparison to desmoplastic CAFs given that these indicators of altered matrix/remodeling arise within the context of the tumor-draining LN. However, to soften the claim, we have changed the wording of the section title from “reminiscent of desmoplastic CAFs” to instead read “reminiscent of the desmoplastic nature of CAFs”, as we acknowledge that ECM production/tissue remodeling are only one facet of CAF function. We do not want to make the claim that the transcriptional changes we see indicate that MRCs have become CAFs, but rather that they have taken on a key function typically associated with CAFs. (Minor) Reviewer Comment 8: “Indeed, genetic mouse models of homeostatic chemokine disruption exhibit impaired immunity.” Need reference. Response: Relevant references have been added. (Minor) Reviewer Comment 9: Should correctly note gene/protein identifiers (e.g., italics for gene names, proteins capitalized, etc.). Response: We have reformatted gene/protein names to match convention. (Minor) Reviewer Comment 10: The 30% mitochondrial gene expression threshold is quite high. Authors should interrogate whether any high pMito clusters were retained in analyses that could confound interpretation. Response: We carefully examined the distribution of mitochondrial gene expression across cells and found no evidence of high mitochondrial clusters that would indicate stressed or damaged cells. Additionally, downstream analyses, such as clustering and differential expression, did not reveal any confounding effects that could be attributed to mitochondrial gene expression. Based on these observations, we are confident that the retained cells are biologically relevant and do not introduce bias into our results. (Major) Reviewer Comment 1: Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The MC38 tumor model is not thought to robustly metastasize (e.g., only ~50% of orthotopically-transplanted tumors form LN metastases; Greenlee & King, 2022), especially after subcutaneous injection. Thus, it is unclear whether the signatures described in the paper truly represent the pre-metastatic niche LN. Without adequately addressing this concern, the authors need to recontextualize their observations to focus on the effects of primary tumor-mediated reprogramming on the tdLN rather than the pre-metastatic niche. Response: The reviewer raises a salient point in that subcutaneously implanted tumor models rarely in fact metastasize (at least within the timeframe at which the primary tumor remains within ethical growth limits). Orthotopically implanted tumors more readily metastasize, perhaps due to the native regional tissue environment of implantation. However, such models are often technically challenging and highly variable in terms of tumor growth kinetics. We opted to use subcutaneous MC38 tumors to minimize technical and experimental variance which might otherwise obscure the identification of transcriptional signatures influenced by tumor-derived factors. We feel the nature of multiple stromal alterations highlighted in this paper align well with previously proposed microenvironmental dependencies for tumor growth and metastasis highlighted within the broader literature, and it is for this reason that we contextualize these changes as “pre-metastatic”. However, we fully recognize that data herein do not constitute experimental proof that any or all of these transcriptional changes are in fact necessary for metastasis but rather offer these results as a resource for more definitive studies to follow. We agree that such experimental proof would necessitate the use of models for which lymph node metastasis can be explicitly documented following experimental perturbation. Such an effort is currently beyond the scope of this limited study. However, we do acknowledge that this is a critically important limitation, and discussion of this point has been included in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 2: “Within the resultant dataset, we identified 7 distinct clusters of fibroblastic cells and 4 clusters of lymphatic endothelial cells, each of which was annotated based on well-established identity markers” – Authors should include either reference to which markers were used or, better yet, a heatmap or dotplot showing expression levels for marker genes in each annotated cell type. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supplementary Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. (Major) Reviewer Comment 3: “Perivascular cells (PRCs), though not localized to the SCS, likewise exhibited significant transcriptomic changes, which may reflect support of vascular expansion” – While entirely possible, it is unclear how the observed increase in the numbers of DEGs suggests support of vascular expansion. Authors should analyze more deeply the genes that are differentially expressed (e.g., GSEA, targeted analysis of known proangiogenic factors expressed by PRCs, etc.) before making this claim. Response: This was a speculative comment due to the association of PRCs with LN vasculature, and the observed increase in BEC proliferation (fig1A). We have tempered this statement in the revised manuscript and included references to studies outlining potential function of PRCs. Importantly, we have also revised our discussion of PRC, in which we describe them as “not localized to the SCS”. Further review of the findings by Rodda et al. 2018 in which they describe localization of CD34+ stromal cells not only in the perivascular compartment, but also within the LN capsule. Transcriptional features identifying our PRC subset align with these CD34 SCs described by Rodda et al. and Sitnik et al. 2016. We have additionally included the full DEG list of PRCs in tdLN vs ndLN in the newly added Supplementary table 3. (Major) Reviewer Comment 4: The Authors make many claims throughout the manuscript about proliferative expansion of certain fibroblast and endothelial cell subsets in tumor-draining lymph nodes, but do not show any direct evidence of this increased proliferation. Since annotating proliferative cells via Mki67 and Hells expression in scRNA-seq data is possible, I recommend the Authors compare the proportions of proliferative subsets in all experimental groups to test/strengthen these claims. Response: Increased cell counts of each major stromal cell population (fibroblast, LEC, and BEC) within the dLN vs ndLN (seen in Fig1A) provides us some direct evidence of proliferative expansion of each of these broader populations. Within each stromal cell type, we then identify changes in the representation of subsets within in the scRNAseq dataset. For instance, “Fibroblasts” are presumed to have proliferated due to the >2 fold increase in cell numbers relative to matched non-draining LN (fig1A). Then, within all fibroblasts, there is then a higher proportion of MRC, PRC, and BRC in the dLN relative to the ndLN (Fig 1C), a differential which is not present in the control conditions of mice inoculated with fixed tumor cells (Fig 1D). This differential in subset representation is also visually evident in the newly added Supplementary figure 2. Unfortunately we do not have the capacity to measure absolute cell counts of individual subsets, and thus proliferative expansion is inferred from the combination of these above observations. Additionally, ki67 + cells are indeed present in our dataset, but constitute a small proportion of every cell population present. In our experience, ki67 has rarely been a reliable or quantitative measure of population expansion in tissues. (Major) Reviewer Comment 5: The Authors do not describe how they handled the removal of doublets during scRNA-seq analysis. The presence of doublets could significantly confound data interpretation – particularly in instances where the underlying cell type distributions between samples are known to differ, as in this case – and, thus, needs to be addressed to ensure the observations are not artefactual in nature. Response: We carefully assessed the quality control metrics of the dataset, including the number of RNA molecules (UMIs) and the number of genes detected per cell. All values fell within the expected range for single cells, as defined in Seurat’s documentation, and we did not observe any anomalies indicative of doublets. Based on these observations, we do not suspect the presence of doublets in our dataset at a level that would confound the interpretation of our results. This information has been added to the methods description in the revised manuscript. (Major) Reviewer Comment 6: In the Methods section, the Authors state that “The present study used tissues collected from naïve mice (n=4), mice bearing live MC38 tumors (n=3), or fixed MC38 tumor cells (n=3)” and then “No animals were excluded from analysis.” However, the described scRNA-seq analyses do not incorporate insights gained from the naïve mouse samples. Comparing naïve and fixed tumor samples could provide key insight for distinguishing the effects of live tumors on the tdLN. Moreover, including comparisons between naïve and fixed tumor samples would be critical for pinpointing the observed effects that are specifically due to antigenic challenge. Alternatively, if naïve samples were not used, the Methods section should be edited to clarify this point. Response: We appreciate the careful review and thoughtful suggestion. However naïve samples were not included in this assessment. The methods section was corrected to reflect the proper cohort used in this analysis. (Major) Reviewer Comment 7: In Fig. 2G, the authors show genes that are specifically enriched in tdLN MRCs and fLECs. How were these genes identified? Is Il33 expression between tdLNs isolated from mice harboring live and fixed tumors statistically-significantly different? The up-regulation between tdLN and ndLN is clear in both live and fixed tumor settings, but tdLNs in fixed tumor samples also increase expression of Il33 compared to ndLNs, suggesting that this observation may not be live tumor-specific. Response: The genes in Fig. 2G were identified by performing DEG analysis across dLN and ndLN of the live and fixed tumor condition. Genes which were commonly upregulated in both the MRC and fLEC of specifically the live tdLN subset were graphed in the bubble plot. The mean expression of IL33 in the dLN of mice receiving fixed tumor cells is overall slightly elevated over the corresponding ndLN, but did not reach statistical significance. A statistically significant change in expression was only seen in the dLN vs ndLN of live tumor-bearing mice. (Minor) Reviewer Comment 1: Authors state “In the pre-metastatic lymph node, expansion of both endothelium and FRCs has been documented” – true, but authors should cite appropriate literature references. Response: Citation has been added. (Minor) Reviewer Comment 2: How did authors verify that MC38 tumor cells were successfully fixed after 4% PFA exposure? Response: No tumor growth was observed in mice receiving fixed tumor cells at the experimental endpoint. (Minor) Reviewer Comment 3: Fig. 1A: Authors should clarify in the legend the reference point they used to calculate relative cell count. Imagining it was the average of the non-draining LN, but should be explicit to avoid confusion. Authors should also clarify how each cell type was identified using their flow cytometry panel. Response: Cell counts are presented as relative to the average of corresponding non-draining lymph nodes. Cell populations were identified by flow cytometry with the following markers: Fibroblastic Reticular (CD45-CD31-PDPN+), Lymphatic Endothelial (CD45-, CD31+, PDPN+), and Blood Endothelial (CD45-, CD31+, PDPN-). This information has been added to the figure legend in the revised manuscript. (Minor) Reviewer Comment 4: Fig. 1B and E: Authors do not ever define some of the acronyms used here (e.g., TRC, FDC) Response: The following definitions have been added to the text: Fibroblastic populations include T-zone Reticular Cells (TRC), B zone Reticular cells (BRC), Marginal Zone Reticular Cells (MRC), Follicular Dendritic Cells (FDC), Medulary Reticular cells (MedRC), Perivascular Reticular cells (PRC), and activated stromal cells (Act SC). Endothelial cell subsets include ceiling LECs (cLEC), floor LECs (fLEC), Medulary LECs (MedLEC), and Cortical LECs (CorLEC). (Minor) Reviewer Comment 5: Page 7: “In this context, Increased conduit thickness…” – ‘Increased’ should not be capitalized. Response: Typo has been corrected (Minor) Reviewer Comment 6: Fig. 2B/E/F – authors should be explicit about what the volcano plot colors scheme to ensure clarity of interpretation. Also, are the p-values presented raw or adjusted? Response: The cutoffs for highlighted genes include a log2 fold change of 0.5 or above (horizontal dashed lines) and adjusted p-value above 0.001 (indicated by the vertical dashed lines). This description is included in the figure legend of the revised manuscript. (Minor) Reviewer Comment 7: As far as I can tell, the Authors use increased expression of fibronectin by MRCs in the tdLN to support the claim that they are induced into a desmoplastic CAF-like state. This may indeed be the case, but more thorough analyses/discussions are needed to sufficiently support this claim. I would suggest leveraging publicly-available scRNA-seq data of desmoplastic CAFs (if such data exists) to assess similarly in transcriptional signatures. Alternatively, the language can be edited to lessen the claimed connection. Response: While we do single-out Fn1 in the text, we note that this is one of several genes differentially expressed in MRCs of the tumor dLN that relate to matrix deposition or tissue remodeling. Many others are additionally labeled in figure 2b. Additionally, both ECM organization and wound-healing are prominent in the pathway analysis as shown in Fig2a. For additional clarity, we have also included the full DEG list of MRC, BRC, fLEC, and PRC populations in the tdLN vs ndLN in Supplementary table 3 of the revised manuscript. We feel comfortable drawing the comparison to desmoplastic CAFs given that these indicators of altered matrix/remodeling arise within the context of the tumor-draining LN. However, to soften the claim, we have changed the wording of the section title from “reminiscent of desmoplastic CAFs” to instead read “reminiscent of the desmoplastic nature of CAFs”, as we acknowledge that ECM production/tissue remodeling are only one facet of CAF function. We do not want to make the claim that the transcriptional changes we see indicate that MRCs have become CAFs, but rather that they have taken on a key function typically associated with CAFs. (Minor) Reviewer Comment 8: “Indeed, genetic mouse models of homeostatic chemokine disruption exhibit impaired immunity.” Need reference. Response: Relevant references have been added. (Minor) Reviewer Comment 9: Should correctly note gene/protein identifiers (e.g., italics for gene names, proteins capitalized, etc.). Response: We have reformatted gene/protein names to match convention. (Minor) Reviewer Comment 10: The 30% mitochondrial gene expression threshold is quite high. Authors should interrogate whether any high pMito clusters were retained in analyses that could confound interpretation. Response: We carefully examined the distribution of mitochondrial gene expression across cells and found no evidence of high mitochondrial clusters that would indicate stressed or damaged cells. Additionally, downstream analyses, such as clustering and differential expression, did not reveal any confounding effects that could be attributed to mitochondrial gene expression. Based on these observations, we are confident that the retained cells are biologically relevant and do not introduce bias into our results. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Luther S. Reviewer Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.159080.r309554 ) The direct URL for this report is: https://f1000research.com/articles/13-223/v1#referee-response-309554 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Aug 2024 Sanjiv Luther , Department of Immunobiology, University of Lausanne, Lausanne, Switzerland Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.159080.r309554 Piquet and coauthors address in this study the interesting question of how the lymph node (LN) stroma changes or is reprorammed upon draining a tumor site with alterations presumably contributing to tumor metastasis to this site along with immune suppression. ... Continue reading READ ALL Piquet and coauthors address in this study the interesting question of how the lymph node (LN) stroma changes or is reprorammed upon draining a tumor site with alterations presumably contributing to tumor metastasis to this site along with immune suppression. They present insightful scRNAseq datasets describing how different murine LN stromal cell types react to either a live or a fixed inoculation with the MC38 colorectal cancer cell line, when compared to a nondraining LN. While both types of inoculation lead to a comparable LN swelling, including a strong amplification of the LN fibroblasts, lymphatic endothelium (LEC) and blood endothelium (BEC), consistent with a strong inflammatory response in both settings, the impact of live tumor cell inoculation shifted much more the proportions and transcriptional profile of distinct subsets of stromal cells compared to injection with fixed tumor cells, pointing to tumor-specific factors driving these changes. The transcriptional shifts were most pronounced among two fibroblast subsets, termed MRC and BRC, as well as among two LEC subsets that are also those thought to be most exposed to the incoming lymph and its factors. The resource provided by the authors should be of great interest to all scientists interested in tumor metastasis or anti-tumor immunity. The manuscript is well written, the data are of high quality and appropriately displayed; and the findings are discussed in an insightful way. Major points: 1) The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed genes for each subset, so that one can judge the cells clustered for example as MRC or BRC. Currently one would be forced to redo all the bioinformatic work again, based on the individual data files provided. Such additional data would help considerably the expert reader to assess and interpret the data and the labeling of the clusters, especially as the frequencies of the FDC and MRC populations look higher compared to other scRNAseq data sets (eg. Rodda/Cyster Immunity 2018) or relative to the analysis by flow cytometry (eg. Huang/Luther, PNAS 2018), also relative to TRC and MedRC. Any histological stainings (with antibodies or by ISH) validating the key scRNAseq data, including the subset identification/clustering would greatly enhance the value of the data; eg. are the MRC-like cells really still restricted to the SCS-lining area, as are the fLEC-phenotype cells 2) Similarly, no tables are provided as supplement to list the DE genes in the various settings as only part of them are mentioned in the figure 2, and they are limited to the ones showing an increase with none labeled showing a decrease in a live tumor cell draining LN (vs the nondraining LN). This reviewer can imagine that labeling also part of the downregulated genes may render the data and its discussion more complex but then at least tables containing these data should be provided to allow an interested reader to look into them without need to reanalyze all the data. They may contain data relevant for the main question of the paper; eg. how does the LN suppress anti-tumor immunity or get prepared for tumor metastasis. Please improve also the description in the legend so that one understands why the log2 fold changes is negative for transcripts enriched in drLN. 3) The draining LN response is investigated on day 12 after tumor cell inoculation, comparing live tumor cell vs fixed tumor cell injection which is an elegant approach. As the same number of cells is injected in the setting of live cells as of fixed cells, but the final number of live tumor cells is not stated for d12 (but is presumably much higher, by a factor of several fold), the authors should probably acknowledge in the discussion the large difference in final tumor cell material in the two settings that is likely to contribute to the transcriptional differences observed, besides larger differences in the tissue cells of the primary injection site during the time period upto d12. Thus, the difference in factors infusing the LN is not only due to the live tumor cells but also due to the difference in final tumor cell numbers and their differential effects on the peripheral tissue in the two settings. 4) It is unclear whether the authors verified if any of the live tumor injected mice showed tumor cell metastasis to the draining LNs which got analyzed. That could have been analyzed by flow cytometry (CD45- cells with specific FSC/SSC characteristics; or a non-stated epithelial marker) or by other means. This information would be valuable to know given the likely difference between premetastatic vs metastatic LNs stroma. Minor points: The results start with a rather lengthy intro and part of it could be incorporated into the introduction where some of these points are already raised. BRC are claimed to populate the interfollicular space; please explain the rational or the publication stating this. Enzymatic digestion (‘as previously described’): please add reference with a more detailed protocol as this aspect is key for appropriate reproduction Mention at beginning that MC38 are a colorectal carcinoma cell line In the conduit section: conduits are not simply fibers (although they appear that way histologically and contain fibers) but have also basement membranes thereby forming true tubes/channels; thus the term fiber is not ideal. Similar, the term ‘transendothelial channels’ is not appropriate even when talking of the SCS where occasionally such channels traversing the SCS can be observed but the majority of conduits do not seem to traverse the endothelium. ‘Exhibited of’ (please drop ‘of’) IL33 discussion: may add another reference showing role of IL33 for memory T cell response (Marx et al.,2024 [Ref 1]) Please spell out LDHA and PKM at first mentioning and possibly the reaction they catalyze to inform the non-specialist reader. Fig.1B and D: I assume these Umaps show data compiled from all 4 groups of mice (please state clearly in legend); it would be of interest to display the Umaps of each group separately (in a supplementary figure) for readers to understand the stromal cell clustering in a 2D space for the 4 groups. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Marx AF, Kallert SM, Brunner TM, Villegas JA, et al.: The alarmin interleukin-33 promotes the expansion and preserves the stemness of Tcf-1+ CD8+ T cells in chronic viral infection. Immunity . 2023; 56 (4): 813-828.e10 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: lymph nodes, stromal cells, immunity, scRNAseq I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Luther S. Reviewer Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.159080.r309554 ) The direct URL for this report is: https://f1000research.com/articles/13-223/v1#referee-response-309554 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Apr 2025 Jonathan Chang , Oncology Translational Research, Novartis, Cambridge, 02139, USA 10 Apr 2025 Author Response (Major) Reviewer Comment 1: The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed ... Continue reading (Major) Reviewer Comment 1: The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed genes for each subset, so that one can judge the cells clustered for example as MRC or BRC. Currently one would be forced to redo all the bioinformatic work again, based on the individual data files provided. Such additional data would help considerably the expert reader to assess and interpret the data and the labeling of the clusters, especially as the frequencies of the FDC and MRC populations look higher compared to other scRNAseq data sets (eg. Rodda/Cyster Immunity 2018) or relative to the analysis by flow cytometry (eg. Huang/Luther, PNAS 2018), also relative to TRC and MedRC. Any histological stainings (with antibodies or by ISH) validating the key scRNAseq data, including the subset identification/clustering would greatly enhance the value of the data; eg. are the MRC-like cells really still restricted to the SCS-lining area, as are the fLEC-phenotype cells. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supp Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. We appreciate the reviewers’ careful consideration of population frequencies within our dataset relative to the above referenced studies. While we are unable to conclusively account for these discrepancies, we hypothesize that differences in frequencies may relate to 1) distinct methodology for tissue dissociation, 2) differences in cell enrichment prior to scRNAseq, and 3) differences in which specific peripheral LNs are included in the dataset. Importantly, we do not make any conclusions or assumptions that are specifically about the baseline abundance of stromal subsets relative to other subsets, but rather all of the comments we make regarding population frequency is centered on changes of subset representation between tumor-draining and corresponding non-draining LNs, and thus these potential variables resulting from sample preparation methodology are internally controlled. We are unfortunately unable to provide comprehensive IHC to corroborate the spatial representation of these markers in our dataset, but IHC of LN tissues exploring many of the above-mentioned markers has been performed in the referenced publications. Regarding the specific example of MRC vs BRC, the primary distinction between these clusters was a lack of madcam1 and tnfsf11 on the subset we identify as “BRCs” relative to MRCs. Both clusters expressed cxcl13 and exhibited low expression of markers typically associated with TRC (including ccl19 / ccl21 ). (Major) Reviewer Comment 2: Similarly, no tables are provided as supplement to list the DE genes in the various settings as only part of them are mentioned in the figure 2, and they are limited to the ones showing an increase with none labeled showing a decrease in a live tumor cell draining LN (vs the nondraining LN). This reviewer can imagine that labeling also part of the downregulated genes may render the data and its discussion more complex but then at least tables containing these data should be provided to allow an interested reader to look into them without need to reanalyze all the data. They may contain data relevant for the main question of the paper; eg. how does the LN suppress anti-tumor immunity or get prepared for tumor metastasis. Please improve also the description in the legend so that one understands why the log2 fold changes is negative for transcripts enriched in drLN. Response: Tables including the complete DEG lists of the indicated subpopulations in tdLN vs ndLN have been included in Supplementary table 3 of the revised manuscript. Regarding log₂ fold change interpretation: In volcano plots (B, D, F), negative log₂ fold change values indicate genes that are more highly expressed in draining lymph nodes (dLNs) relative to non-draining lymph nodes (ndLNs). This is because differential expression was calculated using ndLNs as the reference group. As a result, transcripts upregulated in dLNs are represented on the left side of the plot (negative fold change), while genes enriched in ndLNs appear on the right side (positive fold change). (Major) Reviewer Comment 3: The draining LN response is investigated on day 12 after tumor cell inoculation, comparing live tumor cell vs fixed tumor cell injection which is an elegant approach. As the same number of cells is injected in the setting of live cells as of fixed cells, but the final number of live tumor cells is not stated for d12 (but is presumably much higher, by a factor of several fold), the authors should probably acknowledge in the discussion the large difference in final tumor cell material in the two settings that is likely to contribute to the transcriptional differences observed, besides larger differences in the tissue cells of the primary injection site during the time period upto d12. Thus, the difference in factors infusing the LN is not only due to the live tumor cells but also due to the difference in final tumor cell numbers and their differential effects on the peripheral tissue in the two settings. Response: The reviewer is correct, in that mice inoculated with “live” tumor cell will invariably harbor more tumor cells (and thus more overall tumor antigen) at the time of harvesting the associated LN due to continued tumor growth. This is unfortunately difficult to control for experimentally. We have noted this concern as a potential caveat in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 4: It is unclear whether the authors verified if any of the live tumor injected mice showed tumor cell metastasis to the draining LNs which got analyzed. That could have been analyzed by flow cytometry (CD45- cells with specific FSC/SSC characteristics; or a non-stated epithelial marker) or by other means. This information would be valuable to know given the likely difference between premetastatic vs metastatic LNs stroma. Response: Overt metastatic tumor growth was not observed in any live tumor-bearing mice, nor were any clusters identified as cancer cells within the total 50,381 cells sequenced in this dataset. However, because these samples were enriched for stromal populations, it is likely that cancer cells (if present) would be lost following sample enrichment, particularly if originally present in low numbers. As the reviewer suggested, we looked further into our (unenriched) flow cytometry data of dissociated TdLN and ndLNs to identify presence of tumor cells. While no epithelial marker is included in this panel, MC38 cancer cells can be identified as CD45-, PDPN-, CD31-, PD-L1+ (PMID: 28507803). Indeed we do find small numbers of these cells specifically in the tumor-draining lymph node. Average cell counts were 114 total cells in TdLN compared to 4 in NdLN (p value = 0.0085). This analysis has been added to the revised manuscript as supplementary figure 3. We believe that presence of cancer cells at such low numbers, may reflect either shed/migrating tumor cells or the presence of early micrometastases. Discussion of this topic is likewise added to the revised manuscript, and the above-mentioned flow cytometry analysis has been included as Supplementary Fig. 3. (Minor) Reviewer Comment 1: The results start with a rather lengthy intro and part of it could be incorporated into the introduction where some of these points are already raised. Response: Per the reviewer’s suggestion, we have removed the first paragraph of the results. This paragraph reiterated the role of tissue stroma in tumor progression and was mostly redundant. (Minor) Reviewer Comment 2: BRC are claimed to populate the interfollicular space; please explain the rational or the publication stating this. Response: While the exact subsetting criteria and nomenclature varies across different publications to date, the population we refer to as “BRCs” most closely aligns with interfollicular reticular cell populations described as Ccl19lo, Ch25h+, Cxcl13+ in Pikor et al. (PMID: 32424359) and Rodda et al. (PMID: 29752062). This population (or a proportion of this population) is considered to reside in the interfollicular space due to IHC and RNAscope data included in those respective publications. Regarding the nomenclature, this population of reticular cells is considered a subset of “B cell interacting” reticular cells in Pikor et al. Whereas it is referred to as a subset of TRC in Rodda et al. We use the BRC nomenclature due to low Ccl19 and high Cxcl13 expression, suggestive of a B-cell association. (Minor) Reviewer Comment 3: Enzymatic digestion (‘as previously described’): please add reference with a more detailed protocol as this aspect is key for appropriate reproduction. Response: Reference added to revised manuscript (Minor) Reviewer Comment 4: Mention at beginning that MC38 are a colorectal carcinoma cell line. Response: mention of MC38 as colorectal carcinoma cells has been added to the revised manuscript. (Minor) Reviewer Comment 5: In the conduit section: conduits are not simply fibers (although they appear that way histologically and contain fibers) but have also basement membranes thereby forming true tubes/channels; thus the term fiber is not ideal. Similar, the term ‘transendothelial channels’ is not appropriate even when talking of the SCS where occasionally such channels traversing the SCS can be observed but the majority of conduits do not seem to traverse the endothelium. Response: We have changed the description of LN conduits as “a branched network of fibers” to state instead “a branched network of fibrous structures”. While our discussion of conduit networks does not fully address the intricacies of conduit anatomy, we have included relevant references to more in-depth exploration of this topic. Regarding the statement of transendothelial channels, we discuss the nature of these structures as described in the referenced citation (Ranktakari et al 2015). We do not make any statement speculating whether any conduits themselves traverse the endothelium. Rather, we discuss the findings of the referenced study which explore the idea of size-restriction as a function of PLVAP fibrils that exist within transendothelial channels. To clarify, in this publication, “transendothelial channels” and conduits are separate structures. The former spans the length of sinus-lining endothelial cells, while the latter underlies the endothelium (as such this finding is not reliant on whether any or all conduits actually traverse the endothelium). This publication suggests that molecules pass from LN sinus into the LN parenchyma through transendothelial channels, and upon traversing the endothelial lining, can subsequently access the underlying conduit network freely. However, the structure of the transendothelial channel (dictated by PLVAP) is the gating feature for larger molecules. We reference this finding because any structural disruption to the sinus-lining endothelium could theoretically perturb the above described regulation of molecule sizes that are allowed to pass into the conduit network. This would be one potential explanation to the loss of size-exclusivity noted by Riedel et al within the tdLN. (Minor) Reviewer Comment 6: ‘Exhibited of’ (please drop ‘of’). Response: Typo has been corrected. (Minor) Reviewer Comment 7: IL33 discussion: may add another reference showing role of IL33 for memory T cell response (Marx et al.,2024 [Ref 1]). Response: This reference has be added. Thank you for the suggestion (Minor) Reviewer Comment 8: Please spell out LDHA and PKM at first mentioning and possibly the reaction they catalyze to inform the non-specialist reader. Response: Definitions have been added. (Minor) Reviewer Comment 9: Fig.1B and D: I assume these Umaps show data compiled from all 4 groups of mice (please state clearly in legend); it would be of interest to display the Umaps of each group separately (in a supplementary figure) for readers to understand the stromal cell clustering in a 2D space for the 4 groups. Response: Per the reviewer’s suggestion, we have generated a new supplementary (Supplementary figure 2), which exhibits the distribution of cells from each treatment group within the corresponding endothelial (Supplementary Fig 2A) and fibroblast (Supplementary Fig 2B) Umaps. Visualization of cell clustering in this 2D space further reflects the increased representation of specific stromal subclusters (including fLEC, MRC, and PRC) from the tumor-draining LN. (Major) Reviewer Comment 1: The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed genes for each subset, so that one can judge the cells clustered for example as MRC or BRC. Currently one would be forced to redo all the bioinformatic work again, based on the individual data files provided. Such additional data would help considerably the expert reader to assess and interpret the data and the labeling of the clusters, especially as the frequencies of the FDC and MRC populations look higher compared to other scRNAseq data sets (eg. Rodda/Cyster Immunity 2018) or relative to the analysis by flow cytometry (eg. Huang/Luther, PNAS 2018), also relative to TRC and MedRC. Any histological stainings (with antibodies or by ISH) validating the key scRNAseq data, including the subset identification/clustering would greatly enhance the value of the data; eg. are the MRC-like cells really still restricted to the SCS-lining area, as are the fLEC-phenotype cells. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supp Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. We appreciate the reviewers’ careful consideration of population frequencies within our dataset relative to the above referenced studies. While we are unable to conclusively account for these discrepancies, we hypothesize that differences in frequencies may relate to 1) distinct methodology for tissue dissociation, 2) differences in cell enrichment prior to scRNAseq, and 3) differences in which specific peripheral LNs are included in the dataset. Importantly, we do not make any conclusions or assumptions that are specifically about the baseline abundance of stromal subsets relative to other subsets, but rather all of the comments we make regarding population frequency is centered on changes of subset representation between tumor-draining and corresponding non-draining LNs, and thus these potential variables resulting from sample preparation methodology are internally controlled. We are unfortunately unable to provide comprehensive IHC to corroborate the spatial representation of these markers in our dataset, but IHC of LN tissues exploring many of the above-mentioned markers has been performed in the referenced publications. Regarding the specific example of MRC vs BRC, the primary distinction between these clusters was a lack of madcam1 and tnfsf11 on the subset we identify as “BRCs” relative to MRCs. Both clusters expressed cxcl13 and exhibited low expression of markers typically associated with TRC (including ccl19 / ccl21 ). (Major) Reviewer Comment 2: Similarly, no tables are provided as supplement to list the DE genes in the various settings as only part of them are mentioned in the figure 2, and they are limited to the ones showing an increase with none labeled showing a decrease in a live tumor cell draining LN (vs the nondraining LN). This reviewer can imagine that labeling also part of the downregulated genes may render the data and its discussion more complex but then at least tables containing these data should be provided to allow an interested reader to look into them without need to reanalyze all the data. They may contain data relevant for the main question of the paper; eg. how does the LN suppress anti-tumor immunity or get prepared for tumor metastasis. Please improve also the description in the legend so that one understands why the log2 fold changes is negative for transcripts enriched in drLN. Response: Tables including the complete DEG lists of the indicated subpopulations in tdLN vs ndLN have been included in Supplementary table 3 of the revised manuscript. Regarding log₂ fold change interpretation: In volcano plots (B, D, F), negative log₂ fold change values indicate genes that are more highly expressed in draining lymph nodes (dLNs) relative to non-draining lymph nodes (ndLNs). This is because differential expression was calculated using ndLNs as the reference group. As a result, transcripts upregulated in dLNs are represented on the left side of the plot (negative fold change), while genes enriched in ndLNs appear on the right side (positive fold change). (Major) Reviewer Comment 3: The draining LN response is investigated on day 12 after tumor cell inoculation, comparing live tumor cell vs fixed tumor cell injection which is an elegant approach. As the same number of cells is injected in the setting of live cells as of fixed cells, but the final number of live tumor cells is not stated for d12 (but is presumably much higher, by a factor of several fold), the authors should probably acknowledge in the discussion the large difference in final tumor cell material in the two settings that is likely to contribute to the transcriptional differences observed, besides larger differences in the tissue cells of the primary injection site during the time period upto d12. Thus, the difference in factors infusing the LN is not only due to the live tumor cells but also due to the difference in final tumor cell numbers and their differential effects on the peripheral tissue in the two settings. Response: The reviewer is correct, in that mice inoculated with “live” tumor cell will invariably harbor more tumor cells (and thus more overall tumor antigen) at the time of harvesting the associated LN due to continued tumor growth. This is unfortunately difficult to control for experimentally. We have noted this concern as a potential caveat in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 4: It is unclear whether the authors verified if any of the live tumor injected mice showed tumor cell metastasis to the draining LNs which got analyzed. That could have been analyzed by flow cytometry (CD45- cells with specific FSC/SSC characteristics; or a non-stated epithelial marker) or by other means. This information would be valuable to know given the likely difference between premetastatic vs metastatic LNs stroma. Response: Overt metastatic tumor growth was not observed in any live tumor-bearing mice, nor were any clusters identified as cancer cells within the total 50,381 cells sequenced in this dataset. However, because these samples were enriched for stromal populations, it is likely that cancer cells (if present) would be lost following sample enrichment, particularly if originally present in low numbers. As the reviewer suggested, we looked further into our (unenriched) flow cytometry data of dissociated TdLN and ndLNs to identify presence of tumor cells. While no epithelial marker is included in this panel, MC38 cancer cells can be identified as CD45-, PDPN-, CD31-, PD-L1+ (PMID: 28507803). Indeed we do find small numbers of these cells specifically in the tumor-draining lymph node. Average cell counts were 114 total cells in TdLN compared to 4 in NdLN (p value = 0.0085). This analysis has been added to the revised manuscript as supplementary figure 3. We believe that presence of cancer cells at such low numbers, may reflect either shed/migrating tumor cells or the presence of early micrometastases. Discussion of this topic is likewise added to the revised manuscript, and the above-mentioned flow cytometry analysis has been included as Supplementary Fig. 3. (Minor) Reviewer Comment 1: The results start with a rather lengthy intro and part of it could be incorporated into the introduction where some of these points are already raised. Response: Per the reviewer’s suggestion, we have removed the first paragraph of the results. This paragraph reiterated the role of tissue stroma in tumor progression and was mostly redundant. (Minor) Reviewer Comment 2: BRC are claimed to populate the interfollicular space; please explain the rational or the publication stating this. Response: While the exact subsetting criteria and nomenclature varies across different publications to date, the population we refer to as “BRCs” most closely aligns with interfollicular reticular cell populations described as Ccl19lo, Ch25h+, Cxcl13+ in Pikor et al. (PMID: 32424359) and Rodda et al. (PMID: 29752062). This population (or a proportion of this population) is considered to reside in the interfollicular space due to IHC and RNAscope data included in those respective publications. Regarding the nomenclature, this population of reticular cells is considered a subset of “B cell interacting” reticular cells in Pikor et al. Whereas it is referred to as a subset of TRC in Rodda et al. We use the BRC nomenclature due to low Ccl19 and high Cxcl13 expression, suggestive of a B-cell association. (Minor) Reviewer Comment 3: Enzymatic digestion (‘as previously described’): please add reference with a more detailed protocol as this aspect is key for appropriate reproduction. Response: Reference added to revised manuscript (Minor) Reviewer Comment 4: Mention at beginning that MC38 are a colorectal carcinoma cell line. Response: mention of MC38 as colorectal carcinoma cells has been added to the revised manuscript. (Minor) Reviewer Comment 5: In the conduit section: conduits are not simply fibers (although they appear that way histologically and contain fibers) but have also basement membranes thereby forming true tubes/channels; thus the term fiber is not ideal. Similar, the term ‘transendothelial channels’ is not appropriate even when talking of the SCS where occasionally such channels traversing the SCS can be observed but the majority of conduits do not seem to traverse the endothelium. Response: We have changed the description of LN conduits as “a branched network of fibers” to state instead “a branched network of fibrous structures”. While our discussion of conduit networks does not fully address the intricacies of conduit anatomy, we have included relevant references to more in-depth exploration of this topic. Regarding the statement of transendothelial channels, we discuss the nature of these structures as described in the referenced citation (Ranktakari et al 2015). We do not make any statement speculating whether any conduits themselves traverse the endothelium. Rather, we discuss the findings of the referenced study which explore the idea of size-restriction as a function of PLVAP fibrils that exist within transendothelial channels. To clarify, in this publication, “transendothelial channels” and conduits are separate structures. The former spans the length of sinus-lining endothelial cells, while the latter underlies the endothelium (as such this finding is not reliant on whether any or all conduits actually traverse the endothelium). This publication suggests that molecules pass from LN sinus into the LN parenchyma through transendothelial channels, and upon traversing the endothelial lining, can subsequently access the underlying conduit network freely. However, the structure of the transendothelial channel (dictated by PLVAP) is the gating feature for larger molecules. We reference this finding because any structural disruption to the sinus-lining endothelium could theoretically perturb the above described regulation of molecule sizes that are allowed to pass into the conduit network. This would be one potential explanation to the loss of size-exclusivity noted by Riedel et al within the tdLN. (Minor) Reviewer Comment 6: ‘Exhibited of’ (please drop ‘of’). Response: Typo has been corrected. (Minor) Reviewer Comment 7: IL33 discussion: may add another reference showing role of IL33 for memory T cell response (Marx et al.,2024 [Ref 1]). Response: This reference has be added. Thank you for the suggestion (Minor) Reviewer Comment 8: Please spell out LDHA and PKM at first mentioning and possibly the reaction they catalyze to inform the non-specialist reader. Response: Definitions have been added. (Minor) Reviewer Comment 9: Fig.1B and D: I assume these Umaps show data compiled from all 4 groups of mice (please state clearly in legend); it would be of interest to display the Umaps of each group separately (in a supplementary figure) for readers to understand the stromal cell clustering in a 2D space for the 4 groups. Response: Per the reviewer’s suggestion, we have generated a new supplementary (Supplementary figure 2), which exhibits the distribution of cells from each treatment group within the corresponding endothelial (Supplementary Fig 2A) and fibroblast (Supplementary Fig 2B) Umaps. Visualization of cell clustering in this 2D space further reflects the increased representation of specific stromal subclusters (including fLEC, MRC, and PRC) from the tumor-draining LN. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Apr 2025 Jonathan Chang , Oncology Translational Research, Novartis, Cambridge, 02139, USA 10 Apr 2025 Author Response (Major) Reviewer Comment 1: The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed ... Continue reading (Major) Reviewer Comment 1: The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed genes for each subset, so that one can judge the cells clustered for example as MRC or BRC. Currently one would be forced to redo all the bioinformatic work again, based on the individual data files provided. Such additional data would help considerably the expert reader to assess and interpret the data and the labeling of the clusters, especially as the frequencies of the FDC and MRC populations look higher compared to other scRNAseq data sets (eg. Rodda/Cyster Immunity 2018) or relative to the analysis by flow cytometry (eg. Huang/Luther, PNAS 2018), also relative to TRC and MedRC. Any histological stainings (with antibodies or by ISH) validating the key scRNAseq data, including the subset identification/clustering would greatly enhance the value of the data; eg. are the MRC-like cells really still restricted to the SCS-lining area, as are the fLEC-phenotype cells. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supp Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. We appreciate the reviewers’ careful consideration of population frequencies within our dataset relative to the above referenced studies. While we are unable to conclusively account for these discrepancies, we hypothesize that differences in frequencies may relate to 1) distinct methodology for tissue dissociation, 2) differences in cell enrichment prior to scRNAseq, and 3) differences in which specific peripheral LNs are included in the dataset. Importantly, we do not make any conclusions or assumptions that are specifically about the baseline abundance of stromal subsets relative to other subsets, but rather all of the comments we make regarding population frequency is centered on changes of subset representation between tumor-draining and corresponding non-draining LNs, and thus these potential variables resulting from sample preparation methodology are internally controlled. We are unfortunately unable to provide comprehensive IHC to corroborate the spatial representation of these markers in our dataset, but IHC of LN tissues exploring many of the above-mentioned markers has been performed in the referenced publications. Regarding the specific example of MRC vs BRC, the primary distinction between these clusters was a lack of madcam1 and tnfsf11 on the subset we identify as “BRCs” relative to MRCs. Both clusters expressed cxcl13 and exhibited low expression of markers typically associated with TRC (including ccl19 / ccl21 ). (Major) Reviewer Comment 2: Similarly, no tables are provided as supplement to list the DE genes in the various settings as only part of them are mentioned in the figure 2, and they are limited to the ones showing an increase with none labeled showing a decrease in a live tumor cell draining LN (vs the nondraining LN). This reviewer can imagine that labeling also part of the downregulated genes may render the data and its discussion more complex but then at least tables containing these data should be provided to allow an interested reader to look into them without need to reanalyze all the data. They may contain data relevant for the main question of the paper; eg. how does the LN suppress anti-tumor immunity or get prepared for tumor metastasis. Please improve also the description in the legend so that one understands why the log2 fold changes is negative for transcripts enriched in drLN. Response: Tables including the complete DEG lists of the indicated subpopulations in tdLN vs ndLN have been included in Supplementary table 3 of the revised manuscript. Regarding log₂ fold change interpretation: In volcano plots (B, D, F), negative log₂ fold change values indicate genes that are more highly expressed in draining lymph nodes (dLNs) relative to non-draining lymph nodes (ndLNs). This is because differential expression was calculated using ndLNs as the reference group. As a result, transcripts upregulated in dLNs are represented on the left side of the plot (negative fold change), while genes enriched in ndLNs appear on the right side (positive fold change). (Major) Reviewer Comment 3: The draining LN response is investigated on day 12 after tumor cell inoculation, comparing live tumor cell vs fixed tumor cell injection which is an elegant approach. As the same number of cells is injected in the setting of live cells as of fixed cells, but the final number of live tumor cells is not stated for d12 (but is presumably much higher, by a factor of several fold), the authors should probably acknowledge in the discussion the large difference in final tumor cell material in the two settings that is likely to contribute to the transcriptional differences observed, besides larger differences in the tissue cells of the primary injection site during the time period upto d12. Thus, the difference in factors infusing the LN is not only due to the live tumor cells but also due to the difference in final tumor cell numbers and their differential effects on the peripheral tissue in the two settings. Response: The reviewer is correct, in that mice inoculated with “live” tumor cell will invariably harbor more tumor cells (and thus more overall tumor antigen) at the time of harvesting the associated LN due to continued tumor growth. This is unfortunately difficult to control for experimentally. We have noted this concern as a potential caveat in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 4: It is unclear whether the authors verified if any of the live tumor injected mice showed tumor cell metastasis to the draining LNs which got analyzed. That could have been analyzed by flow cytometry (CD45- cells with specific FSC/SSC characteristics; or a non-stated epithelial marker) or by other means. This information would be valuable to know given the likely difference between premetastatic vs metastatic LNs stroma. Response: Overt metastatic tumor growth was not observed in any live tumor-bearing mice, nor were any clusters identified as cancer cells within the total 50,381 cells sequenced in this dataset. However, because these samples were enriched for stromal populations, it is likely that cancer cells (if present) would be lost following sample enrichment, particularly if originally present in low numbers. As the reviewer suggested, we looked further into our (unenriched) flow cytometry data of dissociated TdLN and ndLNs to identify presence of tumor cells. While no epithelial marker is included in this panel, MC38 cancer cells can be identified as CD45-, PDPN-, CD31-, PD-L1+ (PMID: 28507803). Indeed we do find small numbers of these cells specifically in the tumor-draining lymph node. Average cell counts were 114 total cells in TdLN compared to 4 in NdLN (p value = 0.0085). This analysis has been added to the revised manuscript as supplementary figure 3. We believe that presence of cancer cells at such low numbers, may reflect either shed/migrating tumor cells or the presence of early micrometastases. Discussion of this topic is likewise added to the revised manuscript, and the above-mentioned flow cytometry analysis has been included as Supplementary Fig. 3. (Minor) Reviewer Comment 1: The results start with a rather lengthy intro and part of it could be incorporated into the introduction where some of these points are already raised. Response: Per the reviewer’s suggestion, we have removed the first paragraph of the results. This paragraph reiterated the role of tissue stroma in tumor progression and was mostly redundant. (Minor) Reviewer Comment 2: BRC are claimed to populate the interfollicular space; please explain the rational or the publication stating this. Response: While the exact subsetting criteria and nomenclature varies across different publications to date, the population we refer to as “BRCs” most closely aligns with interfollicular reticular cell populations described as Ccl19lo, Ch25h+, Cxcl13+ in Pikor et al. (PMID: 32424359) and Rodda et al. (PMID: 29752062). This population (or a proportion of this population) is considered to reside in the interfollicular space due to IHC and RNAscope data included in those respective publications. Regarding the nomenclature, this population of reticular cells is considered a subset of “B cell interacting” reticular cells in Pikor et al. Whereas it is referred to as a subset of TRC in Rodda et al. We use the BRC nomenclature due to low Ccl19 and high Cxcl13 expression, suggestive of a B-cell association. (Minor) Reviewer Comment 3: Enzymatic digestion (‘as previously described’): please add reference with a more detailed protocol as this aspect is key for appropriate reproduction. Response: Reference added to revised manuscript (Minor) Reviewer Comment 4: Mention at beginning that MC38 are a colorectal carcinoma cell line. Response: mention of MC38 as colorectal carcinoma cells has been added to the revised manuscript. (Minor) Reviewer Comment 5: In the conduit section: conduits are not simply fibers (although they appear that way histologically and contain fibers) but have also basement membranes thereby forming true tubes/channels; thus the term fiber is not ideal. Similar, the term ‘transendothelial channels’ is not appropriate even when talking of the SCS where occasionally such channels traversing the SCS can be observed but the majority of conduits do not seem to traverse the endothelium. Response: We have changed the description of LN conduits as “a branched network of fibers” to state instead “a branched network of fibrous structures”. While our discussion of conduit networks does not fully address the intricacies of conduit anatomy, we have included relevant references to more in-depth exploration of this topic. Regarding the statement of transendothelial channels, we discuss the nature of these structures as described in the referenced citation (Ranktakari et al 2015). We do not make any statement speculating whether any conduits themselves traverse the endothelium. Rather, we discuss the findings of the referenced study which explore the idea of size-restriction as a function of PLVAP fibrils that exist within transendothelial channels. To clarify, in this publication, “transendothelial channels” and conduits are separate structures. The former spans the length of sinus-lining endothelial cells, while the latter underlies the endothelium (as such this finding is not reliant on whether any or all conduits actually traverse the endothelium). This publication suggests that molecules pass from LN sinus into the LN parenchyma through transendothelial channels, and upon traversing the endothelial lining, can subsequently access the underlying conduit network freely. However, the structure of the transendothelial channel (dictated by PLVAP) is the gating feature for larger molecules. We reference this finding because any structural disruption to the sinus-lining endothelium could theoretically perturb the above described regulation of molecule sizes that are allowed to pass into the conduit network. This would be one potential explanation to the loss of size-exclusivity noted by Riedel et al within the tdLN. (Minor) Reviewer Comment 6: ‘Exhibited of’ (please drop ‘of’). Response: Typo has been corrected. (Minor) Reviewer Comment 7: IL33 discussion: may add another reference showing role of IL33 for memory T cell response (Marx et al.,2024 [Ref 1]). Response: This reference has be added. Thank you for the suggestion (Minor) Reviewer Comment 8: Please spell out LDHA and PKM at first mentioning and possibly the reaction they catalyze to inform the non-specialist reader. Response: Definitions have been added. (Minor) Reviewer Comment 9: Fig.1B and D: I assume these Umaps show data compiled from all 4 groups of mice (please state clearly in legend); it would be of interest to display the Umaps of each group separately (in a supplementary figure) for readers to understand the stromal cell clustering in a 2D space for the 4 groups. Response: Per the reviewer’s suggestion, we have generated a new supplementary (Supplementary figure 2), which exhibits the distribution of cells from each treatment group within the corresponding endothelial (Supplementary Fig 2A) and fibroblast (Supplementary Fig 2B) Umaps. Visualization of cell clustering in this 2D space further reflects the increased representation of specific stromal subclusters (including fLEC, MRC, and PRC) from the tumor-draining LN. (Major) Reviewer Comment 1: The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed genes for each subset, so that one can judge the cells clustered for example as MRC or BRC. Currently one would be forced to redo all the bioinformatic work again, based on the individual data files provided. Such additional data would help considerably the expert reader to assess and interpret the data and the labeling of the clusters, especially as the frequencies of the FDC and MRC populations look higher compared to other scRNAseq data sets (eg. Rodda/Cyster Immunity 2018) or relative to the analysis by flow cytometry (eg. Huang/Luther, PNAS 2018), also relative to TRC and MedRC. Any histological stainings (with antibodies or by ISH) validating the key scRNAseq data, including the subset identification/clustering would greatly enhance the value of the data; eg. are the MRC-like cells really still restricted to the SCS-lining area, as are the fLEC-phenotype cells. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supp Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. We appreciate the reviewers’ careful consideration of population frequencies within our dataset relative to the above referenced studies. While we are unable to conclusively account for these discrepancies, we hypothesize that differences in frequencies may relate to 1) distinct methodology for tissue dissociation, 2) differences in cell enrichment prior to scRNAseq, and 3) differences in which specific peripheral LNs are included in the dataset. Importantly, we do not make any conclusions or assumptions that are specifically about the baseline abundance of stromal subsets relative to other subsets, but rather all of the comments we make regarding population frequency is centered on changes of subset representation between tumor-draining and corresponding non-draining LNs, and thus these potential variables resulting from sample preparation methodology are internally controlled. We are unfortunately unable to provide comprehensive IHC to corroborate the spatial representation of these markers in our dataset, but IHC of LN tissues exploring many of the above-mentioned markers has been performed in the referenced publications. Regarding the specific example of MRC vs BRC, the primary distinction between these clusters was a lack of madcam1 and tnfsf11 on the subset we identify as “BRCs” relative to MRCs. Both clusters expressed cxcl13 and exhibited low expression of markers typically associated with TRC (including ccl19 / ccl21 ). (Major) Reviewer Comment 2: Similarly, no tables are provided as supplement to list the DE genes in the various settings as only part of them are mentioned in the figure 2, and they are limited to the ones showing an increase with none labeled showing a decrease in a live tumor cell draining LN (vs the nondraining LN). This reviewer can imagine that labeling also part of the downregulated genes may render the data and its discussion more complex but then at least tables containing these data should be provided to allow an interested reader to look into them without need to reanalyze all the data. They may contain data relevant for the main question of the paper; eg. how does the LN suppress anti-tumor immunity or get prepared for tumor metastasis. Please improve also the description in the legend so that one understands why the log2 fold changes is negative for transcripts enriched in drLN. Response: Tables including the complete DEG lists of the indicated subpopulations in tdLN vs ndLN have been included in Supplementary table 3 of the revised manuscript. Regarding log₂ fold change interpretation: In volcano plots (B, D, F), negative log₂ fold change values indicate genes that are more highly expressed in draining lymph nodes (dLNs) relative to non-draining lymph nodes (ndLNs). This is because differential expression was calculated using ndLNs as the reference group. As a result, transcripts upregulated in dLNs are represented on the left side of the plot (negative fold change), while genes enriched in ndLNs appear on the right side (positive fold change). (Major) Reviewer Comment 3: The draining LN response is investigated on day 12 after tumor cell inoculation, comparing live tumor cell vs fixed tumor cell injection which is an elegant approach. As the same number of cells is injected in the setting of live cells as of fixed cells, but the final number of live tumor cells is not stated for d12 (but is presumably much higher, by a factor of several fold), the authors should probably acknowledge in the discussion the large difference in final tumor cell material in the two settings that is likely to contribute to the transcriptional differences observed, besides larger differences in the tissue cells of the primary injection site during the time period upto d12. Thus, the difference in factors infusing the LN is not only due to the live tumor cells but also due to the difference in final tumor cell numbers and their differential effects on the peripheral tissue in the two settings. Response: The reviewer is correct, in that mice inoculated with “live” tumor cell will invariably harbor more tumor cells (and thus more overall tumor antigen) at the time of harvesting the associated LN due to continued tumor growth. This is unfortunately difficult to control for experimentally. We have noted this concern as a potential caveat in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 4: It is unclear whether the authors verified if any of the live tumor injected mice showed tumor cell metastasis to the draining LNs which got analyzed. That could have been analyzed by flow cytometry (CD45- cells with specific FSC/SSC characteristics; or a non-stated epithelial marker) or by other means. This information would be valuable to know given the likely difference between premetastatic vs metastatic LNs stroma. Response: Overt metastatic tumor growth was not observed in any live tumor-bearing mice, nor were any clusters identified as cancer cells within the total 50,381 cells sequenced in this dataset. However, because these samples were enriched for stromal populations, it is likely that cancer cells (if present) would be lost following sample enrichment, particularly if originally present in low numbers. As the reviewer suggested, we looked further into our (unenriched) flow cytometry data of dissociated TdLN and ndLNs to identify presence of tumor cells. While no epithelial marker is included in this panel, MC38 cancer cells can be identified as CD45-, PDPN-, CD31-, PD-L1+ (PMID: 28507803). Indeed we do find small numbers of these cells specifically in the tumor-draining lymph node. Average cell counts were 114 total cells in TdLN compared to 4 in NdLN (p value = 0.0085). This analysis has been added to the revised manuscript as supplementary figure 3. We believe that presence of cancer cells at such low numbers, may reflect either shed/migrating tumor cells or the presence of early micrometastases. Discussion of this topic is likewise added to the revised manuscript, and the above-mentioned flow cytometry analysis has been included as Supplementary Fig. 3. (Minor) Reviewer Comment 1: The results start with a rather lengthy intro and part of it could be incorporated into the introduction where some of these points are already raised. Response: Per the reviewer’s suggestion, we have removed the first paragraph of the results. This paragraph reiterated the role of tissue stroma in tumor progression and was mostly redundant. (Minor) Reviewer Comment 2: BRC are claimed to populate the interfollicular space; please explain the rational or the publication stating this. Response: While the exact subsetting criteria and nomenclature varies across different publications to date, the population we refer to as “BRCs” most closely aligns with interfollicular reticular cell populations described as Ccl19lo, Ch25h+, Cxcl13+ in Pikor et al. (PMID: 32424359) and Rodda et al. (PMID: 29752062). This population (or a proportion of this population) is considered to reside in the interfollicular space due to IHC and RNAscope data included in those respective publications. Regarding the nomenclature, this population of reticular cells is considered a subset of “B cell interacting” reticular cells in Pikor et al. Whereas it is referred to as a subset of TRC in Rodda et al. We use the BRC nomenclature due to low Ccl19 and high Cxcl13 expression, suggestive of a B-cell association. (Minor) Reviewer Comment 3: Enzymatic digestion (‘as previously described’): please add reference with a more detailed protocol as this aspect is key for appropriate reproduction. Response: Reference added to revised manuscript (Minor) Reviewer Comment 4: Mention at beginning that MC38 are a colorectal carcinoma cell line. Response: mention of MC38 as colorectal carcinoma cells has been added to the revised manuscript. (Minor) Reviewer Comment 5: In the conduit section: conduits are not simply fibers (although they appear that way histologically and contain fibers) but have also basement membranes thereby forming true tubes/channels; thus the term fiber is not ideal. Similar, the term ‘transendothelial channels’ is not appropriate even when talking of the SCS where occasionally such channels traversing the SCS can be observed but the majority of conduits do not seem to traverse the endothelium. Response: We have changed the description of LN conduits as “a branched network of fibers” to state instead “a branched network of fibrous structures”. While our discussion of conduit networks does not fully address the intricacies of conduit anatomy, we have included relevant references to more in-depth exploration of this topic. Regarding the statement of transendothelial channels, we discuss the nature of these structures as described in the referenced citation (Ranktakari et al 2015). We do not make any statement speculating whether any conduits themselves traverse the endothelium. Rather, we discuss the findings of the referenced study which explore the idea of size-restriction as a function of PLVAP fibrils that exist within transendothelial channels. To clarify, in this publication, “transendothelial channels” and conduits are separate structures. The former spans the length of sinus-lining endothelial cells, while the latter underlies the endothelium (as such this finding is not reliant on whether any or all conduits actually traverse the endothelium). This publication suggests that molecules pass from LN sinus into the LN parenchyma through transendothelial channels, and upon traversing the endothelial lining, can subsequently access the underlying conduit network freely. However, the structure of the transendothelial channel (dictated by PLVAP) is the gating feature for larger molecules. We reference this finding because any structural disruption to the sinus-lining endothelium could theoretically perturb the above described regulation of molecule sizes that are allowed to pass into the conduit network. This would be one potential explanation to the loss of size-exclusivity noted by Riedel et al within the tdLN. (Minor) Reviewer Comment 6: ‘Exhibited of’ (please drop ‘of’). Response: Typo has been corrected. (Minor) Reviewer Comment 7: IL33 discussion: may add another reference showing role of IL33 for memory T cell response (Marx et al.,2024 [Ref 1]). Response: This reference has be added. Thank you for the suggestion (Minor) Reviewer Comment 8: Please spell out LDHA and PKM at first mentioning and possibly the reaction they catalyze to inform the non-specialist reader. Response: Definitions have been added. (Minor) Reviewer Comment 9: Fig.1B and D: I assume these Umaps show data compiled from all 4 groups of mice (please state clearly in legend); it would be of interest to display the Umaps of each group separately (in a supplementary figure) for readers to understand the stromal cell clustering in a 2D space for the 4 groups. Response: Per the reviewer’s suggestion, we have generated a new supplementary (Supplementary figure 2), which exhibits the distribution of cells from each treatment group within the corresponding endothelial (Supplementary Fig 2A) and fibroblast (Supplementary Fig 2B) Umaps. Visualization of cell clustering in this 2D space further reflects the increased representation of specific stromal subclusters (including fLEC, MRC, and PRC) from the tumor-draining LN. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 27 Mar 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 3 (revision) 07 Oct 25 read Version 2 (revision) 10 Apr 25 read read Version 1 27 Mar 24 read read Sanjiv Luther , University of Lausanne, Lausanne, Switzerland Christopher McGinnis , Stanford University, Gladstone-UCSF Institute of Genomic Immunology, Parker Institute for Cancer Immunotherapy, San Francisco, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Luther S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 22 Oct 2025 | for Version 3 Sanjiv Luther , Department of Immunobiology, University of Lausanne, Lausanne, Switzerland 0 Views copyright © 2025 Luther S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I am satisfied with the changes made. I have no further comments. Competing Interests No competing interests were disclosed. Reviewer Expertise lymph nodes, stromal cells, immunity, scRNAseq I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Luther S. Peer Review Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.188508.r421293) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-223/v3#referee-response-421293 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Luther S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 May 2025 | for Version 2 Sanjiv Luther , Department of Immunobiology, University of Lausanne, Lausanne, Switzerland 0 Views copyright © 2025 Luther S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have made major changes in response to my comments by adding supplementary figures and tables that help a lot in reading and understanding the manuscript. The new caveat section is also helpful given that this study is limited to the scRNAseq datasets described. Minor points: Typo in figure 1 E legend: colored by cluster identity ? or a function ‘cluster identify’ and put in italic? Supplementary figure 2 legend: say ‘pooled’ fibroblast or ‘pooled’ endothelial UMAP to be clearer? Supplementary table 3 legend: correct ‘figure 2B,D,F’ Page 9: …chemotactic gradients of CCL19: the authors of cited ref 67 showed nothing with CCL19 but probably another paper should have been cited instead showing an ACKR4 role in DC trafficking from the SCS into the T zone due to CCL21 gradient shaping (Ulvmar, Immunity 2014). That paper did not show CCL19 gradient shaping by ACKR4 in vivo but provided only in vitro evidence; please adapt the claim accordingly. Reply to major comment 1: Madcam1 transcript expression is being used as marker to identify MRC / FDC versus Madcam1-negative BRC, with reference to previous studies looking at LNs in homeostasis or viral infection (refs 33 and 34). While ref 33 shows that Madcam1 transcript levels are a poorly reliable subset marker, ref 34 indicates Madcam1 expression being also present in non-MRC/FDC, although at lower levels than in the latter. In the current study, the authors look at LN stroma in a different setting (tumor drainage and antitumor immunity) not previously described by scRNAseq, with Madcam1 being potentially upregulated upon stromal cell activation (by signals like LTab, similar to LN development). Thus, BRC other than MRC/FDC may express or even upregulate Madcam1 in this setting and thus be misclassified in the current study. The use of few and variable subset markers like Madcam1 is a caveat of the present study given that the authors do not provide data to confirm their claims at the protein and histological level. I recommend that the authors include this point in the caveat section of this manuscript. Page 11 (and reply to major comment 4): Tumor cells were indirectly identified among Pdpn-CD31- cells as being PDL1+. This seems a very vague identification that may be considerably contaminated by various Pdpn-CD31- fibroblast subsets that are likely to upregulate PDL1 upon IFN sensing. Thus, this supplementary figure 3 along with the text should only be shown if the authors have solid evidence for these cells being strongly enriched in metastatic tumor cells. Please verify validity of accession number for the identifiers.org database. Competing Interests No competing interests were disclosed. Reviewer Expertise lymph nodes, stromal cells, immunity, scRNAseq I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 27 Sep 2025 Jonathan Chang, Oncology Translational Research, Novartis, Cambridge, 02139, USA Response to Reviewer's comments Below: Reviewer Comment 1: Typo in figure 1 E legend: colored by cluster identity ? or a function ‘cluster identify’ and put in italic? Response : Typo corrected Reviewer Comment 2: Supplementary figure 2 legend: say ‘pooled’ fibroblast or ‘pooled’ endothelial UMAP to be clearer? Response : Changed according to reviewer’s suggestion. Reviewer Comment 3: Supplementary table 3 legend: correct ‘figure 2B,D,F’. Response : legend has been corrected Reviewer Comment 4: Page 9: …chemotactic gradients of CCL19: the authors of cited ref 67 showed nothing with CCL19 but probably another paper should have been cited instead showing an ACKR4 role in DC trafficking from the SCS into the T zone due to CCL21 gradient shaping (Ulvmar, Immunity 2014). That paper did not show CCL19 gradient shaping by ACKR4 in vivo but provided only in vitro evidence; please adapt the claim accordingly. Response : Thank you for your suggestion – we believe the currently cited paper from Ulvmar et al (Nat Imm 2014) is still appropriate for the intended claim. In the cited paper, the authors demonstrate 1) Ccl21 gradient across the SCS is shaped by cLEC expression of ACKR4 (aka CCLR1 ), 2) loss of ACKR4 impacts DC emigration to the LN from the lymphatics in vivo , and 3) ACKR4 could create functional gradients of Ccl19 in vitro. You are correct that they did not specifically demonstrate Ccl19 gradients across the SCS in vivo , and this is largely because Ccl19 is soluble and cannot be readily imaged in tissues. However, because ACKR4 scavenges both Ccl19/Ccl21, and both chemokines are similarly expressed in the SCS, the authors speculate that ACKR4 influences the patterning of both chemokines. They state: “Thus, we can only speculate that CCL19, produced at the same sites as CCL21, as well as potentially in the SCS itself, might be 'patterned' by CCRL1 on cLECs to form gradients similar to those of CCL21.” Given the lack of explicit in vivo evidence regarding Ccl19 patterning, however, we will have slightly amended our original statement to reference only Ccl21 patterning. The statement in question now reads “Analogous chemotactic gradients of Ccl21 across the SCS has been experimentally demonstrated as critical for the recruitment of migrating dendritic cells into the LN.” Reviewer Comment 5: (Reply to major comment 1): Madcam1 transcript expression is being used as marker to identify MRC / FDC versus Madcam1-negative BRC, with reference to previous studies looking at LNs in homeostasis or viral infection (refs 33 and 34). While ref 33 shows that Madcam1 transcript levels are a poorly reliable subset marker, ref 34 indicates Madcam1 expression being also present in non-MRC/FDC, although at lower levels than in the latter. In the current study, the authors look at LN stroma in a different setting (tumor drainage and antitumor immunity) not previously described by scRNAseq, with Madcam1 being potentially upregulated upon stromal cell activation (by signals like LTab, similar to LN development). Thus, BRC other than MRC/FDC may express or even upregulate Madcam1 in this setting and thus be misclassified in the current study. The use of few and variable subset markers like Madcam1 is a caveat of the present study given that the authors do not provide data to confirm their claims at the protein and histological level. I recommend that the authors include this point in the caveat section of this manuscript. Response : We appreciate the careful consideration of Madcam1 expression, and we agree that we cannot rule out the possibility of tumor-derived signals eliciting upregulation or downregulation of cluster-identification markers (whether Madcam1 or any other marker). As you note, there is some discrepancy between references 33 and 34 (Pikor et al and Rodda et al ) with regard to Madcam1 expression. Notably, Pikor et al (Nat Imm 2020) acknowledged the discrepancy in Madcam1 expression with that seen by Rodda et al (2018), and mentioned that their dataset resolved two tnfsf11+ subsets – one of which is Madcam1 -positive, and the other which is Madcam1 -negative. It is therefore also possible that the difference between these two referenced papers relates to the resolution of clustering. However, we would also like to emphasize that beyond Madcam1 expression, specific expression of tnfsf11 and elevated enpp2 were also used as markers to help distinguish the MRC cluster. Nevertheless, we do acknowledge that this study includes only transcriptomic data and relies on previously published studies to interpret subset identification. And as noted by the reviewer, these previously published studies do not include an investigation of cluster identification within the context of a tumor-draining lymph node – as such we have amended the manuscript to include mention of this concern within the “Caveats and study limitations” section. Reviewer Comment 6: Page 11 (and reply to major comment 4): Tumor cells were indirectly identified among Pdpn-CD31- cells as being PDL1+. This seems a very vague identification that may be considerably contaminated by various Pdpn-CD31- fibroblast subsets that are likely to upregulate PDL1 upon IFN sensing. Thus, this supplementary figure 3 along with the text should only be shown if the authors have solid evidence for these cells being strongly enriched in metastatic tumor cells. Response : The reviewer is correct that we cannot rule out the possibility of this population (PDPN-CD31-PDL1+) containing non-tumor cells. In the amended manuscript, we have included mention of the possibility of PDPN-CD31- stromal cells upregulating PD-L1. This supplementary figure was included in response to the reviewer’s previous question about the possibility of active metastatic growth in the tdLN. As noted in our previous response, this population amounted to an average of just 114 total cells in the tdLN. Whether this PDPN-CD31-PDL1+ population purely consists of mestatic tumor cells, or additionally contains activated DN stromal cells that have upregulated PD-L1, we feel that the conclusion should still be that there are very few (if any) tumor cells within the LN at time of collection. Reviewer Comment 7: Please verify validity of accession number for the identifiers.org database . Response : The accession number is correct. However there appears to have been a space incorrectly placed within the URL. The correct URL is http://identifiers.org/geo:GSE248905 and this has been updated in the revised manuscript. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Luther S. Peer Review Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.178762.r377193) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-223/v2#referee-response-377193 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 McGinnis C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 Apr 2025 | for Version 2 Christopher McGinnis , Department of Pathology, Stanford University, Gladstone-UCSF Institute of Genomic Immunology, Parker Institute for Cancer Immunotherapy, San Francisco, USA 0 Views copyright © 2025 McGinnis C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors satisfactorily addressed all of my major and minor comments (with the exclusion of the handling of cell-cell doublets, but this does not detract from the overall claims of the manuscript). Competing Interests No competing interests were disclosed. Reviewer Expertise Cancer immunology, metastasis biology, single-cell genomics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) McGinnis C. Peer Review Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.178762.r377192) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-223/v2#referee-response-377192 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 McGinnis C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 Sep 2024 | for Version 1 Christopher McGinnis , Department of Pathology, Stanford University, Gladstone-UCSF Institute of Genomic Immunology, Parker Institute for Cancer Immunotherapy, San Francisco, USA 0 Views copyright © 2024 McGinnis C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In this study by Piquet et al, the Authors use single-cell RNA-sequencing (scRNA-seq) to compare lymph node (LN) stromal cell transcriptional profiles in mice bearing live and fixed tumors to disentangle changes in LN stromal cells related to pro-metastatic remodeling mechanisms from changes linked to antigenic challenge. The authors make the following claims: Development of the LN pre-metastatic niche involves changes in the proportions and gene expression profiles of specific subtypes of fibroblasts and endothelial cells comprising the subcapsular sinus, in addition to other LN cell populations (e.g., b-zone reticular cells and perivascular cells). MRCs in the live tdLN are induced into a desmoplastic CAF-like state associated with upregulation of ECM components and remodeling genes such as fibronectin and Mmp2/9. Authors speculate that tumor-reprogrammed MRCs contribute to altered regulation of LN conduit access. fLECs increase expression of pro-inflammatory cytokines (e.g., Ccl20 and Cxcl9/10) in tdLNs, raising the question of whether TILs/NK cells or Cxcr3+ metastatic tumor cells (or both) are preferentially recruited to the subcapsular sinus. MRCs and fLECs enact a coordinate gene expression program in the live tdLN associated with increased Il33 and aerobic glycolysis genes, providing new insights into stromal cell signaling and metabolism mechanisms in the LN metastatic niche. Major Comments Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The MC38 tumor model is not thought to robustly metastasize (e.g., only ~50% of orthotopically-transplanted tumors form LN metastases; Greenlee & King, 2022), especially after subcutaneous injection. Thus, it is unclear whether the signatures described in the paper truly represent the pre-metastatic niche LN. Without adequately addressing this concern, the authors need to recontextualize their observations to focus on the effects of primary tumor-mediated reprogramming on the tdLN rather than the pre-metastatic niche. “Within the resultant dataset, we identified 7 distinct clusters of fibroblastic cells and 4 clusters of lymphatic endothelial cells, each of which was annotated based on well-established identity markers” – Authors should include either reference to which markers were used or, better yet, a heatmap or dotplot showing expression levels for marker genes in each annotated cell type. “Perivascular cells (PRCs), though not localized to the SCS, likewise exhibited significant transcriptomic changes, which may reflect support of vascular expansion” – While entirely possible, it is unclear how the observed increase in the numbers of DEGs suggests support of vascular expansion. Authors should analyze more deeply the genes that are differentially expressed (e.g., GSEA, targeted analysis of known proangiogenic factors expressed by PRCs, etc.) before making this claim. The Authors make many claims throughout the manuscript about proliferative expansion of certain fibroblast and endothelial cell subsets in tumor-draining lymph nodes, but do not show any direct evidence of this increased proliferation. Since annotating proliferative cells via Mki67 and Hells expression in scRNA-seq data is possible, I recommend the Authors compare the proportions of proliferative subsets in all experimental groups to test/strengthen these claims. The Authors do not describe how they handled the removal of doublets during scRNA-seq analysis. The presence of doublets could significantly confound data interpretation – particularly in instances where the underlying cell type distributions between samples are known to differ, as in this case – and, thus, needs to be addressed to ensure the observations are not artefactual in nature. In the Methods section, the Authors state that “The present study used tissues collected from naïve mice (n=4), mice bearing live MC38 tumors (n=3), or fixed MC38 tumor cells (n=3)” and then “No animals were excluded from analysis.” However, the described scRNA-seq analyses do not incorporate insights gained from the naïve mouse samples. Comparing naïve and fixed tumor samples could provide key insight for distinguishing the effects of live tumors on the tdLN. Moreover, including comparisons between naïve and fixed tumor samples would be critical for pinpointing the observed effects that are specifically due to antigenic challenge. Alternatively, if naïve samples were not used, the Methods section should be edited to clarify this point. In Fig. 2G, the authors show genes that are specifically enriched in tdLN MRCs and fLECs. How were these genes identified? Is Il33 expression between tdLNs isolated from mice harboring live and fixed tumors statistically-significantly different? The up-regulation between tdLN and ndLN is clear in both live and fixed tumor settings, but tdLNs in fixed tumor samples also increase expression of Il33 compared to ndLNs, suggesting that this observation may not be live tumor-specific. Minor Comments Authors state “In the pre-metastatic lymph node, expansion of both endothelium and FRCs has been documented” – true, but authors should cite appropriate literature references. How did authors verify that MC38 tumor cells were successfully fixed after 4% PFA exposure? Fig. 1A: Authors should clarify in the legend the reference point they used to calculate relative cell count. Imagining it was the average of the non-draining LN, but should be explicit to avoid confusion. Authors should also clarify how each cell type was identified using their flow cytometry panel. Fig. 1B and E: Authors do not ever define some of the acronyms used here (e.g., TRC, FDC) Page 7: “In this context, Increased conduit thickness…” – ‘Increased’ should not be capitalized. Fig. 2B/E/F – authors should be explicit about what the volcano plot colors scheme to ensure clarity of interpretation. Also, are the p-values presented raw or adjusted? As far as I can tell, the Authors use increased expression of fibronectin by MRCs in the tdLN to support the claim that they are induced into a desmoplastic CAF-like state. This may indeed be the case, but more thorough analyses/discussions are needed to sufficiently support this claim. I would suggest leveraging publicly-available scRNA-seq data of desmoplastic CAFs (if such data exists) to assess similarly in transcriptional signatures. Alternatively, the language can be edited to lessen the claimed connection. “Indeed, genetic mouse models of homeostatic chemokine disruption exhibit impaired immunity.” Need reference. Should correctly note gene/protein identifiers (e.g., italics for gene names, proteins capitalized, etc.). The 30% mitochondrial gene expression threshold is quite high. Authors should interrogate whether any high pMito clusters were retained in analyses that could confound interpretation. On balance, I believe that the presented work represents a useful advance for the field, and I recommend its indexing, assuming that the concerns addressed above are satisfactorily addressed. Moreover, I would also like to specifically note that the manuscript is extremely well-written. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Cancer immunology, metastasis biology, single-cell genomics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Apr 2025 Jonathan Chang, Oncology Translational Research, Novartis, Cambridge, 02139, USA (Major) Reviewer Comment 1: Do the authors have evidence showing that mice harboring MC38 tumors actually form LN metastases? Or at least what proportion of the mice form mets? The MC38 tumor model is not thought to robustly metastasize (e.g., only ~50% of orthotopically-transplanted tumors form LN metastases; Greenlee & King, 2022), especially after subcutaneous injection. Thus, it is unclear whether the signatures described in the paper truly represent the pre-metastatic niche LN. Without adequately addressing this concern, the authors need to recontextualize their observations to focus on the effects of primary tumor-mediated reprogramming on the tdLN rather than the pre-metastatic niche. Response: The reviewer raises a salient point in that subcutaneously implanted tumor models rarely in fact metastasize (at least within the timeframe at which the primary tumor remains within ethical growth limits). Orthotopically implanted tumors more readily metastasize, perhaps due to the native regional tissue environment of implantation. However, such models are often technically challenging and highly variable in terms of tumor growth kinetics. We opted to use subcutaneous MC38 tumors to minimize technical and experimental variance which might otherwise obscure the identification of transcriptional signatures influenced by tumor-derived factors. We feel the nature of multiple stromal alterations highlighted in this paper align well with previously proposed microenvironmental dependencies for tumor growth and metastasis highlighted within the broader literature, and it is for this reason that we contextualize these changes as “pre-metastatic”. However, we fully recognize that data herein do not constitute experimental proof that any or all of these transcriptional changes are in fact necessary for metastasis but rather offer these results as a resource for more definitive studies to follow. We agree that such experimental proof would necessitate the use of models for which lymph node metastasis can be explicitly documented following experimental perturbation. Such an effort is currently beyond the scope of this limited study. However, we do acknowledge that this is a critically important limitation, and discussion of this point has been included in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 2: “Within the resultant dataset, we identified 7 distinct clusters of fibroblastic cells and 4 clusters of lymphatic endothelial cells, each of which was annotated based on well-established identity markers” – Authors should include either reference to which markers were used or, better yet, a heatmap or dotplot showing expression levels for marker genes in each annotated cell type. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supplementary Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. (Major) Reviewer Comment 3: “Perivascular cells (PRCs), though not localized to the SCS, likewise exhibited significant transcriptomic changes, which may reflect support of vascular expansion” – While entirely possible, it is unclear how the observed increase in the numbers of DEGs suggests support of vascular expansion. Authors should analyze more deeply the genes that are differentially expressed (e.g., GSEA, targeted analysis of known proangiogenic factors expressed by PRCs, etc.) before making this claim. Response: This was a speculative comment due to the association of PRCs with LN vasculature, and the observed increase in BEC proliferation (fig1A). We have tempered this statement in the revised manuscript and included references to studies outlining potential function of PRCs. Importantly, we have also revised our discussion of PRC, in which we describe them as “not localized to the SCS”. Further review of the findings by Rodda et al. 2018 in which they describe localization of CD34+ stromal cells not only in the perivascular compartment, but also within the LN capsule. Transcriptional features identifying our PRC subset align with these CD34 SCs described by Rodda et al. and Sitnik et al. 2016. We have additionally included the full DEG list of PRCs in tdLN vs ndLN in the newly added Supplementary table 3. (Major) Reviewer Comment 4: The Authors make many claims throughout the manuscript about proliferative expansion of certain fibroblast and endothelial cell subsets in tumor-draining lymph nodes, but do not show any direct evidence of this increased proliferation. Since annotating proliferative cells via Mki67 and Hells expression in scRNA-seq data is possible, I recommend the Authors compare the proportions of proliferative subsets in all experimental groups to test/strengthen these claims. Response: Increased cell counts of each major stromal cell population (fibroblast, LEC, and BEC) within the dLN vs ndLN (seen in Fig1A) provides us some direct evidence of proliferative expansion of each of these broader populations. Within each stromal cell type, we then identify changes in the representation of subsets within in the scRNAseq dataset. For instance, “Fibroblasts” are presumed to have proliferated due to the >2 fold increase in cell numbers relative to matched non-draining LN (fig1A). Then, within all fibroblasts, there is then a higher proportion of MRC, PRC, and BRC in the dLN relative to the ndLN (Fig 1C), a differential which is not present in the control conditions of mice inoculated with fixed tumor cells (Fig 1D). This differential in subset representation is also visually evident in the newly added Supplementary figure 2. Unfortunately we do not have the capacity to measure absolute cell counts of individual subsets, and thus proliferative expansion is inferred from the combination of these above observations. Additionally, ki67 + cells are indeed present in our dataset, but constitute a small proportion of every cell population present. In our experience, ki67 has rarely been a reliable or quantitative measure of population expansion in tissues. (Major) Reviewer Comment 5: The Authors do not describe how they handled the removal of doublets during scRNA-seq analysis. The presence of doublets could significantly confound data interpretation – particularly in instances where the underlying cell type distributions between samples are known to differ, as in this case – and, thus, needs to be addressed to ensure the observations are not artefactual in nature. Response: We carefully assessed the quality control metrics of the dataset, including the number of RNA molecules (UMIs) and the number of genes detected per cell. All values fell within the expected range for single cells, as defined in Seurat’s documentation, and we did not observe any anomalies indicative of doublets. Based on these observations, we do not suspect the presence of doublets in our dataset at a level that would confound the interpretation of our results. This information has been added to the methods description in the revised manuscript. (Major) Reviewer Comment 6: In the Methods section, the Authors state that “The present study used tissues collected from naïve mice (n=4), mice bearing live MC38 tumors (n=3), or fixed MC38 tumor cells (n=3)” and then “No animals were excluded from analysis.” However, the described scRNA-seq analyses do not incorporate insights gained from the naïve mouse samples. Comparing naïve and fixed tumor samples could provide key insight for distinguishing the effects of live tumors on the tdLN. Moreover, including comparisons between naïve and fixed tumor samples would be critical for pinpointing the observed effects that are specifically due to antigenic challenge. Alternatively, if naïve samples were not used, the Methods section should be edited to clarify this point. Response: We appreciate the careful review and thoughtful suggestion. However naïve samples were not included in this assessment. The methods section was corrected to reflect the proper cohort used in this analysis. (Major) Reviewer Comment 7: In Fig. 2G, the authors show genes that are specifically enriched in tdLN MRCs and fLECs. How were these genes identified? Is Il33 expression between tdLNs isolated from mice harboring live and fixed tumors statistically-significantly different? The up-regulation between tdLN and ndLN is clear in both live and fixed tumor settings, but tdLNs in fixed tumor samples also increase expression of Il33 compared to ndLNs, suggesting that this observation may not be live tumor-specific. Response: The genes in Fig. 2G were identified by performing DEG analysis across dLN and ndLN of the live and fixed tumor condition. Genes which were commonly upregulated in both the MRC and fLEC of specifically the live tdLN subset were graphed in the bubble plot. The mean expression of IL33 in the dLN of mice receiving fixed tumor cells is overall slightly elevated over the corresponding ndLN, but did not reach statistical significance. A statistically significant change in expression was only seen in the dLN vs ndLN of live tumor-bearing mice. (Minor) Reviewer Comment 1: Authors state “In the pre-metastatic lymph node, expansion of both endothelium and FRCs has been documented” – true, but authors should cite appropriate literature references. Response: Citation has been added. (Minor) Reviewer Comment 2: How did authors verify that MC38 tumor cells were successfully fixed after 4% PFA exposure? Response: No tumor growth was observed in mice receiving fixed tumor cells at the experimental endpoint. (Minor) Reviewer Comment 3: Fig. 1A: Authors should clarify in the legend the reference point they used to calculate relative cell count. Imagining it was the average of the non-draining LN, but should be explicit to avoid confusion. Authors should also clarify how each cell type was identified using their flow cytometry panel. Response: Cell counts are presented as relative to the average of corresponding non-draining lymph nodes. Cell populations were identified by flow cytometry with the following markers: Fibroblastic Reticular (CD45-CD31-PDPN+), Lymphatic Endothelial (CD45-, CD31+, PDPN+), and Blood Endothelial (CD45-, CD31+, PDPN-). This information has been added to the figure legend in the revised manuscript. (Minor) Reviewer Comment 4: Fig. 1B and E: Authors do not ever define some of the acronyms used here (e.g., TRC, FDC) Response: The following definitions have been added to the text: Fibroblastic populations include T-zone Reticular Cells (TRC), B zone Reticular cells (BRC), Marginal Zone Reticular Cells (MRC), Follicular Dendritic Cells (FDC), Medulary Reticular cells (MedRC), Perivascular Reticular cells (PRC), and activated stromal cells (Act SC). Endothelial cell subsets include ceiling LECs (cLEC), floor LECs (fLEC), Medulary LECs (MedLEC), and Cortical LECs (CorLEC). (Minor) Reviewer Comment 5: Page 7: “In this context, Increased conduit thickness…” – ‘Increased’ should not be capitalized. Response: Typo has been corrected (Minor) Reviewer Comment 6: Fig. 2B/E/F – authors should be explicit about what the volcano plot colors scheme to ensure clarity of interpretation. Also, are the p-values presented raw or adjusted? Response: The cutoffs for highlighted genes include a log2 fold change of 0.5 or above (horizontal dashed lines) and adjusted p-value above 0.001 (indicated by the vertical dashed lines). This description is included in the figure legend of the revised manuscript. (Minor) Reviewer Comment 7: As far as I can tell, the Authors use increased expression of fibronectin by MRCs in the tdLN to support the claim that they are induced into a desmoplastic CAF-like state. This may indeed be the case, but more thorough analyses/discussions are needed to sufficiently support this claim. I would suggest leveraging publicly-available scRNA-seq data of desmoplastic CAFs (if such data exists) to assess similarly in transcriptional signatures. Alternatively, the language can be edited to lessen the claimed connection. Response: While we do single-out Fn1 in the text, we note that this is one of several genes differentially expressed in MRCs of the tumor dLN that relate to matrix deposition or tissue remodeling. Many others are additionally labeled in figure 2b. Additionally, both ECM organization and wound-healing are prominent in the pathway analysis as shown in Fig2a. For additional clarity, we have also included the full DEG list of MRC, BRC, fLEC, and PRC populations in the tdLN vs ndLN in Supplementary table 3 of the revised manuscript. We feel comfortable drawing the comparison to desmoplastic CAFs given that these indicators of altered matrix/remodeling arise within the context of the tumor-draining LN. However, to soften the claim, we have changed the wording of the section title from “reminiscent of desmoplastic CAFs” to instead read “reminiscent of the desmoplastic nature of CAFs”, as we acknowledge that ECM production/tissue remodeling are only one facet of CAF function. We do not want to make the claim that the transcriptional changes we see indicate that MRCs have become CAFs, but rather that they have taken on a key function typically associated with CAFs. (Minor) Reviewer Comment 8: “Indeed, genetic mouse models of homeostatic chemokine disruption exhibit impaired immunity.” Need reference. Response: Relevant references have been added. (Minor) Reviewer Comment 9: Should correctly note gene/protein identifiers (e.g., italics for gene names, proteins capitalized, etc.). Response: We have reformatted gene/protein names to match convention. (Minor) Reviewer Comment 10: The 30% mitochondrial gene expression threshold is quite high. Authors should interrogate whether any high pMito clusters were retained in analyses that could confound interpretation. Response: We carefully examined the distribution of mitochondrial gene expression across cells and found no evidence of high mitochondrial clusters that would indicate stressed or damaged cells. Additionally, downstream analyses, such as clustering and differential expression, did not reveal any confounding effects that could be attributed to mitochondrial gene expression. Based on these observations, we are confident that the retained cells are biologically relevant and do not introduce bias into our results. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern McGinnis C. Peer Review Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.159080.r296538) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-223/v1#referee-response-296538 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Luther S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Aug 2024 | for Version 1 Sanjiv Luther , Department of Immunobiology, University of Lausanne, Lausanne, Switzerland 0 Views copyright © 2024 Luther S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Piquet and coauthors address in this study the interesting question of how the lymph node (LN) stroma changes or is reprorammed upon draining a tumor site with alterations presumably contributing to tumor metastasis to this site along with immune suppression. They present insightful scRNAseq datasets describing how different murine LN stromal cell types react to either a live or a fixed inoculation with the MC38 colorectal cancer cell line, when compared to a nondraining LN. While both types of inoculation lead to a comparable LN swelling, including a strong amplification of the LN fibroblasts, lymphatic endothelium (LEC) and blood endothelium (BEC), consistent with a strong inflammatory response in both settings, the impact of live tumor cell inoculation shifted much more the proportions and transcriptional profile of distinct subsets of stromal cells compared to injection with fixed tumor cells, pointing to tumor-specific factors driving these changes. The transcriptional shifts were most pronounced among two fibroblast subsets, termed MRC and BRC, as well as among two LEC subsets that are also those thought to be most exposed to the incoming lymph and its factors. The resource provided by the authors should be of great interest to all scientists interested in tumor metastasis or anti-tumor immunity. The manuscript is well written, the data are of high quality and appropriately displayed; and the findings are discussed in an insightful way. Major points: 1) The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed genes for each subset, so that one can judge the cells clustered for example as MRC or BRC. Currently one would be forced to redo all the bioinformatic work again, based on the individual data files provided. Such additional data would help considerably the expert reader to assess and interpret the data and the labeling of the clusters, especially as the frequencies of the FDC and MRC populations look higher compared to other scRNAseq data sets (eg. Rodda/Cyster Immunity 2018) or relative to the analysis by flow cytometry (eg. Huang/Luther, PNAS 2018), also relative to TRC and MedRC. Any histological stainings (with antibodies or by ISH) validating the key scRNAseq data, including the subset identification/clustering would greatly enhance the value of the data; eg. are the MRC-like cells really still restricted to the SCS-lining area, as are the fLEC-phenotype cells 2) Similarly, no tables are provided as supplement to list the DE genes in the various settings as only part of them are mentioned in the figure 2, and they are limited to the ones showing an increase with none labeled showing a decrease in a live tumor cell draining LN (vs the nondraining LN). This reviewer can imagine that labeling also part of the downregulated genes may render the data and its discussion more complex but then at least tables containing these data should be provided to allow an interested reader to look into them without need to reanalyze all the data. They may contain data relevant for the main question of the paper; eg. how does the LN suppress anti-tumor immunity or get prepared for tumor metastasis. Please improve also the description in the legend so that one understands why the log2 fold changes is negative for transcripts enriched in drLN. 3) The draining LN response is investigated on day 12 after tumor cell inoculation, comparing live tumor cell vs fixed tumor cell injection which is an elegant approach. As the same number of cells is injected in the setting of live cells as of fixed cells, but the final number of live tumor cells is not stated for d12 (but is presumably much higher, by a factor of several fold), the authors should probably acknowledge in the discussion the large difference in final tumor cell material in the two settings that is likely to contribute to the transcriptional differences observed, besides larger differences in the tissue cells of the primary injection site during the time period upto d12. Thus, the difference in factors infusing the LN is not only due to the live tumor cells but also due to the difference in final tumor cell numbers and their differential effects on the peripheral tissue in the two settings. 4) It is unclear whether the authors verified if any of the live tumor injected mice showed tumor cell metastasis to the draining LNs which got analyzed. That could have been analyzed by flow cytometry (CD45- cells with specific FSC/SSC characteristics; or a non-stated epithelial marker) or by other means. This information would be valuable to know given the likely difference between premetastatic vs metastatic LNs stroma. Minor points: The results start with a rather lengthy intro and part of it could be incorporated into the introduction where some of these points are already raised. BRC are claimed to populate the interfollicular space; please explain the rational or the publication stating this. Enzymatic digestion (‘as previously described’): please add reference with a more detailed protocol as this aspect is key for appropriate reproduction Mention at beginning that MC38 are a colorectal carcinoma cell line In the conduit section: conduits are not simply fibers (although they appear that way histologically and contain fibers) but have also basement membranes thereby forming true tubes/channels; thus the term fiber is not ideal. Similar, the term ‘transendothelial channels’ is not appropriate even when talking of the SCS where occasionally such channels traversing the SCS can be observed but the majority of conduits do not seem to traverse the endothelium. ‘Exhibited of’ (please drop ‘of’) IL33 discussion: may add another reference showing role of IL33 for memory T cell response (Marx et al.,2024 [Ref 1]) Please spell out LDHA and PKM at first mentioning and possibly the reaction they catalyze to inform the non-specialist reader. Fig.1B and D: I assume these Umaps show data compiled from all 4 groups of mice (please state clearly in legend); it would be of interest to display the Umaps of each group separately (in a supplementary figure) for readers to understand the stromal cell clustering in a 2D space for the 4 groups. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Marx AF, Kallert SM, Brunner TM, Villegas JA, et al.: The alarmin interleukin-33 promotes the expansion and preserves the stemness of Tcf-1+ CD8+ T cells in chronic viral infection. Immunity . 2023; 56 (4): 813-828.e10 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise lymph nodes, stromal cells, immunity, scRNAseq I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Apr 2025 Jonathan Chang, Oncology Translational Research, Novartis, Cambridge, 02139, USA (Major) Reviewer Comment 1: The clustering of the different subsets of fibroblasts, or lymphatic or blood endothelial cells is presented without any tables or heatmaps showing the top differentially expressed genes for each subset, so that one can judge the cells clustered for example as MRC or BRC. Currently one would be forced to redo all the bioinformatic work again, based on the individual data files provided. Such additional data would help considerably the expert reader to assess and interpret the data and the labeling of the clusters, especially as the frequencies of the FDC and MRC populations look higher compared to other scRNAseq data sets (eg. Rodda/Cyster Immunity 2018) or relative to the analysis by flow cytometry (eg. Huang/Luther, PNAS 2018), also relative to TRC and MedRC. Any histological stainings (with antibodies or by ISH) validating the key scRNAseq data, including the subset identification/clustering would greatly enhance the value of the data; eg. are the MRC-like cells really still restricted to the SCS-lining area, as are the fLEC-phenotype cells. Response: Cluster identification for our dataset was based on findings from previously published studies that have deeply characterized of stromal subsets (utilizing combined scRNAseq and IHC approaches of dissociated and intact lymph nodes respectively). We aligned expression patterns of the markers validated in these studies with differential expression patterns of our identified clusters to assign subtypes. Notable markers used for cluster identification are noted below, and bubble plots for each fibroblastic or endothelial cluster are now included in Supp Fig 1 of the revised manuscript as well as the methods section (these Key identifiers also listed below). Additionally, DEG lists were included in Supplementary table 1 (for fibroblast subclusters) and Supplementary table 2 (for endothelial subclusters). Lymphatic Endothelial Subsets of the LN were annotated based on characterization work from den Braanker, et al. 2021 (PMID: 34769408) and Fujimoto, et al. 2020 (PMID: 32251437). Key identifiers for each subset include the following: cLEC : ackr4+, cd36+, edn1+, anxa2+ . fLEC : lyve1+, glycam1+, coch+, madcam1+ Medullary LEC : lyve1+, marco+, il33+, itg2b+ Cortical LEC : mrc1+, ptx3+, reln+ . Fibroblastic Subsets of the LN were annotated based on characterization collective work from multiple studies including Rodda, et al. 2018 (PMID: 29752062), Pikor et al. 2021 (PMID: 32424359) and Beuchler et al. 2021 (PMID: 33981032). Key identifiers for each subset include the following: TRC : ccl19+, ccl21+, grem1+ BRC : cxcl13+, cr2-, madcam1-, tnfsf11-, ch25h+ FDC : cxcl13+, madcam1+, coch+, cr2+, tmem119+, pthlh+ MRC : madcam1+, tnfsf11+, enpp2+ MedRC : cxcl12(hi), inmt+, stc1+, bst1- PRC : cd34+, fndc1+, pi16+, dpp4+ Activated SC : nr4a1+. We appreciate the reviewers’ careful consideration of population frequencies within our dataset relative to the above referenced studies. While we are unable to conclusively account for these discrepancies, we hypothesize that differences in frequencies may relate to 1) distinct methodology for tissue dissociation, 2) differences in cell enrichment prior to scRNAseq, and 3) differences in which specific peripheral LNs are included in the dataset. Importantly, we do not make any conclusions or assumptions that are specifically about the baseline abundance of stromal subsets relative to other subsets, but rather all of the comments we make regarding population frequency is centered on changes of subset representation between tumor-draining and corresponding non-draining LNs, and thus these potential variables resulting from sample preparation methodology are internally controlled. We are unfortunately unable to provide comprehensive IHC to corroborate the spatial representation of these markers in our dataset, but IHC of LN tissues exploring many of the above-mentioned markers has been performed in the referenced publications. Regarding the specific example of MRC vs BRC, the primary distinction between these clusters was a lack of madcam1 and tnfsf11 on the subset we identify as “BRCs” relative to MRCs. Both clusters expressed cxcl13 and exhibited low expression of markers typically associated with TRC (including ccl19 / ccl21 ). (Major) Reviewer Comment 2: Similarly, no tables are provided as supplement to list the DE genes in the various settings as only part of them are mentioned in the figure 2, and they are limited to the ones showing an increase with none labeled showing a decrease in a live tumor cell draining LN (vs the nondraining LN). This reviewer can imagine that labeling also part of the downregulated genes may render the data and its discussion more complex but then at least tables containing these data should be provided to allow an interested reader to look into them without need to reanalyze all the data. They may contain data relevant for the main question of the paper; eg. how does the LN suppress anti-tumor immunity or get prepared for tumor metastasis. Please improve also the description in the legend so that one understands why the log2 fold changes is negative for transcripts enriched in drLN. Response: Tables including the complete DEG lists of the indicated subpopulations in tdLN vs ndLN have been included in Supplementary table 3 of the revised manuscript. Regarding log₂ fold change interpretation: In volcano plots (B, D, F), negative log₂ fold change values indicate genes that are more highly expressed in draining lymph nodes (dLNs) relative to non-draining lymph nodes (ndLNs). This is because differential expression was calculated using ndLNs as the reference group. As a result, transcripts upregulated in dLNs are represented on the left side of the plot (negative fold change), while genes enriched in ndLNs appear on the right side (positive fold change). (Major) Reviewer Comment 3: The draining LN response is investigated on day 12 after tumor cell inoculation, comparing live tumor cell vs fixed tumor cell injection which is an elegant approach. As the same number of cells is injected in the setting of live cells as of fixed cells, but the final number of live tumor cells is not stated for d12 (but is presumably much higher, by a factor of several fold), the authors should probably acknowledge in the discussion the large difference in final tumor cell material in the two settings that is likely to contribute to the transcriptional differences observed, besides larger differences in the tissue cells of the primary injection site during the time period upto d12. Thus, the difference in factors infusing the LN is not only due to the live tumor cells but also due to the difference in final tumor cell numbers and their differential effects on the peripheral tissue in the two settings. Response: The reviewer is correct, in that mice inoculated with “live” tumor cell will invariably harbor more tumor cells (and thus more overall tumor antigen) at the time of harvesting the associated LN due to continued tumor growth. This is unfortunately difficult to control for experimentally. We have noted this concern as a potential caveat in the revised manuscript under the subheading “Caveats and study limitations”. (Major) Reviewer Comment 4: It is unclear whether the authors verified if any of the live tumor injected mice showed tumor cell metastasis to the draining LNs which got analyzed. That could have been analyzed by flow cytometry (CD45- cells with specific FSC/SSC characteristics; or a non-stated epithelial marker) or by other means. This information would be valuable to know given the likely difference between premetastatic vs metastatic LNs stroma. Response: Overt metastatic tumor growth was not observed in any live tumor-bearing mice, nor were any clusters identified as cancer cells within the total 50,381 cells sequenced in this dataset. However, because these samples were enriched for stromal populations, it is likely that cancer cells (if present) would be lost following sample enrichment, particularly if originally present in low numbers. As the reviewer suggested, we looked further into our (unenriched) flow cytometry data of dissociated TdLN and ndLNs to identify presence of tumor cells. While no epithelial marker is included in this panel, MC38 cancer cells can be identified as CD45-, PDPN-, CD31-, PD-L1+ (PMID: 28507803). Indeed we do find small numbers of these cells specifically in the tumor-draining lymph node. Average cell counts were 114 total cells in TdLN compared to 4 in NdLN (p value = 0.0085). This analysis has been added to the revised manuscript as supplementary figure 3. We believe that presence of cancer cells at such low numbers, may reflect either shed/migrating tumor cells or the presence of early micrometastases. Discussion of this topic is likewise added to the revised manuscript, and the above-mentioned flow cytometry analysis has been included as Supplementary Fig. 3. (Minor) Reviewer Comment 1: The results start with a rather lengthy intro and part of it could be incorporated into the introduction where some of these points are already raised. Response: Per the reviewer’s suggestion, we have removed the first paragraph of the results. This paragraph reiterated the role of tissue stroma in tumor progression and was mostly redundant. (Minor) Reviewer Comment 2: BRC are claimed to populate the interfollicular space; please explain the rational or the publication stating this. Response: While the exact subsetting criteria and nomenclature varies across different publications to date, the population we refer to as “BRCs” most closely aligns with interfollicular reticular cell populations described as Ccl19lo, Ch25h+, Cxcl13+ in Pikor et al. (PMID: 32424359) and Rodda et al. (PMID: 29752062). This population (or a proportion of this population) is considered to reside in the interfollicular space due to IHC and RNAscope data included in those respective publications. Regarding the nomenclature, this population of reticular cells is considered a subset of “B cell interacting” reticular cells in Pikor et al. Whereas it is referred to as a subset of TRC in Rodda et al. We use the BRC nomenclature due to low Ccl19 and high Cxcl13 expression, suggestive of a B-cell association. (Minor) Reviewer Comment 3: Enzymatic digestion (‘as previously described’): please add reference with a more detailed protocol as this aspect is key for appropriate reproduction. Response: Reference added to revised manuscript (Minor) Reviewer Comment 4: Mention at beginning that MC38 are a colorectal carcinoma cell line. Response: mention of MC38 as colorectal carcinoma cells has been added to the revised manuscript. (Minor) Reviewer Comment 5: In the conduit section: conduits are not simply fibers (although they appear that way histologically and contain fibers) but have also basement membranes thereby forming true tubes/channels; thus the term fiber is not ideal. Similar, the term ‘transendothelial channels’ is not appropriate even when talking of the SCS where occasionally such channels traversing the SCS can be observed but the majority of conduits do not seem to traverse the endothelium. Response: We have changed the description of LN conduits as “a branched network of fibers” to state instead “a branched network of fibrous structures”. While our discussion of conduit networks does not fully address the intricacies of conduit anatomy, we have included relevant references to more in-depth exploration of this topic. Regarding the statement of transendothelial channels, we discuss the nature of these structures as described in the referenced citation (Ranktakari et al 2015). We do not make any statement speculating whether any conduits themselves traverse the endothelium. Rather, we discuss the findings of the referenced study which explore the idea of size-restriction as a function of PLVAP fibrils that exist within transendothelial channels. To clarify, in this publication, “transendothelial channels” and conduits are separate structures. The former spans the length of sinus-lining endothelial cells, while the latter underlies the endothelium (as such this finding is not reliant on whether any or all conduits actually traverse the endothelium). This publication suggests that molecules pass from LN sinus into the LN parenchyma through transendothelial channels, and upon traversing the endothelial lining, can subsequently access the underlying conduit network freely. However, the structure of the transendothelial channel (dictated by PLVAP) is the gating feature for larger molecules. We reference this finding because any structural disruption to the sinus-lining endothelium could theoretically perturb the above described regulation of molecule sizes that are allowed to pass into the conduit network. This would be one potential explanation to the loss of size-exclusivity noted by Riedel et al within the tdLN. (Minor) Reviewer Comment 6: ‘Exhibited of’ (please drop ‘of’). Response: Typo has been corrected. (Minor) Reviewer Comment 7: IL33 discussion: may add another reference showing role of IL33 for memory T cell response (Marx et al.,2024 [Ref 1]). Response: This reference has be added. Thank you for the suggestion (Minor) Reviewer Comment 8: Please spell out LDHA and PKM at first mentioning and possibly the reaction they catalyze to inform the non-specialist reader. Response: Definitions have been added. (Minor) Reviewer Comment 9: Fig.1B and D: I assume these Umaps show data compiled from all 4 groups of mice (please state clearly in legend); it would be of interest to display the Umaps of each group separately (in a supplementary figure) for readers to understand the stromal cell clustering in a 2D space for the 4 groups. Response: Per the reviewer’s suggestion, we have generated a new supplementary (Supplementary figure 2), which exhibits the distribution of cells from each treatment group within the corresponding endothelial (Supplementary Fig 2A) and fibroblast (Supplementary Fig 2B) Umaps. Visualization of cell clustering in this 2D space further reflects the increased representation of specific stromal subclusters (including fLEC, MRC, and PRC) from the tumor-draining LN. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Luther S. Peer Review Report For: Tumor-driven stromal reprogramming in the pre-metastatic lymph node [version 1; peer review: 2 approved with reservations] . F1000Research 2024, 13 :223 ( https://doi.org/10.5256/f1000research.159080.r309554) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-223/v1#referee-response-309554 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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