Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow

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Spatial omics closes this gap by mapping RNA and proteins in situ while preserving morphology, and recent platforms are increasingly compatible with formalin-fixed paraffin-embedded (FFPE) tissue, enabling use in routine pathology and retrospective cohorts. This mini-review offers a pragmatic, step-by-step workflow for integrating spatial assays with H&E: define the clinical decision; select a fit-for-purpose modality (whole-transcriptome spot/grid vs targeted in situ RNA; multiplex proteomics); lock pre-analytics aligned to histology (sectioning, staining, de-crosslinking, storage); pre-specify regions of interest (ROIs), registration, and segmentation rules; analyse with quality-assurance gates (normalisation, deconvolution, batch handling, spatial statistics); and validate and report using orthogonal assays and multi-site replication. FFPE-ready platforms and typical use-cases are summarised, with emphasis on pre-analytical factors that materially affect signal and analysis “recipes” distilled from recent benchmarks. Brief clinical exemplars illustrate how H&E-anchored spatial maps change decisions by pinpointing actionable niches (e.g., immune neighbourhoods, vascular niches, layer-specific programmes). Common limitations are also outlined, including technology trade-offs, pre-analytics, sampling bias, segmentation and deconvolution error, batch effects, cost, turnaround, and regulatory considerations. Future directions include standards and metadata, cross-platform integration, prospective evidence, automation and quality assurance, and multi-omic detection. Overall, the goal is to support pathology and translational teams in adopting spatial omics in FFPE with both discipline and speed, focusing on clinically meaningful decisions while ensuring reproducibility and credibility." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1057/v1", "name": "Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed..." } } ] } Home Browse Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Alwahaibi N. Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.12688/f1000research.170680.1 ) 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 ▬ ✚ Review Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] Nasar Alwahaibi https://orcid.org/0000-0002-9421-0951 Nasar Alwahaibi https://orcid.org/0000-0002-9421-0951 PUBLISHED 09 Oct 2025 Author details Author details Biomedical Science, Sultan Qaboos University College of Medicine and Health Science, Muscat, Muscat Governorate, Oman Nasar Alwahaibi Roles: Data Curation, Investigation, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Bioinformatics gateway. Abstract Haematoxylin and eosin (H&E) remain the foundation of tissue diagnosis, yet many clinical questions, tumour–immune architecture, spatial heterogeneity, and predictors of therapy response, require molecular context that routine slides cannot provide. Spatial omics closes this gap by mapping RNA and proteins in situ while preserving morphology, and recent platforms are increasingly compatible with formalin-fixed paraffin-embedded (FFPE) tissue, enabling use in routine pathology and retrospective cohorts. This mini-review offers a pragmatic, step-by-step workflow for integrating spatial assays with H&E: define the clinical decision; select a fit-for-purpose modality (whole-transcriptome spot/grid vs targeted in situ RNA; multiplex proteomics); lock pre-analytics aligned to histology (sectioning, staining, de-crosslinking, storage); pre-specify regions of interest (ROIs), registration, and segmentation rules; analyse with quality-assurance gates (normalisation, deconvolution, batch handling, spatial statistics); and validate and report using orthogonal assays and multi-site replication. FFPE-ready platforms and typical use-cases are summarised, with emphasis on pre-analytical factors that materially affect signal and analysis “recipes” distilled from recent benchmarks. Brief clinical exemplars illustrate how H&E-anchored spatial maps change decisions by pinpointing actionable niches (e.g., immune neighbourhoods, vascular niches, layer-specific programmes). Common limitations are also outlined, including technology trade-offs, pre-analytics, sampling bias, segmentation and deconvolution error, batch effects, cost, turnaround, and regulatory considerations. Future directions include standards and metadata, cross-platform integration, prospective evidence, automation and quality assurance, and multi-omic detection. Overall, the goal is to support pathology and translational teams in adopting spatial omics in FFPE with both discipline and speed, focusing on clinically meaningful decisions while ensuring reproducibility and credibility. READ ALL READ LESS Keywords Spatial omics; FFPE, histopathology, H&E, in situ RNA imaging, imaging mass cytometry, multiplex ion beam imaging. Corresponding Author(s) Nasar Alwahaibi ( [email protected] ) Close Corresponding author: Nasar Alwahaibi Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Alwahaibi N. 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: Alwahaibi N. Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.12688/f1000research.170680.1 ) First published: 09 Oct 2025, 14 :1057 ( https://doi.org/10.12688/f1000research.170680.1 ) Latest published: 19 Jan 2026, 14 :1057 ( https://doi.org/10.12688/f1000research.170680.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction Histopathology still begins with haematoxylin and eosin (H&E), yet many clinical questions about tumour–immune architecture, heterogeneity, and therapy response, require molecular context that routine slides cannot provide. Spatial omics helps close this gap by mapping RNA and proteins in situ while preserving tissue architecture, and in the past few years platforms have become increasingly formalin-fixed paraffin-embedded (FFPE) compatible, widening access for routine pathology and retrospective biobanks. 1 , 2 High-resolution spatial transcriptomics can localise billions of transcripts at subcellular scales, supporting detailed maps of cell–cell interactions in clinical material and opening avenues for research and patient care. 3 , 4 Recent overviews aimed at pathologists and translational teams underscore this momentum and its implications for clinical research. 5 – 7 Despite rapid progress, barriers to confident adoption persist. Common pain points include: pre-analytical variability (fixation, sectioning, de-crosslinking), unclear best practices for region-of-interest (ROI) selection and cell segmentation, analytical and batch effects across slides/cohorts, and uncertainty about validation and reporting standards that will satisfy clinical rigour. Methodological reviews and best-practice guide repeatedly call out these gaps, and highlight the need for clearer guidance on how to integrate spatial readouts with H&E across the biopsy-to-report workflow. 8 – 10 This mini-review responds to those needs with a practical, FFPE-focused roadmap for pathology services and translational laboratories. It compares widely used FFPE-ready platforms, sequencing-based spatial transcriptomics (e.g., Visium/Visium HD) 11 and imaging-based in situ platforms (e.g., Xenium, CosMx), 12 in terms of resolution, panel breadth, capture area, and typical use-cases, 13 distills pre-analytics and QC steps aligned to histology workflows, 14 outlines ROI design, registration, segmentation, and analysis “recipes” that survive peer review, 15 and summarises validation strategies, including orthogonal assays and multi-site replication. 16 – 18 By anchoring recommendations in platform documentation and recent translational reviews, the focus remains on choices that are feasible in FFPE and compatible with routine pathology. 19 The aim of this mini-review is to provide a step-by-step guide for integrating spatial omics with routine H&E in FFPE specimens so teams can select a fit-for-purpose modality, implement robust pre-analytics and QC, plan analyses that generalize across sites, and structure validation and reporting to accelerate translational impact. Figure 1 summarises the end-to-end FFPE spatial workflow, define the decision → select modality → lock pre-analytics → pre-specify ROIs & registration → analyse with QA gates → validate & report, which I use to organise the sections that follow. Figure 1. Spatial and haematoxylin and eosin analysis in formalin-fixed paraffin-embedded: streamlined vertical workflow from decision-making to reporting. Platforms for FFPE pathology: what actually works Spatial assays you can deploy on archival FFPE tissue fall into two broad camps. Sequencing-based spatial transcriptomics (ST), e.g., 10x Visium HD (FFPE), captures spot-based whole-transcriptome profiles registered to H&E, trading single-cell resolution for large capture areas and broad gene coverage. 20 In situ imaging platforms, e.g., 10x Xenium and NanoString CosMx SMI, measure targeted RNA (and, for CosMx, proteins) at single-cell or subcellular resolution on FFPE sections. 21 , 22 MERFISH/MERSCOPE (Vizgen) is another high-plex in situ option with FFPE support. 23 For multiplex spatial proteomics, laboratories commonly use Imaging Mass Cytometry (IMC), Multiplexed Ion Beam Imaging (MIBI), 24 , 25 or cyclic immunofluorescence systems such as CODEX/CyCIF 26 ; these often align naturally with IHC-centric diagnostic questions. Good platform overviews for pathologists are now available, alongside manufacturer FFPE handbooks. Key specifications are summarised in Table 1 : sequencing-based “spot/grid” assays (e.g., 10x Visium FFPE/Visium HD) provide whole-transcriptome discovery over 6.5 × 6.5 mm capture areas (Visium 55 μm spots; HD 2 μm pixel output, typically binned), well suited to archival cohort screens and tumour–stroma mapping. 27 , 28 In situ RNA imaging (10x Xenium, NanoString CosMx SMI, Vizgen MERSCOPE) yields targeted single-cell/subcellular maps (CosMx up to ~6,000 RNAs; MERSCOPE up to ~1,000) for pathway-focused profiling, immune-niche interrogation, and cross-validation with IHC/RNAscope. 29 – 31 Multiplex spatial proteomics (IMC, MIBI, CODEX, CyCIF) complements RNA by quantifying proteins at single-cell resolution for immune phenotyping and actionable signatures. 32 Table 1. Formalin-fixed paraffin-embedded -compatible spatial omics platforms: a concise comparison for routine pathology. Class Representative platforms Nominal resolution Analyte Panel breadth Capture area/FOV Typical throughput Typical use-cases References Sequencing-based ST (spot/grid) 10x Visium FFPE/Visium HD Visium: 55 μm spots; HD: 2 μm pixel output (binned for analysis) RNA (whole-transcriptome) Whole-transcriptome Up to 4 capture areas/slide (~6.5 × 6.5 mm each) Tens of sections per run (scanner + NGS dependent) Discovery in archival cohorts; tumor–stroma programs; hypothesis generation 27 , 28 In-situ RNA imaging 10x Xenium; NanoString CosMx SMI; Vizgen MERSCOPE Single-cell/subcellular RNA (targeted); CosMx also protein Hundreds–thousands RNAs (CosMx up to ~6,000; MERSCOPE up to ~1,000); CosMx ~64–76 proteins Tile-based FOVs; user-selected ROIs; multi-tile mosaics ~1–10 slides/week (instrument dependent) Targeted pathway panels; immune niches; cross-validation with IHC/RNAscope 29 – 31 Multiplex spatial proteomics IMC; MIBI; CODEX; CyCIF Single-cell Protein (antibody panels) ~30–60+ markers (panelized) Tile ROIs; mm 2 –cm 2 mosaics ~1–10 slides/week Immune phenotyping; actionable protein signatures; trial correlative studies 32 Pre-analytics & tissue handling: small decisions, big effects FFPE spatial assays are unusually sensitive to pre-analytics. 33 Follow platform-specific guidance on section thickness, deparaffinisation, H&E/IF staining, decrosslinking, and storage; these steps strongly influence RNA integrity, probe binding, and downstream quantification. 34 For example, the Visium HD FFPE handbook and Xenium FFPE guide detail slide prep, staining, and decrosslinking workflows 27 ; MERSCOPE provides FFPE-specific drying and storage advice. Critically, enzymatic steps can backfire: excess Proteinase-K in GeoMx DSP improved total reads but increased negative probe counts and reduced signal-to-noise, ultimately decreasing genes detected, highlighting why labs should pilot enzyme conditions and lock them before a study. 31 ROI selection, registration & segmentation ROI strategy should be hypothesis-driven (e.g., tumour–stroma interfaces, immune niches, invasive fronts) and traceable back to H&E. 35 Platforms such as GeoMx and in situ imagers emphasize explicit ROI selection; document criteria prospectively. 36 Register spatial layers to H&E and use validated, reproducible segmentation—QuPath remains a robust open-source WSI toolset for nuclei/cell detection, while Cellpose (and its newer variants) generalizes well across staining modalities with minimal tuning. 37 When publishing multiplex imaging data, adhere to the Minimum Information about Highly Multiplexed Tissue Imaging (MITI) standard so ROIs, acquisition parameters, and processing are transparent and reusable. 38 , 39 Analysis workflows that survive peer review For spot-based spatial transcriptomics, most groups (a) perform QC and normalisation, 40 , 41 (b) deconvolve spots with scRNA-seq references, 42 , 43 and (c) test spatial associations. 44 Recent benchmarking across dozens of datasets recommends cell2location, CARD, and Tangram as consistently high performers 45 ; newer methods continue to appear, but your review should point readers to benchmark-grounded choices. For multi-slice or multi-cohort integration, use modern alignment tools and report cross-slide consistency. 46 For imaging proteomics, denoising, batch correction, and neighbourhood analysis are critical 47 ; recent best-practice pieces in oncology outline end-to-end pipelines (acquisition → segmentation → phenotyping → spatial stats) 9 , 45 , 48 , 49 Validation & reproducibility Translational claims require orthogonal validation (e.g., RNAscope/IHC for RNA/protein hits), multi-site replication, and pre-registered analysis plans. 50 Use reporting checklists from pathology-facing reviews and adopt MITI for multiplex imaging so images, masks, and metadata are reusable. 37 Where possible, include an external test set (a different scanner/site or archival cohort) and quantify agreement (e.g., correlation of cell-type abundance, niche frequency). 51 High-level clinical perspectives emphasize linking spatial findings to outcomes or therapeutic response, not just discovery. 52 Costs, throughput, and choosing RNA vs protein maps For budgeting and platform choice, compare assay chemistry, resolution, capture area/fields per run, and instrument time rather than chasing absolute prices (which vary by site and service contract). As a guide, instrument cost can be considered high (>$500,000), medium ($100,000–$500,000), or low ($1,000), medium ($100–$1,000), or low (<$100). 53 Protein-centric maps (multiplex IHC/IF) often deliver faster, lower per-slide costs for focused questions (e.g., immune phenotyping), 54 whereas whole-transcriptome ST (UMI-based RNA profiling) is better for unbiased discovery and retrospective cohorts. Resolution needs, spot vs single-cell/subcellular, and capture area (including the effective pixel/“bin,” e.g., 100 μm 2 ) determine run time and sequencing/imaging depth. 55 Above all, FFPE compatibility and workflow fit (embedding within existing histology/QC) should drive selection; LCM remains useful for targeted validation or rare regions. 56 Manufacturer documents (e.g., 10x Genomics, NanoString, Vizgen) summarize throughput, section prep, and run constraints that materially affect real-world cost and turnaround. 12 , 57 , 58 Adoption roadmap for pathology services Start small. Define a narrow clinical question and the decision it might change; pick one FFPE-compatible platform and standardize pre-analytics; write down ROI rules and lock segmentation; pre-register analysis and plan orthogonal validation; follow MITI for data/metadata; include a multi-site or external test component as early as feasible. 59 , 60 Recent best-practice frameworks in multiplex imaging/spatial biology, plus pathology-specific reviews, provide checklists you can adapt to your SOPs and QA documents. 24 Clinical exemplars Below are brief, real-world examples showing how pairing spatial omics with routine H&E can change decisions. By revealing what is happening and exactly where in the tissue, these maps help clinicians choose the right biopsy area, refine risk, and pick or validate targets for therapy, things that routine H&E or bulk tests often miss. In cutaneous squamous cell carcinoma, pairing spatial omics with H&E revealed where distinct tumour programmes live and whom they talk to. Integrated single-cell RNA-seq, spatial transcriptomics, and multiplexed ion-beam imaging mapped four tumour subpopulations, including a tumour-specific keratinocyte (TSK) state that localises to a fibrovascular niche on the H&E slide. Spatial mapping of ligand–receptor networks showed TSK cells act as a communication hub, while Tregs co-localized with CD8 T cells in compartmentalized stroma, an immunosuppressive arrangement you could miss with bulk profiling. Functionally, CRISPR screens flagged subpopulation-enriched networks as essential for tumourigenesis. Clinically, these H&E-anchored spatial readouts can guide biopsy targeting (sample the TSK/fibrovascular interface), refine risk stratification (presence/extent of Treg–CD8 niches), and nominate actionable pathways for trials focused on interrupting TSK-driven signalling or collapsing immunosuppressive neighbourhoods. 61 In pancreatic ductal adenocarcinoma, overlaying spatial proteomics on the H&E slide mapped the tumour microenvironment into 10 distinct neighbourhoods, including a vascular niche within PDAC’s characteristically hypovascular, hypoxic stroma. Across 35 H&E-guided ROIs from 9 patients (>140k cells, 26-marker imaging mass cytometry), the study localized where tumour proliferation concentrates and how immune subsets interface with vessels. Crucially, the vascular niche was tightly linked to CD44 + macrophages with a pro-angiogenic programme, nominating a microenvironmental target that standard bulk assays would miss. Clinically, these H&E-anchored spatial readouts can guide biopsy targeting (sample vascular niches), sharpen risk stratification (proliferative/immune–vascular interfaces), and inform trial design for anti-angiogenic or macrophage-modulating combinations in PDAC. 62 In fatal COVID-19 lung disease, FFPE spatial transcriptomics (GeoMx) co-registered to H&E pinpointed patchy, non-uniform SARS-CoV-2 distribution and localised host responses to the exact anatomic foci. Areas with high viral load on the slide showed amplified type I interferon signaling, alongside broader upregulation of inflammation, coagulation, and angiogenesis pathways, patterns a bulk assay would blur. After controlling for dominant cell types and inter-patient variability, only a few genes distinguished COVID-19 from fatal influenza, but IFI27 remained significantly higher in COVID-19, reinforcing its value as a tissue-level biomarker that aligns with blood-based diagnostics. Clinically, H&E-anchored spatial readouts can guide targeted sampling (multiple foci rather than single cores), support triage/therapy decisions by confirming interferon-rich, highly infected regions, and validate biomarkers like IFI27 directly in diseased lung architecture. 63 In human dorsolateral prefrontal cortex, H&E-anchored spatial transcriptomics (10x Visium) mapped the six cortical layers and uncovered layer-enriched gene programmes, refining classic laminar markers on the same slide. Overlaying these maps onto single-nucleus RNA-seq re-grounded molecular clusters in real anatomy, improving interpretability. Clinically relevant gene sets for schizophrenia and autism showed layer-specific enrichment, pointing to circuits and cell layers most implicated in disease, insight that can guide targeted sampling, neuropathology reporting, and hypothesis-driven trials (e.g., layer-aware biomarkers or neuromodulation targets). A simple data-driven clustering workflow further supports tissues with less obvious architecture, using H&E context to define spatial domains when boundaries are not visually clear. 64 In periodontitis, H&E-anchored spatial transcriptomics resolved gingival tissue into epithelium, inflamed connective tissue, and non-inflamed connective tissue on the same slide, revealing 92 genes upregulated specifically in inflamed zones. Top signals, IGLL5, SSR4, MZB1, XBP1, point to a B-cell/plasma-cell–rich, high-secretory programme and were validated by RT-qPCR and IHC. Clinically, these maps let dentists and pathologists target biopsies to truly active lesions, distinguish active vs quiescent sites for risk stratification and follow-up, and track response to therapy using compartment-specific markers, insights that bulk profiling would average away. 65 In melanoma lymph node metastases, H&E-anchored spatial transcriptomics (10x Visium) sequenced >2,200 tissue domains and, after deconvolution, linked gene programmes to specific histological entities on the slide. This revealed coexisting melanoma transcriptional signatures within single regions and defined lymphoid niches adjacent to tumour with distinct expression patterns, heterogeneity not evident on morphology alone. Clinically, such maps can refine biopsy targeting (sample mixed-signature zones), sharpen staging/prognosis by quantifying tumour–immune interfaces, and inform immunotherapy strategies by identifying lymphoid areas most engaged with tumour. In short, pairing spatial omics with H&E exposes actionable intratumoural and microenvironmental complexity that bulk profiling and routine histology would miss. 66 In rheumatoid arthritis (RA) vs spondyloarthritis (SpA) synovium, H&E-anchored spatial transcriptomics let investigators zoom into mononuclear infiltrates on the slide and read out compartment-specific programmes. RA hotspots showed adaptive immune/T–B cell interaction signatures with enrichment of central memory T cells, whereas SpA regions favoured tissue-repair pathways with effector memory T cells. These H&E-guided spatial maps, validated by IHC and in silico cell-type calls, offer practical levers: refine biopsy targeting, support differential diagnosis when histology overlaps, and align therapy choices (e.g., B/T-cell–directed strategies in RA vs repair-oriented pathways in SpA) while enabling site-specific response monitoring. 67 In leprosy, pairing spatial omics with H&E turned granulomas from a uniform “mass” on the slide into an organised, layered architecture with distinct cellular and functional zones. By integrating single-cell and spatial sequencing on biopsies from reversal reactions (RRs) versus lepromatous disease (L-lep), the study localised interferon-γ/IL-1β–regulated antimicrobial programmes to specific niches where macrophages, T cells, keratinocytes, and fibroblasts cooperate. Clinically, H&E-anchored maps can guide targeted sampling of active antimicrobial layers during RR, inform biomarker development for treatment monitoring (spatially resolved antimicrobial gene sets), and support therapy tailoring by highlighting sites most likely to respond to host-directed or immunomodulatory interventions, granularity that bulk assays or morphology alone would miss. 68 In ALS cortex, H&E-anchored spatial transcriptomics (∼100 μm spots) preserved laminar and regional anatomy on the slide, letting investigators pinpoint where disease programmes reside rather than averaging them out. Mapping post-mortem motor cortex from a C9orf72 case, then validating with BaseScope ISH and an extended cohort (sALS, SOD1, C9orf72), they found 16 dysregulated transcripts spanning six disease pathways and converged on two spatially dysregulated genes, GRM3 and USP47, consistently altered across ALS genotypes. Clinically, these H&E-registered maps help explain selective regional vulnerability, nominate region-aware diagnostic markers and therapeutic targets, and guide targeted sampling in neuropathology, insights that bulk RNA or dissociated single-cell data would miss. 69 In another ALS, H&E-anchored spatial transcriptomics mapped the spinal cord’s molecular shifts across disease time in mice and in human post-mortem tissue, revealing when and where key pathways turn on. The maps distinguished regional microglia vs astrocyte programmes early in disease, and identified transcriptional pathways shared between murine models and human cords, signals that bulk RNA or dissociated cells would blur. Clinically, these slide-localized readouts can guide targeted sampling (vulnerable ventral horn regions), sharpen biomarker development (region- and cell-state markers for progression), and inform trial design/stratification (enrolling patients by pathway-active niches), aligning therapeutic timing with the actual spatial order of neuroinflammatory events. 70 Limitations of spatial omics Limitations of spatial omics span several practical areas. First, there are technology trade-offs on FFPE sections between map resolution, number of targets, and area covered 71 : whole-transcriptome spot/grid methods lose single-cell precision, targeted in situ platforms measure fewer genes, and multiplex proteomics depends on well-validated antibodies. 72 Second, results are sensitive to pre-analytics, fixation quality, section thickness, and deparaffinisation/de-crosslinking, where too little or too much enzyme treatment degrades data; even storage time of cut slides matters. 73 – 75 Certain tissues are difficult: decalcified bone often has fragmented RNA, necrotic/bleeding areas give weak signal, and highly pigmented/autofluorescent tissues (e.g., melanoma, lipofuscin-rich) can confound fluorescence without mitigation. 64 , 76 Third, study design and sampling can introduce bias if ROI rules are not pre-specified and auditable (MITI) 49 , 77 ; small ROIs may be underpowered and single-slide studies face slide/batch variability, so plan power, use multiple slides, and model batch. 78 – 80 Retrospective cohorts may hide clinical/treatment confounders, so follow Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)/Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) principles. 81 , 82 Fourth, quantification and analysis have pitfalls: segmentation/cell calling remains error-prone and software/version changes can shift results, pipelines and parameters should be locked. 83 , 84 For spot-based data, deconvolution depends on single-cell references that may not match tissue/platform/disease and can bias estimates 85 – 87 ; batch effects (slide/run/site) can masquerade as biology without careful normalization/integration 88 ; testing thousands of features inflates false positives unless False Discovery Rate (FDR) is controlled and primary hypotheses are pre-registered. 89 Fifth, validation and generalizability are limited by variable cross-platform concordance (sequencing- vs imaging-based), so orthogonal confirmation (RNAscope/IHC) is important 64 , 90 ; many studies stop at discovery rather than prospective, multi-site validation with outcomes and REMARK-aligned reporting. 18 , 91 Sixth, operational and regulatory barriers include cost, compute/storage, and turnaround (e.g., Visium FFPE/HD depth; multi-TB images and QC), 49 , 92 site-to-site differences in infrastructure/training/QA that hinder reproducibility, 93 and the need to move from Research Use Only/Laboratory-Developed Test (RUO/LDT) RUO/LDT to in vitro Diagnostic (IVD) through Clinical Laboratory Improvement Amendments/College of American Pathologists (CLIA/CAP)-level validation with ongoing monitoring for assay/model drift. 94 Future directions To bring spatial omics into routine pathology, we need simple shared rules for data collection and reporting, using MITI-style metadata/checklists and multi-site harmonization, so ROI choices are auditable and datasets can be compared across studies. 4 , 64 We also need better ways to integrate platforms: align whole-transcriptome maps with targeted in situ RNA and multiplex proteomics on serial sections, and quantify uncertainty in those integrations, building on recent cross-technology benchmarks. 91 , 95 , 96 Clinical adoption will require prospective, multi-site studies with predefined endpoints, external test cohorts, and reporting aligned to biomarker standards such as REMARK. 97 End-to-end automation and QA, registration, segmentation/cell calling, deconvolution, batch correction, with version-locked code and continuous QC dashboards should be standard. 40 , 98 Practical multi-omic co-detection protocols (RNA–protein now, metabolites later) on FFPE, paired with orthogonal validation (RNAscope/IHC), will increase confidence. 83 , 99 Finally, improving cost and throughput, through batching, smart ROI strategies, and targeted panels, will help meet clinical turnaround times; recent work outlines feasible high-throughput paths. 37 , 39 Conclusions Spatial omics now complements routine H&E on FFPE tissue and can answer clinically relevant questions about tumour–immune architecture, heterogeneity, and microenvironmental niches. 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Immunother. Cancer. 2020; 8 . Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 09 Oct 2025 ADD YOUR COMMENT Comment Author details Author details Biomedical Science, Sultan Qaboos University College of Medicine and Health Science, Muscat, Muscat Governorate, Oman Nasar Alwahaibi Roles: Data Curation, Investigation, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 19 Jan 2026, 14:1057 https://doi.org/10.12688/f1000research.170680.2 version 1 Published: 09 Oct 2025, 14:1057 https://doi.org/10.12688/f1000research.170680.1 Copyright © 2025 Alwahaibi N. 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 Alwahaibi N. Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.12688/f1000research.170680.1 ) 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 09 Oct 2025 Views 0 Cite How to cite this report: Yang DW. Reviewer Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.188170.r438425 ) The direct URL for this report is: https://f1000research.com/articles/14-1057/v1#referee-response-438425 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 31 Dec 2025 Da-Wei Yang , Fudan University, Shanghai, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.188170.r438425 This FFPE-focused mini-review provides a pragmatic, clinic-facing roadmap for integrating spatial omics with routine histopathology, organized around a decision-first workflow (define the clinical decision → select modality → lock pre-analytics → pre-specify ROIs/registration → analyze with QA gates → ... Continue reading READ ALL This FFPE-focused mini-review provides a pragmatic, clinic-facing roadmap for integrating spatial omics with routine histopathology, organized around a decision-first workflow (define the clinical decision → select modality → lock pre-analytics → pre-specify ROIs/registration → analyze with QA gates → validate/report). The manuscript appropriately emphasizes pre-analytical sensitivity, ROI strategy, and the importance of reporting standards (e.g., MITI; STROBE/REMARK). However, the current framing overstates near-term readiness for routine pathology/IVD deployment, and the review would benefit from (i) more cautious clinical positioning, (ii) a minimal, transparent review methodology, (iii) updated platform coverage and nomenclature, and (iv) more benchmark-anchored guidance in the analytics section. Recommendation: Major revision. MAJOR COMMENTS Clinical positioning should be recalibrated: statements implying near-term compatibility with routine pathology/IVD deployment are overly optimistic given cost, operational complexity, cross-site reproducibility, and regulatory considerations. The review lacks minimal methodological transparency: a brief description of search strategy, eligibility criteria, and evidence typing is needed to support claims of coverage and reduce concerns about selection bias. Platform landscape coverage and naming require updating: spatial proteomics platforms should be more complete and nomenclature should be corrected/standardized (e.g., Phenocycler Fusion, formerly CODEX). The analytics section needs tightening and stronger evidentiary grounding: remove drafting artifacts/second-person phrasing, temper prescriptive statements, and anchor recommendations to benchmarking/comparative evidence; emphasize reproducibility practices and multidisciplinary expertise. Clinical exemplars should be more explicitly decision-linked: examples are informative but often remain hypothesis-generating; claims of clinical enablement should be supported by clearer decision points, validation pathways, and measurable endpoints, with explicit separation of established utility vs exploratory insights. The Introduction should acknowledge IHC as central to current clinical decision-making and more clearly articulate the incremental value of spatial omics beyond H&E+IHC (e.g., high-plex co-localization, niches, gradients, architecture, objective quantification). Future Directions should prioritize realistic near-term impact: leveraging large archival FFPE resources and standardized retrospective cohorts with cross-site replication, rather than implying near-term IVD certification. MINOR COMMENTS Define all abbreviations at first mention (e.g., ALS) and ensure consistency across text, figures, and tables. Ensure consistent platform naming across the manuscript, including tables and figure legends. Add brief workflow “failure modes and QC checkpoints” where relevant (e.g., autofluorescence, necrosis/hemorrhage, RNA quality variability, registration artifacts). Perform a language pass to remove residual drafting artifacts and standardize tone to formal scientific narration. Is the topic of the review discussed comprehensively in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Early lung cancer, pulmonary nodules, LDCT screening, thoracic imaging AI, radiomics, clinical decision support, biomarkers, multi-omics, liquid biopsy, NK cells, tumor microenvironment, single-cell RNA-seq, spatial transcriptomics, digital health, IoT respiratory medicine, medical simulation/metaverse medicine. 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 Yang DW. Reviewer Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.188170.r438425 ) The direct URL for this report is: https://f1000research.com/articles/14-1057/v1#referee-response-438425 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 19 Jan 2026 Nasar Alwahaibi , Biomedical Science, Sultan Qaboos University College of Medicine and Health Science, Muscat, Oman 19 Jan 2026 Author Response We would like to take this opportunity to express our thanks to the reviewer for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of ... Continue reading We would like to take this opportunity to express our thanks to the reviewer for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewer. Reviewer 2 This FFPE-focused mini-review provides a pragmatic, clinic-facing roadmap for integrating spatial omics with routine histopathology, organized around a decision-first workflow (define the clinical decision → select modality → lock pre-analytics → pre-specify ROIs/registration → analyze with QA gates → validate/report). The manuscript appropriately emphasizes pre-analytical sensitivity, ROI strategy, and the importance of reporting standards (e.g., MITI; STROBE/REMARK). However, the current framing overstates near-term readiness for routine pathology/IVD deployment, and the review would benefit from (i) more cautious clinical positioning, (ii) a minimal, transparent review methodology, (iii) updated platform coverage and nomenclature, and (iv) more benchmark-anchored guidance in the analytics section. Recommendation: Major revision. MAJOR COMMENTS Clinical positioning should be recalibrated: statements implying near-term compatibility with routine pathology/IVD deployment are overly optimistic given cost, operational complexity, cross-site reproducibility, and regulatory considerations. Response We appreciate the reviewer's feedback regarding the clinical positioning of spatial omics. We fully agree that statements implying near-term, widespread compatibility with routine pathology or IVD deployment can be overly optimistic and must be carefully qualified. Our intention with this mini-review is not to suggest immediate readiness for routine clinical integration, but rather to provide a pragmatic, step-by-step roadmap for translational teams and pathology services to rigorously explore and validate spatial omics, guiding efforts toward its eventual clinical utility. As suggested, we have recalibrated the clinical positioning throughout the text, incorporating changes as suggested into the abstract, introduction, limitations, future directions, and conclusion sections. The review lacks minimal methodological transparency: a brief description of search strategy, eligibility criteria, and evidence typing is needed to support claims of coverage and reduce concerns about selection bias. Response As suggested, a new subsection, Methodological Approach, has been added. Platform landscape coverage and naming require updating: spatial proteomics platforms should be more complete and nomenclature should be corrected/standardized (e.g., Phenocycler Fusion, formerly CODEX). Response As suggested, we have updated spatial proteomics platform nomenclature throughout the text, specifically corrected "CODEX" to "PhenoCycler Fusion," and ensure comprehensive coverage in the relevant sections and Table 1. The analytics section needs tightening and stronger evidentiary grounding: remove drafting artifacts/second-person phrasing, temper prescriptive statements, and anchor recommendations to benchmarking/comparative evidence; emphasize reproducibility practices and multidisciplinary expertise. Response As suggested, the analytics section has been revised to remove drafting artifacts and second-person phrasing, temper prescriptive statements with evidentiary grounding, and emphasize reproducibility and multidisciplinary expertise. Clinical exemplars should be more explicitly decision-linked: examples are informative but often remain hypothesis-generating; claims of clinical enablement should be supported by clearer decision points, validation pathways, and measurable endpoints, with explicit separation of established utility vs exploratory insights. Response Thank you for your comment, all clinical exemplars have been revised to be more explicitly decision-linked, clarify validation pathways, and separate established utility from exploratory insights as suggested. The Introduction should acknowledge IHC as central to current clinical decision-making and more clearly articulate the incremental value of spatial omics beyond H&E+IHC (e.g., high-plex co-localization, niches, gradients, architecture, objective quantification). Response As suggested, the introduction section has been revised to acknowledge IHC's central role and explicitly articulate the incremental value of spatial omics beyond H&E+IHC. Future Directions should prioritize realistic near-term impact: leveraging large archival FFPE resources and standardized retrospective cohorts with cross-site replication, rather than implying near-term IVD certification. Response The future directions section has been revised to prioritize leveraging archival FFPE resources and cross-site replication for near-term impact, distinguishing these steps from later IVD certification, as suggested. MINOR COMMENTS Define all abbreviations at first mention (e.g., ALS) and ensure consistency across text, figures, and tables. Response The entire manuscript, including text, figure, and table, has been reviewed to ensure all abbreviations are defined at first mention and consistently used thereafter, as suggested. Ensure consistent platform naming across the manuscript, including tables and figure legends. Response As suggested, consistent platform naming, including updates like PhenoCycler Fusion, has been ensured across the entire manuscript, including table and figure legends. Add brief workflow “failure modes and QC checkpoints” where relevant (e.g., autofluorescence, necrosis/hemorrhage, RNA quality variability, registration artifacts). Response As suggested, Workflow "failure modes and QC checkpoints" have been incorporated into relevant sections, particularly within pre-analytics, ROI selection, and analysis, , to enhance the practical utility of the guide. Perform a language pass to remove residual drafting artifacts and standardize tone to formal scientific narration. Response As suggested, the comprehensive language pass has been performed throughout the entire manuscript. Thank you. We would like to take this opportunity to express our thanks to the reviewer for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewer. Reviewer 2 This FFPE-focused mini-review provides a pragmatic, clinic-facing roadmap for integrating spatial omics with routine histopathology, organized around a decision-first workflow (define the clinical decision → select modality → lock pre-analytics → pre-specify ROIs/registration → analyze with QA gates → validate/report). The manuscript appropriately emphasizes pre-analytical sensitivity, ROI strategy, and the importance of reporting standards (e.g., MITI; STROBE/REMARK). However, the current framing overstates near-term readiness for routine pathology/IVD deployment, and the review would benefit from (i) more cautious clinical positioning, (ii) a minimal, transparent review methodology, (iii) updated platform coverage and nomenclature, and (iv) more benchmark-anchored guidance in the analytics section. Recommendation: Major revision. MAJOR COMMENTS Clinical positioning should be recalibrated: statements implying near-term compatibility with routine pathology/IVD deployment are overly optimistic given cost, operational complexity, cross-site reproducibility, and regulatory considerations. Response We appreciate the reviewer's feedback regarding the clinical positioning of spatial omics. We fully agree that statements implying near-term, widespread compatibility with routine pathology or IVD deployment can be overly optimistic and must be carefully qualified. Our intention with this mini-review is not to suggest immediate readiness for routine clinical integration, but rather to provide a pragmatic, step-by-step roadmap for translational teams and pathology services to rigorously explore and validate spatial omics, guiding efforts toward its eventual clinical utility. As suggested, we have recalibrated the clinical positioning throughout the text, incorporating changes as suggested into the abstract, introduction, limitations, future directions, and conclusion sections. The review lacks minimal methodological transparency: a brief description of search strategy, eligibility criteria, and evidence typing is needed to support claims of coverage and reduce concerns about selection bias. Response As suggested, a new subsection, Methodological Approach, has been added. Platform landscape coverage and naming require updating: spatial proteomics platforms should be more complete and nomenclature should be corrected/standardized (e.g., Phenocycler Fusion, formerly CODEX). Response As suggested, we have updated spatial proteomics platform nomenclature throughout the text, specifically corrected "CODEX" to "PhenoCycler Fusion," and ensure comprehensive coverage in the relevant sections and Table 1. The analytics section needs tightening and stronger evidentiary grounding: remove drafting artifacts/second-person phrasing, temper prescriptive statements, and anchor recommendations to benchmarking/comparative evidence; emphasize reproducibility practices and multidisciplinary expertise. Response As suggested, the analytics section has been revised to remove drafting artifacts and second-person phrasing, temper prescriptive statements with evidentiary grounding, and emphasize reproducibility and multidisciplinary expertise. Clinical exemplars should be more explicitly decision-linked: examples are informative but often remain hypothesis-generating; claims of clinical enablement should be supported by clearer decision points, validation pathways, and measurable endpoints, with explicit separation of established utility vs exploratory insights. Response Thank you for your comment, all clinical exemplars have been revised to be more explicitly decision-linked, clarify validation pathways, and separate established utility from exploratory insights as suggested. The Introduction should acknowledge IHC as central to current clinical decision-making and more clearly articulate the incremental value of spatial omics beyond H&E+IHC (e.g., high-plex co-localization, niches, gradients, architecture, objective quantification). Response As suggested, the introduction section has been revised to acknowledge IHC's central role and explicitly articulate the incremental value of spatial omics beyond H&E+IHC. Future Directions should prioritize realistic near-term impact: leveraging large archival FFPE resources and standardized retrospective cohorts with cross-site replication, rather than implying near-term IVD certification. Response The future directions section has been revised to prioritize leveraging archival FFPE resources and cross-site replication for near-term impact, distinguishing these steps from later IVD certification, as suggested. MINOR COMMENTS Define all abbreviations at first mention (e.g., ALS) and ensure consistency across text, figures, and tables. Response The entire manuscript, including text, figure, and table, has been reviewed to ensure all abbreviations are defined at first mention and consistently used thereafter, as suggested. Ensure consistent platform naming across the manuscript, including tables and figure legends. Response As suggested, consistent platform naming, including updates like PhenoCycler Fusion, has been ensured across the entire manuscript, including table and figure legends. Add brief workflow “failure modes and QC checkpoints” where relevant (e.g., autofluorescence, necrosis/hemorrhage, RNA quality variability, registration artifacts). Response As suggested, Workflow "failure modes and QC checkpoints" have been incorporated into relevant sections, particularly within pre-analytics, ROI selection, and analysis, , to enhance the practical utility of the guide. Perform a language pass to remove residual drafting artifacts and standardize tone to formal scientific narration. Response As suggested, the comprehensive language pass has been performed throughout the entire manuscript. Thank you. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 19 Jan 2026 Nasar Alwahaibi , Biomedical Science, Sultan Qaboos University College of Medicine and Health Science, Muscat, Oman 19 Jan 2026 Author Response We would like to take this opportunity to express our thanks to the reviewer for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of ... Continue reading We would like to take this opportunity to express our thanks to the reviewer for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewer. Reviewer 2 This FFPE-focused mini-review provides a pragmatic, clinic-facing roadmap for integrating spatial omics with routine histopathology, organized around a decision-first workflow (define the clinical decision → select modality → lock pre-analytics → pre-specify ROIs/registration → analyze with QA gates → validate/report). The manuscript appropriately emphasizes pre-analytical sensitivity, ROI strategy, and the importance of reporting standards (e.g., MITI; STROBE/REMARK). However, the current framing overstates near-term readiness for routine pathology/IVD deployment, and the review would benefit from (i) more cautious clinical positioning, (ii) a minimal, transparent review methodology, (iii) updated platform coverage and nomenclature, and (iv) more benchmark-anchored guidance in the analytics section. Recommendation: Major revision. MAJOR COMMENTS Clinical positioning should be recalibrated: statements implying near-term compatibility with routine pathology/IVD deployment are overly optimistic given cost, operational complexity, cross-site reproducibility, and regulatory considerations. Response We appreciate the reviewer's feedback regarding the clinical positioning of spatial omics. We fully agree that statements implying near-term, widespread compatibility with routine pathology or IVD deployment can be overly optimistic and must be carefully qualified. Our intention with this mini-review is not to suggest immediate readiness for routine clinical integration, but rather to provide a pragmatic, step-by-step roadmap for translational teams and pathology services to rigorously explore and validate spatial omics, guiding efforts toward its eventual clinical utility. As suggested, we have recalibrated the clinical positioning throughout the text, incorporating changes as suggested into the abstract, introduction, limitations, future directions, and conclusion sections. The review lacks minimal methodological transparency: a brief description of search strategy, eligibility criteria, and evidence typing is needed to support claims of coverage and reduce concerns about selection bias. Response As suggested, a new subsection, Methodological Approach, has been added. Platform landscape coverage and naming require updating: spatial proteomics platforms should be more complete and nomenclature should be corrected/standardized (e.g., Phenocycler Fusion, formerly CODEX). Response As suggested, we have updated spatial proteomics platform nomenclature throughout the text, specifically corrected "CODEX" to "PhenoCycler Fusion," and ensure comprehensive coverage in the relevant sections and Table 1. The analytics section needs tightening and stronger evidentiary grounding: remove drafting artifacts/second-person phrasing, temper prescriptive statements, and anchor recommendations to benchmarking/comparative evidence; emphasize reproducibility practices and multidisciplinary expertise. Response As suggested, the analytics section has been revised to remove drafting artifacts and second-person phrasing, temper prescriptive statements with evidentiary grounding, and emphasize reproducibility and multidisciplinary expertise. Clinical exemplars should be more explicitly decision-linked: examples are informative but often remain hypothesis-generating; claims of clinical enablement should be supported by clearer decision points, validation pathways, and measurable endpoints, with explicit separation of established utility vs exploratory insights. Response Thank you for your comment, all clinical exemplars have been revised to be more explicitly decision-linked, clarify validation pathways, and separate established utility from exploratory insights as suggested. The Introduction should acknowledge IHC as central to current clinical decision-making and more clearly articulate the incremental value of spatial omics beyond H&E+IHC (e.g., high-plex co-localization, niches, gradients, architecture, objective quantification). Response As suggested, the introduction section has been revised to acknowledge IHC's central role and explicitly articulate the incremental value of spatial omics beyond H&E+IHC. Future Directions should prioritize realistic near-term impact: leveraging large archival FFPE resources and standardized retrospective cohorts with cross-site replication, rather than implying near-term IVD certification. Response The future directions section has been revised to prioritize leveraging archival FFPE resources and cross-site replication for near-term impact, distinguishing these steps from later IVD certification, as suggested. MINOR COMMENTS Define all abbreviations at first mention (e.g., ALS) and ensure consistency across text, figures, and tables. Response The entire manuscript, including text, figure, and table, has been reviewed to ensure all abbreviations are defined at first mention and consistently used thereafter, as suggested. Ensure consistent platform naming across the manuscript, including tables and figure legends. Response As suggested, consistent platform naming, including updates like PhenoCycler Fusion, has been ensured across the entire manuscript, including table and figure legends. Add brief workflow “failure modes and QC checkpoints” where relevant (e.g., autofluorescence, necrosis/hemorrhage, RNA quality variability, registration artifacts). Response As suggested, Workflow "failure modes and QC checkpoints" have been incorporated into relevant sections, particularly within pre-analytics, ROI selection, and analysis, , to enhance the practical utility of the guide. Perform a language pass to remove residual drafting artifacts and standardize tone to formal scientific narration. Response As suggested, the comprehensive language pass has been performed throughout the entire manuscript. Thank you. We would like to take this opportunity to express our thanks to the reviewer for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewer. Reviewer 2 This FFPE-focused mini-review provides a pragmatic, clinic-facing roadmap for integrating spatial omics with routine histopathology, organized around a decision-first workflow (define the clinical decision → select modality → lock pre-analytics → pre-specify ROIs/registration → analyze with QA gates → validate/report). The manuscript appropriately emphasizes pre-analytical sensitivity, ROI strategy, and the importance of reporting standards (e.g., MITI; STROBE/REMARK). However, the current framing overstates near-term readiness for routine pathology/IVD deployment, and the review would benefit from (i) more cautious clinical positioning, (ii) a minimal, transparent review methodology, (iii) updated platform coverage and nomenclature, and (iv) more benchmark-anchored guidance in the analytics section. Recommendation: Major revision. MAJOR COMMENTS Clinical positioning should be recalibrated: statements implying near-term compatibility with routine pathology/IVD deployment are overly optimistic given cost, operational complexity, cross-site reproducibility, and regulatory considerations. Response We appreciate the reviewer's feedback regarding the clinical positioning of spatial omics. We fully agree that statements implying near-term, widespread compatibility with routine pathology or IVD deployment can be overly optimistic and must be carefully qualified. Our intention with this mini-review is not to suggest immediate readiness for routine clinical integration, but rather to provide a pragmatic, step-by-step roadmap for translational teams and pathology services to rigorously explore and validate spatial omics, guiding efforts toward its eventual clinical utility. As suggested, we have recalibrated the clinical positioning throughout the text, incorporating changes as suggested into the abstract, introduction, limitations, future directions, and conclusion sections. The review lacks minimal methodological transparency: a brief description of search strategy, eligibility criteria, and evidence typing is needed to support claims of coverage and reduce concerns about selection bias. Response As suggested, a new subsection, Methodological Approach, has been added. Platform landscape coverage and naming require updating: spatial proteomics platforms should be more complete and nomenclature should be corrected/standardized (e.g., Phenocycler Fusion, formerly CODEX). Response As suggested, we have updated spatial proteomics platform nomenclature throughout the text, specifically corrected "CODEX" to "PhenoCycler Fusion," and ensure comprehensive coverage in the relevant sections and Table 1. The analytics section needs tightening and stronger evidentiary grounding: remove drafting artifacts/second-person phrasing, temper prescriptive statements, and anchor recommendations to benchmarking/comparative evidence; emphasize reproducibility practices and multidisciplinary expertise. Response As suggested, the analytics section has been revised to remove drafting artifacts and second-person phrasing, temper prescriptive statements with evidentiary grounding, and emphasize reproducibility and multidisciplinary expertise. Clinical exemplars should be more explicitly decision-linked: examples are informative but often remain hypothesis-generating; claims of clinical enablement should be supported by clearer decision points, validation pathways, and measurable endpoints, with explicit separation of established utility vs exploratory insights. Response Thank you for your comment, all clinical exemplars have been revised to be more explicitly decision-linked, clarify validation pathways, and separate established utility from exploratory insights as suggested. The Introduction should acknowledge IHC as central to current clinical decision-making and more clearly articulate the incremental value of spatial omics beyond H&E+IHC (e.g., high-plex co-localization, niches, gradients, architecture, objective quantification). Response As suggested, the introduction section has been revised to acknowledge IHC's central role and explicitly articulate the incremental value of spatial omics beyond H&E+IHC. Future Directions should prioritize realistic near-term impact: leveraging large archival FFPE resources and standardized retrospective cohorts with cross-site replication, rather than implying near-term IVD certification. Response The future directions section has been revised to prioritize leveraging archival FFPE resources and cross-site replication for near-term impact, distinguishing these steps from later IVD certification, as suggested. MINOR COMMENTS Define all abbreviations at first mention (e.g., ALS) and ensure consistency across text, figures, and tables. Response The entire manuscript, including text, figure, and table, has been reviewed to ensure all abbreviations are defined at first mention and consistently used thereafter, as suggested. Ensure consistent platform naming across the manuscript, including tables and figure legends. Response As suggested, consistent platform naming, including updates like PhenoCycler Fusion, has been ensured across the entire manuscript, including table and figure legends. Add brief workflow “failure modes and QC checkpoints” where relevant (e.g., autofluorescence, necrosis/hemorrhage, RNA quality variability, registration artifacts). Response As suggested, Workflow "failure modes and QC checkpoints" have been incorporated into relevant sections, particularly within pre-analytics, ROI selection, and analysis, , to enhance the practical utility of the guide. Perform a language pass to remove residual drafting artifacts and standardize tone to formal scientific narration. Response As suggested, the comprehensive language pass has been performed throughout the entire manuscript. Thank you. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Asselin-Labat ML. Reviewer Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.188170.r427396 ) The direct URL for this report is: https://f1000research.com/articles/14-1057/v1#referee-response-427396 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 20 Nov 2025 Marie-Liesse Asselin-Labat , Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.188170.r427396 This review article provides an overview of spatial omics technologies and their potential use in pathology departments. In the introduction, the author indicates the transition from routine HandE for clinical decision making to spatial omics. The author should also ... Continue reading READ ALL This review article provides an overview of spatial omics technologies and their potential use in pathology departments. In the introduction, the author indicates the transition from routine HandE for clinical decision making to spatial omics. The author should also acknowledge that IHC is used for many clinical decisions. It seems a bit unrealistic to think that spatial omics is poised to be compatible with routine pathology. Cost and complexity of the methodology and analyses are a major barrier for clinical uptake, far from cross-centre validation for clinical testing. The author should temper the introduction and the future direction to acknowledge that spatial omics is not ready for IVD and routine clinical application. The focus of the review may be more on enabling high-quality translational research than bringing spatial omics technologies to IVD and routine pathology. Lunaphore COMET and MACSIMA should be included in the spatial proteomic platforms with CODEX. CODEX has been renamed Phenocycler Fusion. There is a comment on page 4, in Analysis workflow stating: ‘your review should point readers to benchmark-grounded choices’. This sentence needs to be edited, and benchmarked tools provided. Analysis workflows are still very complex, and there are a number of new analytical tools being generated. This section should be written with caution to highlight the evolving analytical tools. Also, experts in these analysis methodologies should be involved in the analysis. The examples cited are interesting and highlight ongoing translational research. The future direction section may focus on the opportunity for pathology labs to exploit spatial omics technologies to use their huge archival resources to address important clinical questions on retrospective, well-curated cohorts of samples, rather than IVD accreditation for routine clinical use, which is unrealistic now, given cost and complexity. Pathology labs should use this opportunity to work with technologists and data analysts/bioinformaticians to solve outstanding questions. Please define ALS. Is the topic of the review discussed comprehensively in the context of the current literature? Partly Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: cancer biology, spatial omics 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 Asselin-Labat ML. Reviewer Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.188170.r427396 ) The direct URL for this report is: https://f1000research.com/articles/14-1057/v1#referee-response-427396 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 19 Jan 2026 Nasar Alwahaibi , Biomedical Science, Sultan Qaboos University College of Medicine and Health Science, Muscat, Oman 19 Jan 2026 Author Response We would like to take this opportunity to express our thanks to the reviewers for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of ... Continue reading We would like to take this opportunity to express our thanks to the reviewers for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewers. Reviewer 1 This review article provides an overview of spatial omics technologies and their potential use in pathology departments. In the introduction, the author indicates the transition from routine H and E for clinical decision making to spatial omics. The author should also acknowledge that IHC is used for many clinical decisions. Response: As suggested, the introduction section has been revised to acknowledge IHC's central role for many clinical decisions. It seems a bit unrealistic to think that spatial omics is poised to be compatible with routine pathology. Cost and complexity of the methodology and analyses are a major barrier for clinical uptake, far from cross-centre validation for clinical testing. The author should temper the introduction and the future direction to acknowledge that spatial omics is not ready for IVD and routine clinical application. The focus of the review may be more on enabling high-quality translational research than bringing spatial omics technologies to IVD and routine pathology. Response: We appreciate your feedback on the readiness of spatial omics for routine clinical pathology. We fully agree that significant barriers (cost, complexity, validation, regulation) mean spatial omics is not yet ready for IVD or widespread clinical application. Our review's primary focus is on providing a roadmap for high-quality translational research, which is a critical prerequisite for eventual clinical impact, not immediate clinical adoption. We have refined the Introduction and Abstract sections to temper the overall tone and emphasize this translational research focus. Our Limitations' and Future Directions sections further elaborate on these crucial hurdles and the disciplined steps required for future clinical utility. Lunaphore COMET and MACSIMA should be included in the spatial proteomic platforms with CODEX. CODEX has been renamed Phenocycler Fusion. Response: Thank you for pointing out the omission of Lunaphore COMET and MACSIMA, and for the update regarding CODEX's renaming. We agree that these platforms are important to include for a comprehensive overview of spatial proteomics. We have updated the text in the 'Platforms for FFPE pathology: what actually works' section and Table 1 to reflect these additions and the correct nomenclature, specifically noting PhenoCycler Fusion (formerly CODEX). There is a comment on page 4, in Analysis workflow stating: ‘your review should point readers to benchmark-grounded choices’. This sentence needs to be edited, and benchmarked tools provided. Response: Thank you for your suggestion. We have incorporated it as requested. On page 4, under 'Analysis workflows that survive peer review,' we now highlight benchmark-grounded tools: Recent benchmarking studies across dozens of datasets consistently recommend methods such as cell2location, CARD, and Tangram for their high performance (48). Analysis workflows are still very complex, and there are a number of new analytical tools being generated. This section should be written with caution to highlight the evolving analytical tools. Also, experts in these analysis methodologies should be involved in the analysis. Response: We agree that spatial omics analysis workflows are complex and rapidly evolving, requiring caution and expert involvement. We've revised the 'Analysis workflows that survive peer review' section to emphasize this dynamic landscape, the continuous evaluation of tools, and the critical need for dedicated computational and statistical expertise. The examples cited are interesting and highlight ongoing translational research. Response: Thank you for this positive feedback. The future direction section may focus on the opportunity for pathology labs to exploit spatial omics technologies to use their huge archival resources to address important clinical questions on retrospective, well-curated cohorts of samples, rather than IVD accreditation for routine clinical use, which is unrealistic now, given cost and complexity. Pathology labs should use this opportunity to work with technologists and data analysts/bioinformaticians to solve outstanding questions. Response: Thank you for this highly relevant feedback. We fully agree that the immediate opportunity for pathology labs lies in leveraging archival resources for high-quality translational research, rather than immediate IVD accreditation, given current costs and complexity. We have revised the Future directions section to explicitly emphasize this approach and highlight the critical need for collaboration between pathologists, technologists, and data scientists. Thank you. We would like to take this opportunity to express our thanks to the reviewers for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewers. Reviewer 1 This review article provides an overview of spatial omics technologies and their potential use in pathology departments. In the introduction, the author indicates the transition from routine H and E for clinical decision making to spatial omics. The author should also acknowledge that IHC is used for many clinical decisions. Response: As suggested, the introduction section has been revised to acknowledge IHC's central role for many clinical decisions. It seems a bit unrealistic to think that spatial omics is poised to be compatible with routine pathology. Cost and complexity of the methodology and analyses are a major barrier for clinical uptake, far from cross-centre validation for clinical testing. The author should temper the introduction and the future direction to acknowledge that spatial omics is not ready for IVD and routine clinical application. The focus of the review may be more on enabling high-quality translational research than bringing spatial omics technologies to IVD and routine pathology. Response: We appreciate your feedback on the readiness of spatial omics for routine clinical pathology. We fully agree that significant barriers (cost, complexity, validation, regulation) mean spatial omics is not yet ready for IVD or widespread clinical application. Our review's primary focus is on providing a roadmap for high-quality translational research, which is a critical prerequisite for eventual clinical impact, not immediate clinical adoption. We have refined the Introduction and Abstract sections to temper the overall tone and emphasize this translational research focus. Our Limitations' and Future Directions sections further elaborate on these crucial hurdles and the disciplined steps required for future clinical utility. Lunaphore COMET and MACSIMA should be included in the spatial proteomic platforms with CODEX. CODEX has been renamed Phenocycler Fusion. Response: Thank you for pointing out the omission of Lunaphore COMET and MACSIMA, and for the update regarding CODEX's renaming. We agree that these platforms are important to include for a comprehensive overview of spatial proteomics. We have updated the text in the 'Platforms for FFPE pathology: what actually works' section and Table 1 to reflect these additions and the correct nomenclature, specifically noting PhenoCycler Fusion (formerly CODEX). There is a comment on page 4, in Analysis workflow stating: ‘your review should point readers to benchmark-grounded choices’. This sentence needs to be edited, and benchmarked tools provided. Response: Thank you for your suggestion. We have incorporated it as requested. On page 4, under 'Analysis workflows that survive peer review,' we now highlight benchmark-grounded tools: Recent benchmarking studies across dozens of datasets consistently recommend methods such as cell2location, CARD, and Tangram for their high performance (48). Analysis workflows are still very complex, and there are a number of new analytical tools being generated. This section should be written with caution to highlight the evolving analytical tools. Also, experts in these analysis methodologies should be involved in the analysis. Response: We agree that spatial omics analysis workflows are complex and rapidly evolving, requiring caution and expert involvement. We've revised the 'Analysis workflows that survive peer review' section to emphasize this dynamic landscape, the continuous evaluation of tools, and the critical need for dedicated computational and statistical expertise. The examples cited are interesting and highlight ongoing translational research. Response: Thank you for this positive feedback. The future direction section may focus on the opportunity for pathology labs to exploit spatial omics technologies to use their huge archival resources to address important clinical questions on retrospective, well-curated cohorts of samples, rather than IVD accreditation for routine clinical use, which is unrealistic now, given cost and complexity. Pathology labs should use this opportunity to work with technologists and data analysts/bioinformaticians to solve outstanding questions. Response: Thank you for this highly relevant feedback. We fully agree that the immediate opportunity for pathology labs lies in leveraging archival resources for high-quality translational research, rather than immediate IVD accreditation, given current costs and complexity. We have revised the Future directions section to explicitly emphasize this approach and highlight the critical need for collaboration between pathologists, technologists, and data scientists. Thank you. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 19 Jan 2026 Nasar Alwahaibi , Biomedical Science, Sultan Qaboos University College of Medicine and Health Science, Muscat, Oman 19 Jan 2026 Author Response We would like to take this opportunity to express our thanks to the reviewers for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of ... Continue reading We would like to take this opportunity to express our thanks to the reviewers for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewers. Reviewer 1 This review article provides an overview of spatial omics technologies and their potential use in pathology departments. In the introduction, the author indicates the transition from routine H and E for clinical decision making to spatial omics. The author should also acknowledge that IHC is used for many clinical decisions. Response: As suggested, the introduction section has been revised to acknowledge IHC's central role for many clinical decisions. It seems a bit unrealistic to think that spatial omics is poised to be compatible with routine pathology. Cost and complexity of the methodology and analyses are a major barrier for clinical uptake, far from cross-centre validation for clinical testing. The author should temper the introduction and the future direction to acknowledge that spatial omics is not ready for IVD and routine clinical application. The focus of the review may be more on enabling high-quality translational research than bringing spatial omics technologies to IVD and routine pathology. Response: We appreciate your feedback on the readiness of spatial omics for routine clinical pathology. We fully agree that significant barriers (cost, complexity, validation, regulation) mean spatial omics is not yet ready for IVD or widespread clinical application. Our review's primary focus is on providing a roadmap for high-quality translational research, which is a critical prerequisite for eventual clinical impact, not immediate clinical adoption. We have refined the Introduction and Abstract sections to temper the overall tone and emphasize this translational research focus. Our Limitations' and Future Directions sections further elaborate on these crucial hurdles and the disciplined steps required for future clinical utility. Lunaphore COMET and MACSIMA should be included in the spatial proteomic platforms with CODEX. CODEX has been renamed Phenocycler Fusion. Response: Thank you for pointing out the omission of Lunaphore COMET and MACSIMA, and for the update regarding CODEX's renaming. We agree that these platforms are important to include for a comprehensive overview of spatial proteomics. We have updated the text in the 'Platforms for FFPE pathology: what actually works' section and Table 1 to reflect these additions and the correct nomenclature, specifically noting PhenoCycler Fusion (formerly CODEX). There is a comment on page 4, in Analysis workflow stating: ‘your review should point readers to benchmark-grounded choices’. This sentence needs to be edited, and benchmarked tools provided. Response: Thank you for your suggestion. We have incorporated it as requested. On page 4, under 'Analysis workflows that survive peer review,' we now highlight benchmark-grounded tools: Recent benchmarking studies across dozens of datasets consistently recommend methods such as cell2location, CARD, and Tangram for their high performance (48). Analysis workflows are still very complex, and there are a number of new analytical tools being generated. This section should be written with caution to highlight the evolving analytical tools. Also, experts in these analysis methodologies should be involved in the analysis. Response: We agree that spatial omics analysis workflows are complex and rapidly evolving, requiring caution and expert involvement. We've revised the 'Analysis workflows that survive peer review' section to emphasize this dynamic landscape, the continuous evaluation of tools, and the critical need for dedicated computational and statistical expertise. The examples cited are interesting and highlight ongoing translational research. Response: Thank you for this positive feedback. The future direction section may focus on the opportunity for pathology labs to exploit spatial omics technologies to use their huge archival resources to address important clinical questions on retrospective, well-curated cohorts of samples, rather than IVD accreditation for routine clinical use, which is unrealistic now, given cost and complexity. Pathology labs should use this opportunity to work with technologists and data analysts/bioinformaticians to solve outstanding questions. Response: Thank you for this highly relevant feedback. We fully agree that the immediate opportunity for pathology labs lies in leveraging archival resources for high-quality translational research, rather than immediate IVD accreditation, given current costs and complexity. We have revised the Future directions section to explicitly emphasize this approach and highlight the critical need for collaboration between pathologists, technologists, and data scientists. Thank you. We would like to take this opportunity to express our thanks to the reviewers for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewers. Reviewer 1 This review article provides an overview of spatial omics technologies and their potential use in pathology departments. In the introduction, the author indicates the transition from routine H and E for clinical decision making to spatial omics. The author should also acknowledge that IHC is used for many clinical decisions. Response: As suggested, the introduction section has been revised to acknowledge IHC's central role for many clinical decisions. It seems a bit unrealistic to think that spatial omics is poised to be compatible with routine pathology. Cost and complexity of the methodology and analyses are a major barrier for clinical uptake, far from cross-centre validation for clinical testing. The author should temper the introduction and the future direction to acknowledge that spatial omics is not ready for IVD and routine clinical application. The focus of the review may be more on enabling high-quality translational research than bringing spatial omics technologies to IVD and routine pathology. Response: We appreciate your feedback on the readiness of spatial omics for routine clinical pathology. We fully agree that significant barriers (cost, complexity, validation, regulation) mean spatial omics is not yet ready for IVD or widespread clinical application. Our review's primary focus is on providing a roadmap for high-quality translational research, which is a critical prerequisite for eventual clinical impact, not immediate clinical adoption. We have refined the Introduction and Abstract sections to temper the overall tone and emphasize this translational research focus. Our Limitations' and Future Directions sections further elaborate on these crucial hurdles and the disciplined steps required for future clinical utility. Lunaphore COMET and MACSIMA should be included in the spatial proteomic platforms with CODEX. CODEX has been renamed Phenocycler Fusion. Response: Thank you for pointing out the omission of Lunaphore COMET and MACSIMA, and for the update regarding CODEX's renaming. We agree that these platforms are important to include for a comprehensive overview of spatial proteomics. We have updated the text in the 'Platforms for FFPE pathology: what actually works' section and Table 1 to reflect these additions and the correct nomenclature, specifically noting PhenoCycler Fusion (formerly CODEX). There is a comment on page 4, in Analysis workflow stating: ‘your review should point readers to benchmark-grounded choices’. This sentence needs to be edited, and benchmarked tools provided. Response: Thank you for your suggestion. We have incorporated it as requested. On page 4, under 'Analysis workflows that survive peer review,' we now highlight benchmark-grounded tools: Recent benchmarking studies across dozens of datasets consistently recommend methods such as cell2location, CARD, and Tangram for their high performance (48). Analysis workflows are still very complex, and there are a number of new analytical tools being generated. This section should be written with caution to highlight the evolving analytical tools. Also, experts in these analysis methodologies should be involved in the analysis. Response: We agree that spatial omics analysis workflows are complex and rapidly evolving, requiring caution and expert involvement. We've revised the 'Analysis workflows that survive peer review' section to emphasize this dynamic landscape, the continuous evaluation of tools, and the critical need for dedicated computational and statistical expertise. The examples cited are interesting and highlight ongoing translational research. Response: Thank you for this positive feedback. The future direction section may focus on the opportunity for pathology labs to exploit spatial omics technologies to use their huge archival resources to address important clinical questions on retrospective, well-curated cohorts of samples, rather than IVD accreditation for routine clinical use, which is unrealistic now, given cost and complexity. Pathology labs should use this opportunity to work with technologists and data analysts/bioinformaticians to solve outstanding questions. Response: Thank you for this highly relevant feedback. We fully agree that the immediate opportunity for pathology labs lies in leveraging archival resources for high-quality translational research, rather than immediate IVD accreditation, given current costs and complexity. We have revised the Future directions section to explicitly emphasize this approach and highlight the critical need for collaboration between pathologists, technologists, and data scientists. Thank you. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 09 Oct 2025 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 3 Version 2 (revision) 19 Jan 26 read read read Version 1 09 Oct 25 read read Marie-Liesse Asselin-Labat , Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia Da-Wei Yang , Fudan University, Shanghai, China Xu Xun , State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China 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 © 2026 Xun X. 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. 06 Feb 2026 | for Version 2 Xu Xun , State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China 0 Views copyright © 2026 Xun X. 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 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 General Assessment: This mini-review addresses a timely and critical topic. However, specifically for Version 2, the manuscript requires sharper delineation between "research utility" and "clinical readiness," and needs to provide more concrete guidance on the technical bottlenecks of FFPE implementation. Major Comments 1. Sharpen clinical positioning and recalibrate expectations: The clinical exemplars currently blur the line between hypothesis-generating research and actionable clinical insight. Phrases like "guide biopsy targeting" imply near-term utility that is currently unsupported by cost/benefit or regulatory evidence. The authors must explicitly label the research phase of these technologies (e.g., "Exploratory" vs. "Validation") and add disclaimers regarding the gap between biological relevance and clinical readiness. 2. Deepen methodological transparency regarding selection bias: While the new "Methodological Approach" section is noted, it lacks critical analysis regarding selection and publication bias. The literature heavily favors successful experiments on high-quality samples. The review must acknowledge that "step-by-step workflows" derived from these papers often overlook the high failure rates encountered with real-world archival blocks (e.g., block age >5 years, variable fixation). Please discuss these limitations to avoid survivorship bias in your recommendations. 3. Guide cross-platform decisions with a decision matrix: Synthesize the benchmarking literature to characterize batch effects and expand Figure 1 into a Decision Matrix. This should match the modality not just to the research question, but to sample constraints (e.g., "If RNA integrity is low, prioritize Modality X over Modality Y"). 4. Distinguish H&E workflows: Same-section vs. Adjacent-section: This is a critical technical distinction often overlooked. Same-section (e.g., Visium) versus adjacent-section (e.g., many proteomic panels) approaches carry distinct analytical consequences for registration error tolerance and segmentation parameterization. These distinctions must be articulated explicitly, accompanied by empirically grounded quality control thresholds for registration accuracy. 5. Stratify operational feasibility beyond technical specs: Instead of a theoretical framework, please stratify the clinical applicability based on Operational Readiness. Analyze the specific translational bottlenecks for each technology class, such as Turnaround Time (TAT) pressures, computational infrastructure requirements (e.g., local server vs. cloud compliance), and the complexity of CLIA-level validation. 6. Elevate H&E as an independent modality: The manuscript currently treats H&E predominantly as a spatial anchor. It is recommended that the "Future Directions" section explicitly state the value of integrating quantitative morphometric features (Computational Pathology) with molecular data, rather than viewing H&E merely as a background map. Minor Comments 1. Pre-analytical QC Metrics: Please specify concrete quality control metrics for FFPE tissues. For instance, discuss the role of DV200 scores in determining sample eligibility for transcriptomic assays, rather than just generic "fixation quality." 2. Nomenclature Consistency: Ensure platform names are current and consistent (e.g., clarify "CODEX" vs. "PhenoCycler Fusion" usage throughout). 3. Table 1 Coverage: In Table 1, please clarify the "effective coverage" or "gap area" for spot-based transcriptomics, as this is a material limitation for detecting rare niches compared to imaging-based methods. Is the topic of the review discussed comprehensively in the context of the current literature? Partly Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise my area of expertise involves the development of core instrumentation and technologies for DNA sequencing and synthesis, with a specific focus on single-cell sequencing and spatial omics technologies. Additionally, I am dedicated to the translational application of these advanced methods across diverse fields, including synthetic biology, clinical disease diagnosis and treatment. 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 (0) Xun X. Peer Review Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.195156.r452525) 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/14-1057/v2#referee-response-452525 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Asselin-Labat M. 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. 28 Jan 2026 | for Version 2 Marie-Liesse Asselin-Labat , Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia 0 Views copyright © 2026 Asselin-Labat M. 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 author has addressed my previous queries. I have no further comments. Competing Interests No competing interests were disclosed. Reviewer Expertise cancer biology, spatial omics 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) Asselin-Labat ML. Peer Review Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.195156.r451194) 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/14-1057/v2#referee-response-451194 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Yang D. 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. 20 Jan 2026 | for Version 2 Da-Wei Yang , Fudan University, Shanghai, China 0 Views copyright © 2026 Yang D. 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 have addressed all comments point by point, and I recommend acceptance of the current version. Competing Interests No competing interests were disclosed. Reviewer Expertise Early lung cancer, pulmonary nodules, LDCT screening, thoracic imaging AI, radiomics, clinical decision support, biomarkers, multi-omics, liquid biopsy, NK cells, tumor microenvironment, single-cell RNA-seq, spatial transcriptomics, digital health, IoT respiratory medicine, medical simulation/metaverse medicine. 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) Yang DW. Peer Review Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.195156.r451193) 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/14-1057/v2#referee-response-451193 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Yang D. 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. 31 Dec 2025 | for Version 1 Da-Wei Yang , Fudan University, Shanghai, China 0 Views copyright © 2026 Yang D. 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 This FFPE-focused mini-review provides a pragmatic, clinic-facing roadmap for integrating spatial omics with routine histopathology, organized around a decision-first workflow (define the clinical decision → select modality → lock pre-analytics → pre-specify ROIs/registration → analyze with QA gates → validate/report). The manuscript appropriately emphasizes pre-analytical sensitivity, ROI strategy, and the importance of reporting standards (e.g., MITI; STROBE/REMARK). However, the current framing overstates near-term readiness for routine pathology/IVD deployment, and the review would benefit from (i) more cautious clinical positioning, (ii) a minimal, transparent review methodology, (iii) updated platform coverage and nomenclature, and (iv) more benchmark-anchored guidance in the analytics section. Recommendation: Major revision. MAJOR COMMENTS Clinical positioning should be recalibrated: statements implying near-term compatibility with routine pathology/IVD deployment are overly optimistic given cost, operational complexity, cross-site reproducibility, and regulatory considerations. The review lacks minimal methodological transparency: a brief description of search strategy, eligibility criteria, and evidence typing is needed to support claims of coverage and reduce concerns about selection bias. Platform landscape coverage and naming require updating: spatial proteomics platforms should be more complete and nomenclature should be corrected/standardized (e.g., Phenocycler Fusion, formerly CODEX). The analytics section needs tightening and stronger evidentiary grounding: remove drafting artifacts/second-person phrasing, temper prescriptive statements, and anchor recommendations to benchmarking/comparative evidence; emphasize reproducibility practices and multidisciplinary expertise. Clinical exemplars should be more explicitly decision-linked: examples are informative but often remain hypothesis-generating; claims of clinical enablement should be supported by clearer decision points, validation pathways, and measurable endpoints, with explicit separation of established utility vs exploratory insights. The Introduction should acknowledge IHC as central to current clinical decision-making and more clearly articulate the incremental value of spatial omics beyond H&E+IHC (e.g., high-plex co-localization, niches, gradients, architecture, objective quantification). Future Directions should prioritize realistic near-term impact: leveraging large archival FFPE resources and standardized retrospective cohorts with cross-site replication, rather than implying near-term IVD certification. MINOR COMMENTS Define all abbreviations at first mention (e.g., ALS) and ensure consistency across text, figures, and tables. Ensure consistent platform naming across the manuscript, including tables and figure legends. Add brief workflow “failure modes and QC checkpoints” where relevant (e.g., autofluorescence, necrosis/hemorrhage, RNA quality variability, registration artifacts). Perform a language pass to remove residual drafting artifacts and standardize tone to formal scientific narration. Is the topic of the review discussed comprehensively in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Early lung cancer, pulmonary nodules, LDCT screening, thoracic imaging AI, radiomics, clinical decision support, biomarkers, multi-omics, liquid biopsy, NK cells, tumor microenvironment, single-cell RNA-seq, spatial transcriptomics, digital health, IoT respiratory medicine, medical simulation/metaverse medicine. 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 19 Jan 2026 Nasar Alwahaibi, Biomedical Science, Sultan Qaboos University College of Medicine and Health Science, Muscat, Oman We would like to take this opportunity to express our thanks to the reviewer for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewer. Reviewer 2 This FFPE-focused mini-review provides a pragmatic, clinic-facing roadmap for integrating spatial omics with routine histopathology, organized around a decision-first workflow (define the clinical decision → select modality → lock pre-analytics → pre-specify ROIs/registration → analyze with QA gates → validate/report). The manuscript appropriately emphasizes pre-analytical sensitivity, ROI strategy, and the importance of reporting standards (e.g., MITI; STROBE/REMARK). However, the current framing overstates near-term readiness for routine pathology/IVD deployment, and the review would benefit from (i) more cautious clinical positioning, (ii) a minimal, transparent review methodology, (iii) updated platform coverage and nomenclature, and (iv) more benchmark-anchored guidance in the analytics section. Recommendation: Major revision. MAJOR COMMENTS Clinical positioning should be recalibrated: statements implying near-term compatibility with routine pathology/IVD deployment are overly optimistic given cost, operational complexity, cross-site reproducibility, and regulatory considerations. Response We appreciate the reviewer's feedback regarding the clinical positioning of spatial omics. We fully agree that statements implying near-term, widespread compatibility with routine pathology or IVD deployment can be overly optimistic and must be carefully qualified. Our intention with this mini-review is not to suggest immediate readiness for routine clinical integration, but rather to provide a pragmatic, step-by-step roadmap for translational teams and pathology services to rigorously explore and validate spatial omics, guiding efforts toward its eventual clinical utility. As suggested, we have recalibrated the clinical positioning throughout the text, incorporating changes as suggested into the abstract, introduction, limitations, future directions, and conclusion sections. The review lacks minimal methodological transparency: a brief description of search strategy, eligibility criteria, and evidence typing is needed to support claims of coverage and reduce concerns about selection bias. Response As suggested, a new subsection, Methodological Approach, has been added. Platform landscape coverage and naming require updating: spatial proteomics platforms should be more complete and nomenclature should be corrected/standardized (e.g., Phenocycler Fusion, formerly CODEX). Response As suggested, we have updated spatial proteomics platform nomenclature throughout the text, specifically corrected "CODEX" to "PhenoCycler Fusion," and ensure comprehensive coverage in the relevant sections and Table 1. The analytics section needs tightening and stronger evidentiary grounding: remove drafting artifacts/second-person phrasing, temper prescriptive statements, and anchor recommendations to benchmarking/comparative evidence; emphasize reproducibility practices and multidisciplinary expertise. Response As suggested, the analytics section has been revised to remove drafting artifacts and second-person phrasing, temper prescriptive statements with evidentiary grounding, and emphasize reproducibility and multidisciplinary expertise. Clinical exemplars should be more explicitly decision-linked: examples are informative but often remain hypothesis-generating; claims of clinical enablement should be supported by clearer decision points, validation pathways, and measurable endpoints, with explicit separation of established utility vs exploratory insights. Response Thank you for your comment, all clinical exemplars have been revised to be more explicitly decision-linked, clarify validation pathways, and separate established utility from exploratory insights as suggested. The Introduction should acknowledge IHC as central to current clinical decision-making and more clearly articulate the incremental value of spatial omics beyond H&E+IHC (e.g., high-plex co-localization, niches, gradients, architecture, objective quantification). Response As suggested, the introduction section has been revised to acknowledge IHC's central role and explicitly articulate the incremental value of spatial omics beyond H&E+IHC. Future Directions should prioritize realistic near-term impact: leveraging large archival FFPE resources and standardized retrospective cohorts with cross-site replication, rather than implying near-term IVD certification. Response The future directions section has been revised to prioritize leveraging archival FFPE resources and cross-site replication for near-term impact, distinguishing these steps from later IVD certification, as suggested. MINOR COMMENTS Define all abbreviations at first mention (e.g., ALS) and ensure consistency across text, figures, and tables. Response The entire manuscript, including text, figure, and table, has been reviewed to ensure all abbreviations are defined at first mention and consistently used thereafter, as suggested. Ensure consistent platform naming across the manuscript, including tables and figure legends. Response As suggested, consistent platform naming, including updates like PhenoCycler Fusion, has been ensured across the entire manuscript, including table and figure legends. Add brief workflow “failure modes and QC checkpoints” where relevant (e.g., autofluorescence, necrosis/hemorrhage, RNA quality variability, registration artifacts). Response As suggested, Workflow "failure modes and QC checkpoints" have been incorporated into relevant sections, particularly within pre-analytics, ROI selection, and analysis, , to enhance the practical utility of the guide. Perform a language pass to remove residual drafting artifacts and standardize tone to formal scientific narration. Response As suggested, the comprehensive language pass has been performed throughout the entire manuscript. Thank you. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Yang DW. Peer Review Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.188170.r438425) 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/14-1057/v1#referee-response-438425 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Asselin-Labat M. 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. 20 Nov 2025 | for Version 1 Marie-Liesse Asselin-Labat , Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia 0 Views copyright © 2025 Asselin-Labat M. 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 This review article provides an overview of spatial omics technologies and their potential use in pathology departments. In the introduction, the author indicates the transition from routine HandE for clinical decision making to spatial omics. The author should also acknowledge that IHC is used for many clinical decisions. It seems a bit unrealistic to think that spatial omics is poised to be compatible with routine pathology. Cost and complexity of the methodology and analyses are a major barrier for clinical uptake, far from cross-centre validation for clinical testing. The author should temper the introduction and the future direction to acknowledge that spatial omics is not ready for IVD and routine clinical application. The focus of the review may be more on enabling high-quality translational research than bringing spatial omics technologies to IVD and routine pathology. Lunaphore COMET and MACSIMA should be included in the spatial proteomic platforms with CODEX. CODEX has been renamed Phenocycler Fusion. There is a comment on page 4, in Analysis workflow stating: ‘your review should point readers to benchmark-grounded choices’. This sentence needs to be edited, and benchmarked tools provided. Analysis workflows are still very complex, and there are a number of new analytical tools being generated. This section should be written with caution to highlight the evolving analytical tools. Also, experts in these analysis methodologies should be involved in the analysis. The examples cited are interesting and highlight ongoing translational research. The future direction section may focus on the opportunity for pathology labs to exploit spatial omics technologies to use their huge archival resources to address important clinical questions on retrospective, well-curated cohorts of samples, rather than IVD accreditation for routine clinical use, which is unrealistic now, given cost and complexity. Pathology labs should use this opportunity to work with technologists and data analysts/bioinformaticians to solve outstanding questions. Please define ALS. Is the topic of the review discussed comprehensively in the context of the current literature? Partly Are all factual statements correct and adequately supported by citations? Yes Is the review written in accessible language? Yes Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise cancer biology, spatial omics 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 19 Jan 2026 Nasar Alwahaibi, Biomedical Science, Sultan Qaboos University College of Medicine and Health Science, Muscat, Oman We would like to take this opportunity to express our thanks to the reviewers for the positive feedback and helpful comments. Below are our responses, point-by-point to the queries of the reviewers. Reviewer 1 This review article provides an overview of spatial omics technologies and their potential use in pathology departments. In the introduction, the author indicates the transition from routine H and E for clinical decision making to spatial omics. The author should also acknowledge that IHC is used for many clinical decisions. Response: As suggested, the introduction section has been revised to acknowledge IHC's central role for many clinical decisions. It seems a bit unrealistic to think that spatial omics is poised to be compatible with routine pathology. Cost and complexity of the methodology and analyses are a major barrier for clinical uptake, far from cross-centre validation for clinical testing. The author should temper the introduction and the future direction to acknowledge that spatial omics is not ready for IVD and routine clinical application. The focus of the review may be more on enabling high-quality translational research than bringing spatial omics technologies to IVD and routine pathology. Response: We appreciate your feedback on the readiness of spatial omics for routine clinical pathology. We fully agree that significant barriers (cost, complexity, validation, regulation) mean spatial omics is not yet ready for IVD or widespread clinical application. Our review's primary focus is on providing a roadmap for high-quality translational research, which is a critical prerequisite for eventual clinical impact, not immediate clinical adoption. We have refined the Introduction and Abstract sections to temper the overall tone and emphasize this translational research focus. Our Limitations' and Future Directions sections further elaborate on these crucial hurdles and the disciplined steps required for future clinical utility. Lunaphore COMET and MACSIMA should be included in the spatial proteomic platforms with CODEX. CODEX has been renamed Phenocycler Fusion. Response: Thank you for pointing out the omission of Lunaphore COMET and MACSIMA, and for the update regarding CODEX's renaming. We agree that these platforms are important to include for a comprehensive overview of spatial proteomics. We have updated the text in the 'Platforms for FFPE pathology: what actually works' section and Table 1 to reflect these additions and the correct nomenclature, specifically noting PhenoCycler Fusion (formerly CODEX). There is a comment on page 4, in Analysis workflow stating: ‘your review should point readers to benchmark-grounded choices’. This sentence needs to be edited, and benchmarked tools provided. Response: Thank you for your suggestion. We have incorporated it as requested. On page 4, under 'Analysis workflows that survive peer review,' we now highlight benchmark-grounded tools: Recent benchmarking studies across dozens of datasets consistently recommend methods such as cell2location, CARD, and Tangram for their high performance (48). Analysis workflows are still very complex, and there are a number of new analytical tools being generated. This section should be written with caution to highlight the evolving analytical tools. Also, experts in these analysis methodologies should be involved in the analysis. Response: We agree that spatial omics analysis workflows are complex and rapidly evolving, requiring caution and expert involvement. We've revised the 'Analysis workflows that survive peer review' section to emphasize this dynamic landscape, the continuous evaluation of tools, and the critical need for dedicated computational and statistical expertise. The examples cited are interesting and highlight ongoing translational research. Response: Thank you for this positive feedback. The future direction section may focus on the opportunity for pathology labs to exploit spatial omics technologies to use their huge archival resources to address important clinical questions on retrospective, well-curated cohorts of samples, rather than IVD accreditation for routine clinical use, which is unrealistic now, given cost and complexity. Pathology labs should use this opportunity to work with technologists and data analysts/bioinformaticians to solve outstanding questions. Response: Thank you for this highly relevant feedback. We fully agree that the immediate opportunity for pathology labs lies in leveraging archival resources for high-quality translational research, rather than immediate IVD accreditation, given current costs and complexity. We have revised the Future directions section to explicitly emphasize this approach and highlight the critical need for collaboration between pathologists, technologists, and data scientists. Thank you. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Asselin-Labat ML. Peer Review Report For: Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1057 ( https://doi.org/10.5256/f1000research.188170.r427396) 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/14-1057/v1#referee-response-427396 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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