CD38+ Alveolar macrophages mediate early control of M. tuberculosis proliferation in the lung

CD38+ Alveolar macrophages mediate early control of M. tuberculosis proliferation in the lung | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article CD38+ Alveolar macrophages mediate early control of M. tuberculosis proliferation in the lung David Russell, Davide Pisu, Joshua Mattila, Luana Johnston This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3934768/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Tuberculosis, caused by M.tuberculosis (Mtb), remains an enduring global health challenge, especially given the limited efficacy of current therapeutic interventions. Much of existing research has focused on immune failure as a driver of tuberculosis. However, the crucial role of host macrophage biology in controlling the disease remains underappreciated. While we have gained deeper insights into how alveolar macrophages (AMs) interact with Mtb, the precise AM subsets that mediate protection and potentially prevent tuberculosis progression have yet to be identified. In this study, we employed multi-modal scRNA-seq analyses to evaluate the functional roles of diverse macrophage subpopulations across different infection timepoints, allowing us to delineate the dynamic landscape of controller and permissive AM populations during the course of infection. Our analyses at specific time-intervals post-Mtb challenge revealed macrophage populations transitioning between distinct anti- and pro-inflammatory states. Notably, early in Mtb infection, CD38 - AMs showed a muted response. As infection progressed, we observed a phenotypic shift in AMs, with CD38 + monocyte-derived AMs (moAMs) and a subset of tissue-resident AMs (TR-AMs) emerging as significant controllers of bacterial growth. Furthermore, scATAC-seq analysis of naïve lungs demonstrated that CD38 + TR-AMs possessed a distinct chromatin signature prior to infection, indicative of epigenetic priming and predisposition to a pro-inflammatory response. BCG intranasal immunization increased the numbers of CD38 + macrophages, substantially enhancing their capability to restrict Mtb growth. Collectively, our findings emphasize the pivotal, dynamic roles of different macrophage subsets in TB infection and reveal rational pathways for the development of improved vaccines and immunotherapeutic strategies. Biological sciences/Immunology/Infectious diseases/Tuberculosis Biological sciences/Microbiology/Bacteriology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Tuberculosis (TB) remains a major global health issue, as reported in WHO’s Global Tuberculosis Report 2022 1 . Mycobacterium tuberculosis (Mtb), the etiological agent of TB, primarily infects the lungs and has evolved to survive the host immune response. Within the lungs, alveolar macrophages (AMs) play a critical role in protecting the airway surfaces upon early infection 2 – 5 , with recruited monocyte-derived macrophages increasing in number as the infection progresses 6 , 7 . Therefore, understanding the specific role of AMs during Mtb infection is key to uncovering disease mechanisms and guiding the development of effective vaccines. In recent years, our understanding of the interactions between AMs and Mtb has grown considerably. Bulk transcriptomic studies, alongside dual RNA-seq, have demonstrated that AMs can facilitate Mtb replication and dissemination in the lung 6 , 8 – 10 . Recent advances in single-cell transcriptomics have uncovered the presence of heterogenous populations within the AM and IM lineages, each exhibiting varying responses to Mtb infection 9 . Moreover, innovative vaccination approaches, including subcutaneous, intravenous (iv) and pulmonary immunization using live BCG, have been found to activate AMs and innate immune cells, and provide lasting protection against Mtb 11 – 17 . However, despite these advancements, the specific AM populations and properties underlying this protective effect remain unidentified. In this study, we leverage our established multi-modal single-cell RNA sequencing (scRNA-seq) protocol 9 , 18 to identify the specific AM subsets that provide protection against Mtb. Employing the murine TB model, we evaluate macrophage phenotypes at 2-, 3-, 4-, and 6-weeks post-Mtb challenge to understand their roles in the early stages of infection and after BCG intranasal immunization. Our findings traced the evolving lung immune response to Mtb infection. We observe macrophage populations transitioning from anti-inflammatory to pro-inflammatory states over time. Our analysis revealed the most pronounced phenotypic changes occurring within the resident AM populations and the recruited monocyte-derived AMs (moAMs), underscoring their critical roles in the immune response to Mtb infection. A crucial discovery was the identification of CD38, an extracellular NADase linked to macrophage activation, inflammation and infection 19 – 22 , as a potential marker for protective responses against Mtb. We observed distinct phenotypes for SiglecF + CD38 + TR-AMs, SiglecF − CD38 + moAMs, and SiglecF + CD38 − TR-AMs throughout the disease process. Initially, CD38 − TR-AMs, displaying a muted pro-inflammatory response, were the main infected cell type. As infection progressed, CD38 − TR-AM numbers decreased, while CD38 + TR-AMs and the newly recruited moAMs rose in prevalence. In the later stages of infection, these CD38 + AM subsets emerged as the dominant infected host AM populations, demonstrating enhanced capacity to restrict Mtb's growth. Single-cell Assay for Transposase-Accessible Chromatin using sequencing (ScATAC-seq) analysis revealed that CD38 + TR-AMs have a unique chromatin structure that is distinct from CD38 − TR-AMs. These cells were already present in the lungs of naïve mice prior to infection, indicating an inherent epigenetic predisposition modulating their respective responses to Mtb infection. Additionally, intranasal immunization with live BCG, resulted in an increase in CD38 + macrophages in the lungs. This increase, the result of both an influx of CD38 + monocyte-derived macrophages and polarization of existing reactive pro-inflammatory CD38 + TR-AMs, offers significant protection against Mtb challenge. To date vaccine development against tuberculosis has met with a modest success 23 – 26 and it is seriously impaired by our lack of reliable biomarkers that are predictive of vaccine efficacy and outcome 27 – 31 . The identification and phenotypic characterization of those macrophage subsets best equipped to mediate protection against Mtb represents a significant advance in defining the immune response that we wish to drive with new immunotherapeutic interventions. Results and Discussion Ontogeny and intra-lineage diversity are key determinants in macrophage responses to Mtb infection. To better understand macrophage behavior during Mtb infection, we conducted an extensive multi-modal scRNA-seq analysis of macrophage phenotypes at 2-, 3-, 4-, 6-weeks post-Mtb challenge, which spans the transition from innate to adaptive immune responses 2,10,32-36 . Our aim was to determine how distinct macrophage populations respond post-Mtb infection, with the goal of identifying the subsets that are responsible for controller responses and those that favor Mtb replication and expansion. Consistent with our previous work 9 , we infected mice with the hspx’ ::gfp/smyc’::mCherry Mtb reporter strain. This strain expresses mCherry constitutively and GFP in response to host mediated immune-related stress 35 . For each infected host cell, we were able to assess the fitness status of intracellular Mtb, quantify the surface marker expression and profile their transcriptome 9 . After QC, our dataset comprised of 49600 cells, the significant majority of which were identified as macrophages (Figure 1A and Supp. Figure 1A). Recent studies, including ours, have shown that both resident alveolar macrophages (AMs) and recruited interstitial macrophages (IMs), are ontogenically diverse 6,8,37,38 and comprised of phenotypically-distinct subpopulations 9,39-42 . These subpopulations exhibit markedly different inflammatory responses to Mtb infection 9 . In the current study, we extend these findings, further characterizing the discrete AM and IMs subpopulations based on their origin and inflammatory profiles. AMs are often identified by their expression of surface markers SiglecF and CD11c 6,43,44 , and in our dataset we could identify AM subpopulations by SiglecF and CD11c staining (Figure 1B). Interestingly, we also noticed a population of cells that cluster with AMs by UMAP analysis, but do not stain for either SiglecF or CD11c (Figure 1A and 1C). To determine the origin of these cells, we focused on the early infection stages (Naïve and 2 weeks post-infection). We performed unbiased weighted gene correlation network analysis (WGCNA) on the resulting 13,125 cells to identify gene co-expression modules that define this population 45 . The analysis identified two gene expression modules that mirrored the differential staining of the SiglecF and CD11c antibodies (Figure 1D). Module 6 consists of a 28-gene co-expression set and includes genes commonly used to define tissue resident alveolar macrophages—such as Cd9, Mrc1 (CD206), Lpl, Trf, and Chil3 (Ym1) (Figure 1E) 8,9,46,47 . In contrast, Module 5, represented by a larger 110-gene set and expressed by the SiglecF/CD11c double negative population, is enriched in genes associated with the monocytic lineage (Figure 1E) and monocyte-to-macrophage differentiation (Supp Figure 1B). These AM-like cells express MafB —a driver of monocyte-to-macrophage differentiation—and Ly6c2 , a marker for cells of monocyte origins (Figure 1F) 48-52 . We therefore designated this population as moAMs (monocyte-derived AMs). In the lungs of naïve mice moAMs are absent, implying these monocytes migrate there and differentiate to perform AM-related functions after Mtb infection (Figure 2A). Trajectory and pseudotime analyses corroborate this hypothesis, revealing that moAMs, along with the classical NOS2 + and NOS2 - IMs, represent distinct cell fates (grey leaves) that originate from a population of infiltrating monocytes (root 2 - white circle - and branching point 11 – black circle -), characterized by a unique transcriptional signature associated with leukocyte adhesion and extravasation (Figures 2B and 2C) 53-64 . Uninfected bystander moAMs express genes associated with macrophage functions including phagocytosis, lipid metabolism, and RNA/protein synthesis, indicative of roles beyond mere homeostasis (Figure 2D) 65-67 . This perspective is supported by the contrasting gene expression in Mtb-infected moAMs, which pivot to a pro-inflammatory state, as evidenced by the upregulation of genes such as Acsl1, Hmox1, Saa3, Ptgs2, Slc7a11, Clec4e among others(Figure 2D) 9 . This transition is marked by elevated Nos2 expression and association with hspx’ ::GFP-high bacteria (Figure 2E). Such findings align with prior research which described the role of interferon-g in deactivating enhancer regions bound by the transcription factor Maf. This mechanism is crucial for suppressing M2 genes and increasing activation of monocyte-derived macrophages 68 . This moAM cohort expresses CD38 (Figure 2F), hence their designation as CD38 + moAMs. Next, we focused on TR-AMs and identified two subpopulations based on their CD38 expression. The CD38 + population exhibits increased Nos2 expression and is associated with stressed ( hspx’ ::GFP high) bacteria (Figure 2E and 2F). Notably, these TR-AMs are dual positive for SiglecF and CD11c (Figure 1B). They also express the TR-AM gene signature from Module 6 (Figure 1D), leading us to annotate them as CD38 + TR-AMs. Importantly, trajectory and pseudotime analyses establish the CD38 + TR-AMs as a distinct lineage from moAMs, as evidenced by their origin from root 1 (white circle) (Figure 2B). Additionally, we also identified CD38 - subpopulations of TR-AMs, associated with hspx’ ::GFP-low Mtb, indicating bacteria experiencing minimal host-derived stress (Figures 2E and 2F). In examining the IM subpopulations, we observed a similar level of heterogeneity. While both IM populations appear to originate from infiltrating monocytes (Figure 2B), the NOS2 + IMs are CD38 + , whereas the NOS2 - IM lack CD38 expression (Figure 2F). Looking at the transcriptional profiles of these populations revealed that CD38 + IMs, akin to CD38 + moAMs and TR-AMs, display a gene expression profile consistent with classic pro-inflammatory responses, which were previously linked to effective tuberculosis control 6,9 (Figure 2G) (Suppl. Table 1). Conversely, the IM subpopulation that is CD38 - not only lack Nos2 expression, but is also associated with hspx’ ::GFP-low Mtb (Figure 2E and 2F). Relative to NOS2 + IMs, these cells show amplified expression of genes linked to anti-inflammatory responses. This includes transcripts for complement proteins C1q, Ccl8, Ms4a7 among others, which have been previously characterized (Figure 2G) 9,69-73 . Furthermore, this subset still shows a partial expression of extravasation and adhesion markers shared with infiltrating monocytes (Figure 2C). This suggests that they represent recently arrived interstitial monocytes that have just been infected and have not yet undergone immune activation (Suppl Table 1). In summary, our analysis identified distinct subpopulations of AMs and IMs with varying ontogeny and inflammatory profiles. Importantly, we discovered a population of monocyte-derived AMs capable of transitioning to a pro-inflammatory state upon Mtb infection, and two subpopulations of TR-AMs based on CD38 expression, which are associated with different bacterial phenotypes following Mtb infection. Pro-inflammatory immune responses are associated with rising CD38 expression in macrophages over time. To understand the temporal evolution of macrophage immune responses following Mtb infection, we examined the expression patterns of pro-inflammatory gene signatures and associated markers over time. From our past studies, there was a discernible correlation between Nos2 expression in host macrophages and bacterial stress (Figure 2E) 9 . Building on this, we tracked the temporal trends of Nos2 expression within the infected macrophage populations. As the infection progresses, we observed an increase in nitric oxide production, reflected by both an increase in the level of Nos2 expression per cell (intensity) and the number of cells expressing Nos2 (prevalence), as shown in Figure 3A. Flow cytometry data confirmed this trend; the median fluorescence intensity (MFI) of the GFP signal from hspx’ ::GFP-infected cells increased over time, indicating heightened bacterial stress as the infection proceeded (Figure 3B). Importantly, infecting Nos2-KO mice with the hspx ’::GFP Mtb reporter strain resulted in minimal GFP induction by Mtb at both 2 and 4 weeks post-infection (Figure 3C). The limited induction of GFP expression by Mtb in Nos2-KO mice was also accompanied by an increase in bacterial load (Figure 3D). Finally, flow cytometry analysis at 2 wpi of Rag1 and IFN-γ KO mice revealed no induction of hspx’::GFP (Supplementary Figure 2A), confirming that production of nitric oxide by pro-inflammatory macrophages is critically dependent on the adaptive immune response. Consistent with these data, our surface marker analysis mirrored the trends observed in Nos2 RNA levels. Specifically, CD38 protein levels – a marker associated with inflammation 20,22 – increase in infected macrophages during the latter stages (Figure 3E). Parallel trends were also seen with the marker CD11b (Suppl Figure 2B). To gain deeper insights into the molecular mechanisms and biological processes driving these phenotypes, we performed a time-dependent pathway analysis 74 . This analysis identified 68 pathways that exhibited variations in expression as the infection progressed (p.adj < 0.05)(Table 1). Pathways showing increased representation over time were predominantly associated with inflammatory and anti-bacterial responses and were overly represented in CD38 + macrophages (Figure 3F and Supp figure 2C). Conversely, pathways that dominated early stages of infection were mostly associated with CD38 - macrophages, and aligned with processes linked to M2 polarization, tissue homeostasis and bacterial survival, such as negative regulation of protein kinase c signaling 75 , suppression of apoptosis and cell proliferation among others (Figure 3F and Supp figure 2D). Confirming these observations, our analysis revealed an increased recovery of Mtb mRNA reads from CD38 + macrophage populations over time, indicative of active bacterial degradation within these cells. Specifically, we noted low levels of Mtb reads at 2 weeks post-infection, which significantly increased from 3 to 6 weeks, as illustrated in Supplementary Figure 2E. Intriguingly, when focusing on bystander macrophages, our analysis revealed no change in their CD38 staining levels, irrespective of the timepoint examined, contrasting starkly with their infected counterparts (Figure 3E). To validate this, we leveraged a force-directed layout to conduct unbiased graph-based clustering of both cell types 76 . While infected cells clustered according to duration of infection, bystander cells grouped based on their ontogeny without temporal distinctions (Figure 3G). This pronounced difference emphasizes that the inflammatory responses we observe aren't merely a result of a broad immune activation of host macrophages over time; rather, they are finely tuned and specifically driven by active Mtb infection. In conclusion, our findings highlight a dynamic shift in the phenotype and relative proportion of infected macrophage populations over the course of infection. This transition emphasizes the crucial role of CD38 + macrophages, which appear to be closely associated with the control of infection. The relative proportion of Mtb-infected cells shifts from AMs to IMs over time. To quantify the changes in the proportion of infected macrophage populations over time, we employed a recently developed statistical framework designed for differential abundance testing (DAB) 77 . We used this approach to assess whether changes in abundance of both infected and bystander cells occur over the course of infection. Our analysis identified variations in the abundance of 400 neighbors across different timepoints in infected AMs and IMs subpopulations (with FDR < 0.1). This contrasted with bystander populations that exhibited minimal variation (Figure 4A). Mtb-infected neighbors belonging to the CD38 - TR-AMs subpopulations showed a marked decline in numbers late in infection. Conversely, neighbors within the NOS2 + IMs showed a significant increase in abundance during the same later timepoints (Figure 4B). Observations at the broader population level further highlighted this trend: at 2 weeks post-infection (wpi), AMs constituted 80% of all the infected macrophages, but by 3wpi this percentage was reduced to approximately 39%, and by the 4th and 6th wpi, it dropped to around 15% (Figure 4C). Overall, our data highlight a substantial shift in abundance from TR-AMs to IMs among the infected macrophages as the infection advances, as also confirmed by flow cytometry analysis (Supp. Figure 3). This trend sharply contrast the patterns seen in bystander AMs and IMs, whose proportions remain consistent across the analyzed timepoints (Figure 4A). The stability in the proportion of bystander macrophages in the Mtb-infected lung from the 2 weeks post-infection (wpi) suggests that, within our infection model, monocyte-derived cells have already been recruited to the lung by this time but remain largely uninfected (Figure 4D), similar to a previous report 78 . This premise is further supported by examining the distinct timelines of infection rates between CD38 + IMs and moAMs. Despite pseudotime analysis indicating a shared origin for both populations from the early monocyte cluster (as illustrated in Figure 2B), CD38 + IMs only became the more abundant infected group by the 3rd week post-infection (Figures 4A and 4C). In contrast, a substantial proportion of CD38 + moAMs were infected at a much earlier stage, by 2wpi (Figures 4C and 2A). Recent studies provide context to these observations. The initial delay in the infection of monocyte-derived IM may be attributed to the early-stage preference of the infection for the lung's alveolar space during the onset of tuberculosis. As the disease progresses, leading to the translocation of infected AMs from the alveolar to the interstitial lung space 2,6,8,10 , the infection rate of CD38 + Nos2 + IMs increases, leading to the overall shift in the abundance of infected macrophages from AMs to IMs as described above. In summary, while the total bystander IM and AM populations in the lung remain constant from 2wpi onwards, our data indicates a decline in the number of newly infected TR-AMs as the infection shifts from the alveolar to the interstitial lung space. In the established infection, our data demonstrate that monocyte-derived IMs are the dominant host cell population accounting for the majority of new infection events. The early infection timepoints are dominated by CD38 - TR-AMs that are relatively nonresponsive to tuberculosis infection. Work by Rothchild et al. highlighted that murine AMs exhibit a robust anti-inflammatory response during the early stages of tuberculosis infection, up to 10 dpi. This early-phase response is postulated to be pivotal to their role in the initial infection process 2,6,8,10 . Building on this previous work, our focus on the AM populations revealed that the CD38 - AM subset dominates this early infection landscape. Specifically, they account for approximately 70% of the Mtb-infected host cells at 14 dpi (Figure 5A). To gain a more nuanced understanding of the CD38 - AMs and the impact of their changing numbers, we employed differential abundance testing (DAB) to group previously identified neighbors based on their log-fold change (LFC) depletion rates, as an alternative approach to the traditional cluster-based categorization (Figure 5B) 77 . Through this approach, we identified two neighbor groups (group 2 and 5) exhibiting significant depletion rates in late infection (> 5 LFC). Examining the cell types associated with these high LFC depletion rate groups (FDR <0.1), we found that 98% of cells from group 2 belonged to the CD38 - TR-AMs subsets (spanning CD38 - TR-AM_1, CD38 - TR-AM_2, CD38 - TR-AM_3). For group 5, 57% were associated with the CD38 - TR-AMs and 33% with CD38 + moAMs (Figure 5C). The over-representation of CD38 - TR-AMs in groups marked by pronounced depletion rates, identified through our DAB approach and independently confirmed by compositional analysis through scCODA 79 (Supp. File 1) , suggests an innate susceptibility of these cells to Mtb infection. This vulnerability becomes more obvious when compared with the unchanging relative abundance of CD38 - TR-AMs in bystander populations across different infection timepoints (Figure 4A and 4D). Coupled with the observed decline in the overall count of infected AMs as the infection unfolds (Figure 4C), it becomes evident that very few CD38 - TR-AMs become infected in the later stages. This underscores the idea that the CD38 - TR-AM clusters, dominant at 14 dpi, are poorly equipped to survive Mtb infection. This hypothesis is supported by the absence of Nos2 mapping reads across all infection timepoints in CD38 - TR-AMs (Figure 5D), excepting cells bordering the MoAM in the CD38 - TR-AM_1 cluster (likely signifying a common scRNA-seq occurrence of cells being misattributed among neighboring clusters). DGE analysis of the CD38 - AMs further supports this hypothesis. We found CD38 - AMs to be associated with known markers of AM populations ( Chil3, Lpl, Marco, Mrc1, Trf ) 8,9 (Figure 5E). Examining the transcriptional profile of these populations we observed an upregulation of genes involved with lipid metabolism such as Fabp4, Mgll, and Lpl 8,80 . Additionally, we noted increased expression in genes linked to the electron transport chain (like mt-Nd1, mt-Nd2, mt-Nd4, mt-Cytb, and mt-Atp6 ) which play pivotal roles in cellular energy metabolism. This metabolic transcriptional signature was further complemented by the upregulation of genes connected to fatty acid uptake and transport (e.g., Dbi ) 81 , lipid droplet formation (e.g., Cidec, Plin2 ) 82,83 , and protection against oxidative stress (e.g., Gpx1, Gpx4 ) 84 (Supp Table 2). Collectively, this data indicates a shift towards fatty acid oxidation (FAO) metabolism. Importantly, the upregulation of genes involved in metabolic and homeostatic functions stands in contrast to a diminished expression of pro-inflammatory genes, critical in combating tuberculosis infection (Figure 5E) (Supp. Table 2). Moreover, Gene Ontology (GO) functional enrichment analysis of the transcriptional profile of CD38 - AMs also supports these observations. The functional profile of the CD38 - TR-AMs aligns with alveolar macrophage characteristics that have been shown to promote Mtb growth 6 . These include increased oxidative phosphorylation and fatty acid metabolism (Figure 5F). In summary, our data suggests that the broad range of anti-inflammatory responses often associated with AM population are mediated through CD38 - TR-AMs, which are highly susceptible and sensitive to Mtb infection during the early phases of TB. CD38 + AMs are key contributors in controlling tuberculosis infection. We re-clustered the AM subsets based on their surface expression of CD38 protein. Consistent with the previous observations, CD38 + cells only constituted 12% of the total infected AMs at 2 weeks, but this proportion increased to 89% and 93% at the 4 and 6-week timepoints, respectively (Figure 5G). Intriguingly, DAB testing revealed unchanging abundance of infected CD38 + AMs across timepoints (Figure 4A), and mo_AMs and CD38 + TR-AMs constitute the majority of the infected alveolar macrophages in the later stages of infection (Figure 4B-4C). This stability in numbers, even amidst a pronounced decline in the overall infected AMs (Figure 4C), implies that these cellular subsets might be inherently more resilient to Mtb infection. This interpretation aligns with their observed Nos2 expression patterns. Both CD38 + TR-AMs and mo_AMs exhibit a bimodal Nos2 expression, with increasing cell counts and Nos2 expression intensity, suggesting their antimicrobial capacity increases over time (Figure 5D). The elevated CD38 expression and pro-inflammatory pathways in AMs in latter stages, as described earlier, are predominantly represented by these two CD38 + populations. Trajectory and pseudotime analyses provide additional insights into these populations. While mo_AMs, CD38 - TR-AM_1, and CD38 - TR_AM_2 appear as mature endpoints (grey circles 12,18,17,7,5 and root 3, white circle), CD38 + TR-AMs seem to arise from enhanced polarization of the CD38 - TR-AM_3 subset (root 1, white circle and branching point 12,17, black circles, Figure 2B). Thus, we hypothesize that the decreased presence of the CD38 - TR-AM_3 subset in subsequent infection stages is likely a consequence of these cells transitioning to CD38 + TR-AMs, as highlighted in the subsequent paragraphs. Finally, enrichment analysis of the genes upregulated by CD38 + AMs reveals up-regulation of the same pathways that are associated with control of Mtb infection (Figure 5H). These findings suggest that the divergent behavior of CD38 + and CD38 - AMs in response to Mtb infection is programmed into the cell subsets prior to infection, with the CD38 + AMs playing a potentially crucial role in managing Mtb growth and spread. Intrapulmonary BCG vaccination amplifies the CD38 + macrophage population resulting in enhanced control of Mtb infection. Recent investigations, including those by Mata et al. 2021 14 , have shown that intrapulmonary BCG vaccination prior to Mtb infection induces protective responses in lung resident macrophages. To extend this observation, we assessed how pulmonary BCG administration modulates the responses of CD38 +/- macrophages to Mtb infection. Mice were vaccinated intranasally with live BCG and after a two-month period, were infected with the reporter smyc’ ::mCherry/ hspx’ ::GFP Mtb Erdman for a duration of two weeks. Subsequently, we integrated scRNA-seq datasets from these vaccinated mice with our existing timepoint analysis data. Analysis of infected macrophage populations from both BCG-vaccinated (n=3931 cells) and unvaccinated (n=2278 cells) mouse lungs, two weeks post-Mtb infection, revealed marked differences. In the BCG-vaccinated cohort, the monocyte-derived macrophages, associated with hspx’ ::GFP-high Mtb (Figures 1F and 2E), constituted the vast majority of infected macrophages, at 74.43%. This is in stark contrast to the unvaccinated cohort, where such cells accounted for just 43.19% (Figure 6A). Flow cytometry data from the sorted populations reinforced these findings. Macrophages from the BCG-treated group mounted a more effective response, limiting Mtb replication more efficiently. This is underscored by a marked reduction of the mCherry signal in infected cells of BCG-vaccinated mice, which was approximately ~2 log 10 times lower than that in infected cells from unvaccinated mice. Moreover, there was a substantial difference in the proportion of hspx’ ::GFPstressed bacteria between the two cohorts: GFP-high cells represented 22.6% of the total infected cells in the unvaccinated group compared to 77.78% in their BCG-vaccinated counterparts (Figure 6B). The distribution of hspx’: :GFP + cells between the two groups, as revealed in our scRNA-seq dataset (Supp. Figure 4B), aligned with our flow cytometry data. Complementing these findings, we observed a marked decrease in the overall percentage of infected cells in BCG treated mice (0.45%) compared to unvaccinated mice (2.08%) (Figure 6B), as also confirmed by independent flow cytometry analysis (Supp Figure 4A). This was also supported by an overall reduction in bacterial burden in BCG-treated mice, as assessed by CFU counts (Figure 6C). These differences in cellular responses between BCG-treated and unvaccinated mice highlight significant alterations in their macrophage populations. BCG-treated mouse lungs were dominated by highly pro-inflammatory monocytes, and there was a marked reduction in infected CD38 - TR-AMs compared to unvaccinated controls (Figure 6D and 6E). The reduced proportion of infected CD38 - TR-AMs in BCG-treated mice is likely the result of increased recruitment of pro-inflammatory monocytes, in addition to the transition of the CD38 - TR-AMs_3 into their CD38 + counterparts (Figure 6E). Using WGCNA, we identified a unique co-expression module of 463 genes exclusive to the macrophage populations of BCG-vaccinated mice (Supp. Figure 4C) (Supp. Table 3). This module was highly enriched in pro-inflammatory genes, including Nos2 and Cd38 (Supp Figure 4D). Further analysis revealed significant presence of genes involved in small GTPase signal transduction, encompassing Rab GTPases, guanine nucleotide exchange factors (GEFs) and GTPase activators (GAPs) (Supp. Figure 4E). Rab GTPases are known for their roles in endosomal trafficking, while GAPs and GEFs are vital for membrane transport, phagocytosis, and controlling of the actin cytoskeleton 85-87 . Their increased expression suggests modifications in intracellular trafficking within the BCG-treated macrophages. Altered Mtb intracellular trafficking and increased lysosomal fusion can limit bacterial growth 88-90 and amplify killing mechanisms due to immune activation and increased autophagy 91-95 . Our data confirms the previous findings as we observe that increased intracellular trafficking is tightly linked to increased expression of genes involved in lysosomal and autophagy functions in BCG-treated macrophages (Figure 6F). Additionally, genes facilitating macrophage migration and tissue invasion saw heightened expression in macrophages from BCG-treated lungs (Supp.Table 3). This observation aligns with the gene-set enrichment analysis of the 463 co-expressed genes from the scWGCNA module. Here, cell migration, motility, programmed cell death and autophagy emerged as the pathways enriched in BCG-treated macrophages, in both GO and KEGG analysis (Supp Figure 5A) (Supp Table 4). The increased expression of pro-inflammatory gene signatures involved in control of Mtb infection in BCG-treated macrophages aligns with their observed phenotype. A hallmark feature across BCG-treated mice was the increased expression of CD38 (Supp. Figure 5B). This immune control phenotype was still evident at 4 weeks post-infection, corroborating Mata et al.'s findings (Supp Figure 5C). Intriguingly, we observed discrepancies between the two studies regarding the origin of infected macrophages after BCG vaccination. In our study, the majority of infected macrophages in BCG-treated mice were monocyte-derived, while previous data indicated Mtb was mostly confined to AMs after BCG vaccination 14 . We believe the disparity was due to weak CD64 staining on pro-inflammatory monocyte-derived cells (Supp Figure 5D). To test this hypothesis, we re-infected BCG-treated and control mice with a high infection dose (5x10 3 CFU), similarly to Mata et al. 14 The published CD64 flow cytometry gating approach will identify most infected macrophages as AMs in BCG-treated mice 14 (Supp Figure 5E). However, when gating only on SiglecF, expressed uniquely by AMs (Figure 1B), the results indicate the majority of infected cells are monocyte-derived (Supp Figure 5F), as in our scRNA-seq dataset. Regardless of the gating method, we found a consistent increase in the percentage of infected AMs in BCG-vaccinated mice compared to the unvaccinated counterparts, underscoring their improved ability to restrict Mtb growth, both at 2wpi (Supp Figure 5F) and at 4wpi (Supp figure 5G) 14 . This aligns with our scRNA-seq results and highlights the fundamental role of the increased polarization of the CD38 - TR-AMs_3 transitioning to CD38 + TR-AMs for enhanced Mtb control in BCG-treated AMs (Figure 6E). In conclusion, our data suggests that the heightened defense against Mtb reinfection observed after intranasal BCG vaccination is due to both the increased presence of CD38 + monocyte-derived macrophages and the activation of resident CD38 + TR-AMs. Pre-existing differences in the chromatin organization of CD38 + vs CD38 - AMs are linked to differential responses to Mtb infection. Our scRNA-seq analysis revealed distinct differences in AM populations between naïve and Mtb-infected mice. Specifically, we observed that CD38 + moAMs are recruited to the lung in response to Mtb infection (Figure 2A). In contrast, CD38 + TR-AMs were already present prior to infection, predominantly exhibiting a less active CD38 - phenotype, which we previously defined as CD38 - TR-AM_3 (Figure 2A and 2B). To investigate the chromatin landscape and potential epigenetic regulation of AM subsets prior to infection, we performed scATAC-seq on CD45 + cells isolated from the lungs of naïve mice. Unbiased clustering based on differential chromatin accessibility identified 10 distinct clusters (Figure 7A). Integrating this scATAC-seq dataset with our timepoint-specific scRNA-seq data, which includes naïve, bystander, and infected cells, revealed that variations in chromatin organization before infection align closely with the diverse transcriptional phenotypes observed during tuberculosis infection. Using gene scores, we inferred the potential gene expression profiles for each cell in the scATAC-seq sample, based on the accessibility of regulatory elements adjacent to each gene. We then performed data integration with the scRNA-seq dataset, as previously described 96 (Figure 7B). Given that our scATAC-seq sample comprised only cells from naïve mice, we anticipated that the inferred gene expression profiles from the scATAC-seq dataset would predominantly align with those of naïve cells from our scRNA-seq dataset. Surprisingly, we found that the inferred gene expression of cells from cluster C7 mirrored that of the pro-inflammatory CD38 + subsets from our scRNA-seq data (Figure 7B and 7C), which by transcriptional profiling are not present in naïve mice (Figure 2A). In contrast, clusters C6 and C8 aligned with CD38 - TR-AMs, while cluster C5 correlated with Mki67 + AMs (Figures 7B and 7C). To validate that cluster C7 represented CD38 + TR-AMs and not monocyte-derived AMs, we probed for open chromatin within the promoter regions of monocyte markers Mafb and Ly6a , whose expression is restricted to monocyte-derived macrophages in our scRNA-seq dataset, as noted earlier (Figure 1F). We found high levels of open chromatin for these markers only in monocyte-derived cells, but not in cluster C7 (Supp. Figure 6B). To further understand why the inferred gene expression of cluster C7 aligns with pro-inflammatory populations in scRNA-seq, we first assessed transcription factor dynamics, performing marker peak and motif enrichment analysis (FDR 0.5) to identify transcription factor binding sites (TFBS) that are enriched across the different scATAC-seq clusters. Cluster C7 exhibited highly significant enrichment for binding sites of transcription factors such as Smarcc1 , Bach1 , and Rela , known to drive pro-inflammatory gene activation in macrophages 97-99 (Figure 7D). We further validated the expression of these TFs in our scRNA-seq dataset and found them to be uniquely expressed by CD38 + pro-inflammatory macrophages following Mtb infection (Figure 7E). Finally, our analysis of open chromatin peaks within the regulatory regions (± 5k from TSS) of pro-inflammatory genes that define CD38 + AMs, such as Nos2, Cd38, Slc7a11, Ccl5, Ptgs2, Il1b, and Cxcl 2, revealed higher open chromatin in cells from cluster C7 compared to clusters C6 and C8, further validating that CD38 + TR-AMs are pre-primed for a pro-inflammatory response (Supp Figure 6C). Overall, our analysis demonstrates that CD38 + TR-AMs are present in naïve mouse lungs before infection, but are transcriptionally quiescent and have an epigenetic profile markedly distinct from their CD38 - TR-AM counterparts. These CD38 + TR-AMs have increased chromatin accessibility in promoter regions of pro-inflammatory genes, with significant enrichment of transcription factor binding sites that have the potential to drive pro-inflammatory macrophage activity and control Mtb growth. Our findings support the hypothesis that the response of different TR-AM subsets to Mtb infection is largely predetermined by their intrinsic chromatin organization prior to infection. Discussion Our understanding of immune protection against tuberculosis largely comes from studying immune failure 31 , 100 . Experimental infections in immune deficient mouse strains have informed us which pathways, when compromised, increase susceptibility to infection and disease 101 . Similarly, several mutations in the human population have been linked to increased incidence or severity of disease 102 , 103 . However, if disease outcome is determined by the biology of the host macrophages, and if different macrophage populations are responsible for control or promotion of bacterial growth, focusing solely on immune failure offers a limited view of disease control. This focus has resulted in our reliance on Interferon-g Release Assays (IGRA), Mycobacterial Growth Inhibition Assays (MGIA) and similar biological readouts for vaccine development, which have proven to be non-predictive of immune protection 27 , 28 . Our current study uses a mouse challenge model with fluorescent fitness reporter bacteria to define and characterize different macrophage populations in the infected mouse lung. We've previously found that recruited pro-inflammatory monocyte-derived IMs effectively control Mtb, 6 , 8 , 9 while AMs exhibit diverse phenotypes. We posit that the variability among these macrophage subsets significantly influences disease progression in tuberculosis. In early infection, Mtb primarily resides in CD38 − AMs, which show a muted response, as noted by Rothchild et al 10 . As infection progresses, the AM landscape changes significantly with the recruitment of moAMs and activation of a subset of TR-AMs, leading to increased bacterial control. These macrophage subsets transition the lung environment from immune homeostasis to a pro-inflammatory state, effectively curtailing Mtb growth. The shifts in macrophage phenotypes are rooted in their intrinsic epigenetic programming, as revealed by ScATAC-seq analyses. CD38 + TR-AMs, are already present in naïve lungs, exhibit chromatin landscapes predisposed for a pro-inflammatory response, indicating epigenetic priming as a key factor in their infection response. The diverse chromatin organization of the different TR-AMs subsets before infection suggests the potential for manipulating their epigenetic programs, opening new avenues to enhance macrophage function in TB. Furthermore, examination of post-infection phenotypic changes in AM subsets, particularly after intrapulmonary BCG administration, provides crucial insights into potential strategies for reprogramming these macrophages to our advantage. Our focus was not on promoting BCG as a long-term vaccination strategy, but rather on understanding how transient changes in macrophage function post-BCG administration can inform potential therapeutic targets. We observed transcriptional shifts resulting in the polarization of CD38 − TR-AM_3 towards the CD38 + TR-AM phenotype and increased inflammatory activation of monocyte-derived macrophages in BCG-treated mice. These transcriptional responses are associated with augmented expression of pathways related to intracellular trafficking and lysosomal/autophagic functions, suggesting that intranasal BCG administration triggers the pre-activation of genetic programs inherent to the epigenetic profile of pro-inflammatory TR-AM subsets. This promotes a phenotype more effective in restricting Mtb replication. Additionally, BCG also increases the recruitment of monocyte-derived macrophages, resulting in fewer unresponsive CD38 − TR-AMs being infected with Mtb, further strengthening the lung myeloid populations' ability to counter the Mtb challenge. These results extend the recent reports that BCG reprograms lung AMs to better control Mtb 14 , 17 . While our findings, conducted in SPF mice, do not fully capture the complex genetic and environmental influences found in human populations, the changes in macrophage phenotype observed during infection and post-BCG vaccination offer a clear path towards improved tuberculosis control. We propose that the dynamic nature of these macrophage subpopulations plays a major role in the early events following Mtb infection and throughout the course of the disease. Through understanding the underlying molecular mechanisms and the broader immune context driving the distinct responses of CD38 + and CD38 − AMs to Mtb infection, we could guide the screening of therapeutics aimed at improving macrophage control of Mtb early in infection. However, factors such as genetic diversity, nutritional status, co-infections, and early-life BCG vaccination can significantly alter immune responses; therefore, these results will require validation under more complex conditions in humans. The significant shifts in macrophage phenotypes we've observed, especially in the context of BCG vaccination and immune function, emphasize the functional resolution of the analytical tools employed in this current study. The WGCNA method, a focal point of this and ongoing studies, has produced gene expression modules that functionally categorize various macrophage subpopulations, in both mouse and NHP infections. These modules, alongside newly identified CD markers, facilitate integration with skin and lung challenge approaches for more accurate phenotype identification of tissue-resident and monocyte-derived macrophages in relation to disease or vaccination status. We believe this represents a viable avenue to the development of predictive biomarkers for immune protection. MATERIALS AND METHODS Mtb and BCG strains The parental strain employed for all experiments was Mycobacterium tuberculosis Erdman (ATCC 35801). Fluorescent reporter strains including smyc′::mCherry, smyc′::mCherry/hspx’::GFP, and hsp60′::GFP have been reported 35 , 104 – 106 . Both the M. tuberculosis strain and BCG (Pasteur) were cultivated at 37°C until they reached the mid-log phase in MiddleBrook 7H9 broth enriched with 10% OADC (Becton, Dickinson and Company), 0.2% glycerol, and 0.05% tyloxapol (Sigma-Aldrich). For the selection of fluorescent strains, Hygromycin B (50 mg/ml) was utilized. For mouse infections, bacterial aliquots were prepared in 10% glycerol, titrated, and preserved at − 80°C, following the protocol detailed in Pisu et al., 2023 18 . Mice C57BL/6J WT, B6.129P2-Nos2tm1Lau/J (NOS2 −/− ), B6.129S7-Ifngtm1Ts/J (IFNγ −/− ), B6.129S7-Rag1tm1Mom/J (Rag1 −/−) mice were purchased from The Jackson Laboratory. The mice used in this study were 6–8 wk old. All mice were maintained in a specific pathogen–free animal biosafety level 3 facility at Cornell University. Animal care was in accordance with the guidelines of the Association for Assessment and Accreditation of Laboratory Animal Care. All animal procedures were approved by the Institutional Animal Care and Use Committee of Cornell University. Mice infection and lung cells isolation For Mtb infections, mice were anesthetized and intranasally inoculated with 1.5X10 3 CFUs of the Erdman strains ( smyc′ ::mCherry, hspx′ ::GFP/ smyc′ ::mCherry, or hsp60′ ::GFP) resuspended in 30 µl of PBS containing 0.05% Tween 80. The inoculum dose was verified by plating various dilutions of the bacterial stocks used for infection on 7H10 agar plates supplemented with OADC Enrichment and glycerol. These plates were incubated at 37°C, and after 3 weeks, colonies were counted. At 2, 3, 4, and 6 weeks post-infection (w.p.i.), mice were euthanized. Lungs were aseptically removed and immersed in PBS containing 5% FBS and Collagenase IV (250U/mL). To preserve the gene expression profiles of both the host and bacteria, samples were immediately processed using a GentleMACS tissue dissociator (Miltenyi Biotec) and maintained on ice. The dissociated lung material was subsequently strained through a 70-µM mesh, and red blood cells were lysed using ammonium-chloride-potassium (ACK) lysis buffer (Lonza) 18 . For BCG vaccinations, mice received an intranasal dose of 2x10 6 CFU of BCG (Pasteur) bacilli in 30 µL of PBS containing 0.05% Tween 80. Post-infection, these mice were housed in pathogen-free cages at a biosafety level 2 facility at Cornell University, in preparation for re-infection with M. tuberculosis Erdman. After a period of two months (60 days) from the initial vaccination, these mice underwent a secondary intranasal challenge. This challenge involved approximately 1.5x10 3 or 5x10 3 CFU of either hspx′ ::GFP/ smyc′ ::mCherry or smyc′ ::mCherry Mtb Erdman respectively, with the bacteria also resuspended in 30 µL of PBS containing 0.05% Tween 80. At specified intervals post-infection, specifically at 2 and 4 weeks, the lungs of these mice were aseptically removed and immersed in a solution of PBS containing 5% FBS and 250U/mL of Collagenase IV. The harvested lung tissues were then processed for subsequent scRNA-seq or flow cytometry analyses. Sorting of the murine lung suspensions for scRNA-seq analysis Infected populations To generate single cell suspensions for cell sorting, we followed the steps 4–18 of the previously published protocol 107 . In brief, cells from infected mice (n = 5/timepoint) were washed in PBS containing 5% FBS, resuspended in sorting buffer (PBS, 1% FBS, 5 mM EDTA, and 25 mM Hepes), filtered through a 40-µM strainer, and sorted. Throughout the sorting, samples were kept at 4°C and directly collected into Cell Staining Buffer (BioLegend). Mice infected with either smyc′ ::mCherry or hsp60′ ::GFP were used as a control to define the sorting gates for the hspx′ ::GFP/ smyc′ ::mCherry-infected cells. For the BCG analysis, we followed the same protocol sorting n = 5 mice. Bystander populations Single-cell suspensions from mice infected with smyc′::mCherry (n = 3 / timepoint) were incubated for 20 min with fluorophore-bound CD45 antibodies (104; BD). After two PBS washes, samples were resuspended in the sorting buffer, filtered via a 40-µM strainer, and sorted. During sorting, samples were consistently held at 4°C and collected directly into Cell Staining Buffer (BioLegend). scRNA-seq libraries preparation and sequencing Sample Preparation and Staining Sorted cells were centrifuged at 500 g for 5 min and then resuspended in 50 µl of cell staining buffer containing 0.25 µg of TruStain FcX PLUS (BioLegend), followed by a 10 min incubation at 4°C. An ADT plus HTO antibody cocktail mix (50 µl) was then added to the samples, and the cells were further incubated for 30 min at 4°C. After two washes in cell staining buffer, differentially tagged samples (e.g., hspx′ ::GFPhigh/ hspx′ ::GFPlow) were combined and resuspended in 1× Dulbecco’s PBS. The samples were then fixed by slowly adding ice-cold methanol to a final concentration of 90% (vol/vol) and stored at − 20°C overnight. Sample Rehydration and mRNA Library Preparation Post-fixation, the samples were brought out of the BSL3 facility, equilibrated on ice for 15 min, and washed twice with rehydration buffer (1× Dulbecco’s PBS with 1.0% BSA [Thermo Fisher Scientific] and 0.5 U/µl RNase Inhibitor [Sigma-Aldrich]). The cell count was determined prior to loading onto the 10× chip. For mRNA library preparation, we adapted the 10× protocol (CG000206 Rev D), making a minor alteration in step 2.2. Specifically, we incorporated 1 µl of ADT and HTO additive primers (0.2 µM stock) following the method described by Stoeckius et al 108 . HTO and ADT libraries were prepared according to BioLegend's standard protocols. Library Generation and Sequencing The mRNA, HTO, and ADT libraries underwent quality control assessment using an Agilent Fragment Analyzer. Their concentrations were determined using the QX200 digital PCR system from Bio-Rad. Libraries were pooled in the same sequencing run at specific ratios: 90% mRNA, 5% ADT, and 5% HTO. Sequencing was performed on the NextSeq2000 (Illumina) using the 50-bp P3 NextSeq kit. The cycle distribution was: read 1 (28 cycles), i7 index (8 cycles), and read 2 (52 cycles). Sequencing depth exceeded 50,000 reads/cell. scATAC-seq nuclei isolation, library preparation and sequencing Murine naïve lung sorting was performed as described in Pisu et al 9 , 18 . For nuclei isolation, sorted cells were centrifuged at 300 rcf for 5 minutes at 4°C and then resuspended in 150uL of PBS supplemented with 0.04% BSA. A 100µL aliquot of this cell suspension was transferred to a 0.2mL flat-cap tube, centrifuged again under the same conditions, and subsequently resuspended in 50uL of Lysis Buffer (containing 10mM Tris-HCL, 10mM NaCl, 3mM MgCl2, 0.1% Tween 20, 0.1% Nonidet P40 Substitute, 0.01% Digitonin, and 1% BSA). This was incubated on ice for 4 minutes. Post-incubation, 50µL of Wash Buffer (comprising 10mM Tris-HCL, 10mM NaCl, 3mM MgCl2, 1% BSA, and 0.1% Tween 20) was added to the lysed cells. The nuclei suspension was then centrifuged at 500 rcf for 5 minutes at 4°C. The nuclei were washed with 45µL of a 1:20 dilution of the Nuclei Buffer (10x Genomics, PN-2000153/2000207) and centrifuged again using the same conditions. Finally, the isolated nuclei were resuspended in a volume of Diluted Nuclei Buffer to obtain a concentration ranging from 3080 to 7700 nuclei/µL. This was used as input for the 10X protocol (CG 000209 Rev D) targeting a recovery of 10,000 nuclei. The transposition reaction and library construction were performed following the protocol from 10X (CG000209 REV D). Sequencing was conducted on a NextSeq 500 with the parameters: Read 1N: 50 cycles, i7 index: 8 cycles, i5 index: 16 cycles, and Read 2N: 50 cycles. Antibodies Used for scRNA-seq For our scRNA-seq timepoint experiments, we used a range of TotalSeq (BioLegend) murine antibodies in our antibody cocktail mix, each at a concentration of 0.5 µg/sample. These antibodies included SiglecF (custom-made, clone S17007L), CD64 (cat. # 139325), Ly6G (cat. # 127655), CD11c (cat. # 117355), CD14 (cat. # 123333), Ly6G-Ly6C (cat. # 108459), CD63 (cat. # 143915), F4/80 (cat. # 123153), CD38 (cat. # 102733), TLR4 (cat. # 117614), CD11b (cat. # 101265), CD16/32 (cat. # 101343), CD86 (cat. # 105047), CD1d (cat. # 123529), CD3 (cat. # 100251), CD4 (cat. # 100569), and CD8a (cat. # 100773). In addition, for hashing purposes, we used BioLegend's Hashtag 1 murine (cat. # 155801), Hashtag 2 murine (cat. # 155803) antibodies. Flow cytometry analysis Lung cell suspensions were counted and incubated for 30 min in the dark at room temperature with fluorophore-conjugated antibodies, washed twice with PBS 1×, and fixed in 4% paraformaldehyde. Antibody panels and Fluorescence Minus One controls were generated as appropriate. For this study, we used fluorochrome-conjugated mAbs specific to mouse SiglecF (E50-2440; Becton Dickinson), CD64 (X54-5/7.1; BioLegend), MerTK (2B10C42; BioLegend), CD38 (90; Biolegend) and CD45 (104; Becton Dickinson), along with the following reporter strains: smyc′::mCherry (mCherry), hsp60′::GFP (GFP), and hspx′::GFP/smyc′::mCherry. Cells were analyzed with a Symphony A3 (BD Biosciences). Data were analyzed using FlowJo software (version 10.9; BD). Quantification of bacterial loads At 2 weeks post-infection (for BCG-vaccinated) and at 4 weeks post-infection (for B6.129P2-Nos2tm1Lau/J (NOS2 −/− )) mice were sacrificed and the lung lobes homogenized in PBS containing 0.05% tyloxapol (Sigma-Aldrich). Bacterial loads were determined by plating serial dilutions of the homogenates on 7H10 agar. Plates were incubated at 37°C and colonies enumerated 3–4 weeks after. Data Analysis Data Acquisition and QC Sequencing data derived from each run underwent processing using distinct software tailored to the library type. mRNA libraries were processed using the CellRanger software (v. 6.0) from 10X Genomics and the mouse mm10 genome (GRCm38). The ADT and HTO libraries were processed using CITE-Seq-Count (v. 1.4.3), available at https://hoohm.github.io/CITE-seq-Count/ . This processing yielded raw count matrices for both mRNA and proteins. Downstream data analysis was conducted in Seurat (v. 4.3), following methods outlined by Stuart et al 109 . Cells with less than 200 unique genes or with over 30% of the total reads belonging to mitochondria were filtered out. The HTOs multiplexed samples were then demultiplexed using the MULTIseqDemux() function in Seurat 110 . This step allowed for the removal of doublets and empty droplets and ensured the correct assignment of identities to each sample. Single-Cell RNA Sequencing Data Preprocessing : All scRNA-seq datasets from individual timepoints were preprocessed using Seurat's regularized negative binomial regression, where both the number of counts and the percentage of mitochondrial reads were regressed out, as per Hafemeister and Satija 111 . For analysis of the macrophage populations in the uninfected naïve lungs we used the previously generated datasets from Pisu et al 9 . Metadata columns, namely “ Timepoint ”, “ Batch ”, “ Infection Status ”, and “ Vaccination Status ”, were added to each dataset prior to subsequent steps. Data Merging and Integration : Datasets were combined into a unified Seurat object. The raw counts contained in the RNA slot of the merged object were used as an input for Harmony integration 112 . Raw counts underwent log-normalization, followed by the identification of the top 3,000 variable genes. These genes were scaled and centered. PCA was then performed on these values. Integration in Harmony incorporated “ Batch ”, “ Infection Status ”, “ Timepoint ”, and “ Vaccination Status ” as covariates. Cluster Detection and Annotation : Using the Harmony-aligned embeddings, graph-based cluster detection was achieved utilizing principal components (PCs) that had been marked statistically significant by the jackstraw method 113 . For community detection, we employed the Louvain algorithm 114 . Cell types for each identified cluster were annotated using both reference-based 115 and canonical marker genes. Trajectory and Pseudotime Analysis : This analysis was carried out in Monocle (v3.0) 116 . The integrated Seurat object was converted using the SeuratWrappers package in R (v. 0.2.0). For unbiased trajectory and pseudotime analysis of the macrophage populations, all cells classified as macrophages were assigned to the same partition, and trajectory/pseudotime analysis was conducted as previously described 117 . White circles indicate the root origins of the trajectory, grey circles indicate destination fates and black circles indicate branching points. Generation of a force layout embedding (FLE) To visualize infected and bystander cells in a force layout embedding (FLE) we used the methods described in Waddington-OT 76 . Briefly, using the Pegasus library (pegasus), a PCA was performed on the expression data using the function pg.pca(). This was followed by the determination of nearest neighbors using pg.neighbors(). Subsequently, a diffusion map was generated through the function pg.diffmap() to visualize the transition between cellular states. To visualize the data, the FLE algorithm implemented in Pegasus was employed by invoking pg.fle(). The resulting 2D coordinates were then extracted for further visualization. A scatter plot was generated using matplotlib to represent cells in the 2D space derived from the FLE calculations. Cells were color-coded based on their respective timepoints (weeks). Time-dependent pathway analysis For our time-dependent pathway analysis, we employed the Tempora package 74 . First, the integrated Seurat object was subset to only contain macrophage clusters. Subsequently, the “ImportSeuratObject()” function was employed to prepare the data for Tempora, where clusters and timepoints were explicitly defined. Given the necessity of pathway enrichment analysis, gene set files were fetched from the BaderLab online resource. We specifically selected gene set files that incorporated all pathways, excluding those inferred from electronic annotations (IEA). The downloaded gene matrix transposed (GMT) file was then leveraged for pathway enrichment calculations using the GSVA method with “CalculatePWProfiles()”. To construct the cellular trajectory, the “BuildTrajectory()” function was applied, with the number of principal components set to 11 and a statistical significance threshold of < 0.05. The trajectory visualization was achieved using the “PlotTrajectory()” function, and subsequently, generalized additive models (GAMs) were employed to identify significant time-varying pathways through the “IdentifyVaryingPWs()” function. The temporal dynamics of these pathways were illustrated using a custom ggplot function, “ggplotVaryingPWs()” for improved visualizations. WGCNA co-expression analysis WGCNA is an analytical pipeline used extensively by developmental biologists, that support the iterative, unbiased assembly of gene expression modules that define cell populations of interest. To perform weighted gene co-expression network analysis on our scRNA-seq datasets, we employed the scWGCNA package 45 . Pseudocells were computed using the “calculate.pseudocells()” function from the scWGCNA package. This method aggregates single cells into pseudocells to reduce the complexity of the dataset. A fraction of 0.2 of the cells were used as seeds, and for each seed, 10 nearest neighbors were aggregated based on the Harmony dimensional reduction. For each analysis, the selected single cell data was normalized using the “LogNormalize()” method with a scaling factor of 10,000. Variable features were identified using the Variance Stabilizing Transformation (VST) method, and the top 3,000 features were retained. Subsequently, pseudocells (pcells) and the variable genes (var.genes) identified in the previous step were used in the “scWGNA()” function to identify modules of co-expressed genes. Membership tables were inspected to understand the genes belonging to each module, and average expressions of each co-expression module per cell were analyzed. Eigengenes for the co-expression modules were computed using the “scW.eigen()” function. This function collapses the expression profile of each module into a single representative profile known as the module eigengene. The average expression of each module was then visualized on a UMAP plot using a customized “scW.p.expression()” function for improved visualization. Differential Abundance Testing with Milo We used the neighborhood-based statistical framework “Mylo” 77 to test for changes in the abundance of infected and bystander macrophage populations across timepoints. To determine the best parameters for running the model both the neighborhood cell distribution and the distribution of uncorrected P-values were assessed. A k-nearest neighbors (k-NN) graph was constructed on the data using the “buildGraph()” function, with k set to 10, and using Harmony as the dimensionality reduction method. The neighborhoods were then defined on this graph using the “makeNhoods()” function, using the same k and d parameters as before. Subsequently, cells were counted within these neighborhoods based on their originating samples using the “countCells()” function. The neighborhoods were tested for differential abundance using the “testNhoods()” function. This test took into account a design matrix (consisting of a sample identifier and a variable for the timepoint) and the Harmony dimensions. Results were then sorted by the spatial False Discovery Rate (SpatialFDR). The neighborhood graph was further constructed using the “buildNhoodGraph()” function. Visualizations of the neighborhood graph highlighting the differential abundance results were then generated. Following this, neighborhoods were annotated based on cell identity, and a histogram was plotted to visualize the fraction of identified cells. Neighborhoods with an identity fraction less than 0.4 were labeled as "Mixed". Bee swarm plots were generated to further visualize the data based on these identities. Finally, neighborhoods were grouped using the “groupNhoods()” function, with a max.lfc.delta of 2. The resultant grouped neighborhoods were visualized on a UMAP plot and further explored through bee swarm plots based on their LFC differences. scCODA We used scCODA to investigate changes in cell composition across different timepoints during Mtb infection. The cell count data was reshaped to match the format required for scCODA. This involved mapping the original timepoints to new categories (e.g., “2 Weeks” and “3 Weeks”' to “Early Timepoint”, “4 Weeks” and “6 Weeks” to “Late Timepoint”) and summing counts when necessary to consolidate the data into these new categories. The reshaped data was loaded into a pandas DataFrame, where each column represented a different timepoint and rows represented individual cell types. This DataFrame was then converted to an AnnData object, which is the data structure required by scCODA for compositional data analysis. The analysis focused on comparing the composition of macrophage subsets at these defined timepoints to identify significant changes in their proportions that could be linked to infection progression. The compositional analysis model was created using scCODA's “CompositionalAnalysis” class, specifying “Timepoint” as the covariate and “automatic” as the reference cell type. The model was then run to perform Hamiltonian Monte Carlo (HMC) sampling and generating posterior distributions for the compositional changes between the specified groups. Results from the scCODA model were summarized to determine which cell types showed significant changes in proportion relative to the reference group across the study's timepoints. Pathway enrichment analysis Pathway enrichment analysis was performed using G::profiler 118 . For each analysis, we created an ordered by fold change (FC) (for DGE) or membership value (for scWGCNA modules) list of genes as a query, selecting only those genes where adjusted P value (p-adj) < 0.05. The analysis was performed using the g:SCS method for multiple testing correction, the gene ontology (GO), KEGG and Reactome databases as a data source, and the default settings for the other parameters in G::profiler. Only pathways enriched with p-value < 0.05 were considered statistically significant. Manual exploration of the gene lists for each analysis has also been performed to identify relevant themes for genes whose function is described in the literature (e.g., Small GTPase signal transduction). For this purpose, we only considered genes whose FC was absolute > 1.5 and p-adj 0.4. scATAC-seq chromatin accessibility analysis We utilized the ArchR software 96 to perform integrative single-cell chromatin accessibility analysis. For the analysis we used the precompiled version of the mm10 genome in ArchR. Quality filtering parameters for data pre-processing included filtering out cells with TSS enrichment scores below 4 and less than 1000 unique fragments. As an additional step for quality control, we performed doublet identification and removal. Doublet scores were added to the Arrow files using the “addDoubletScores()” function with parameters knnMethod = UMAP, k = 10 and LSIMethod = 1. Post doublet identification, cells suspected to be doublets were filtered using “filterDoublets()”, reducing the initial nuclei count from 9371 to 8493. Subsequently, dimensionality reduction has been performed using the Iterative Latent Semantic Indexing (LSI) on the insertion count matrix with the “addIterativeLSI()” function in ArchR for 4 iterations. Clustering was performed on the IterativeLSI dimensions using Seurat with a resolution of 0.7. UMAP embeddings were added with the addUMAP() function using parameters nNeighbors = 30, minDist = 0.5 and metric = cosine. The UMAP was plotted and color-coded by clusters. To identify marker genes for each cluster, gene expression for marker genes was estimated from chromatin accessibility data by using gene scores. A gene score is considered as a prediction of how highly expressed a gene will be based on the accessibility of regulatory elements in the vicinity of the gene 96 . To create the gene scores, we used distance-weighted accessibility models as defined in ArchR and implemented in the function “addGeneScoreMatrix()”. Marker genes were then identified for each cluster using the Gene Score Matrix with a Wilcoxon test method. The markers for each cluster were filtered using an FDR = 0.58. Imputed weights were added using the function “addImputeWeights()” to visualize the marker genes e.g., " Mafb " and “ Ly6a ” on the UMAP. To visualize the local chromatin accessibility around specific genes on a per cluster basis, the “plotBrowserTrack” function was employed, considering 5,000 base pairs both upstream and downstream of the start of the genes of interest. Integration of scRNA-seq and scATAC-seq data Single-cell timepoint RNA-seq data (scRNA-seq) was utilized for integration with ATAC-seq data. Unconstrained integration, a completely agnostic approach that takes all of the cells in the scATAC-seq experiment and attempt to align them to any of the cells in the scRNA-seq experiment, was used. The “addGeneIntegrationMatrix()” function was used to generate a gene integration matrix, which was named "GeneIntegrationMatrix". For visualization, a palette was derived from the scRNA-seq data's cell type categories. An embedding plot was generated using the “plotEmbedding()” function, colored by predicted cell groups to label and visualize the scATAC-seq clusters with the cell types predicted from our scRNA-seq dataset. Transcription Factor (TF) Analysis For TF analysis, the following steps were performed: 1) A reproducible peak set was determined using the “addReproduciblePeakSet()” function, with MACS2 as the peak caller 119 . The “getMarkerFeatures()” function was employed to identify marker peaks unique to an individual cluster or a small group of clusters in an unsupervised fashion, using the above calculated peak matrix and accounting for biases such as TSS Enrichment and fragment counts. Significant marker peaks were determined with criteria set at FDR = 0.58. 2) To assess whether marker peaks or differential peaks were enriched for binding sites of specific transcription factors, we performed motif and feature enrichment analysis. We annotated the ArchR project with motif information using the “addMotifAnnotations()” function, employing the "cisbp" motif set. Analysis of enrichment of motifs within marker peaks was performed with the “peakAnnoEnrichment()” function, using the following criteria FDR = 0.5. The enriched transcription factor binding sites (TFBS) for each scATAC-seq cluster were then visualized in a ranked scatter plot. Statistical Analysis scRNA-seq Differential expression analysis was performed using the nonparametric Wilcoxon rank-sum test as implemented in Seurat 109 . Only genes with FDR < 0.05 between two comparisons were considered statistically significant. Unless otherwise specified, the Wilcoxon rank-sum test followed by FDR correction has also been used to compare the distribution of a specific gene expression among two groups of cells in different plots and visualizations. For plots where the distribution of values (eg: CD38 protein level) have been compared, for each timepoint, across two conditions (eg: Bystander vs Infected) pairwise t-tests has been performed to determine which pairs of groups are different from each other. The p-values from the pairwise t-tests are adjusted for multiple comparisons using the Benjamini-Hochberg (BH) method. The Kruskal-Wallis test has been used to analyze the differences among group medians in a sample (eg: if the medians of protein expression levels differ significantly across clusters). The Dunn's test has been used following the Kruskal-Wallis test to determine which specific groups' distributions differ from each other and the Bonferroni method has been used to adjust p-values for multiple comparisons. In addition, effect sizes for differences in expression between groups were quantified using Cliff's Delta, which measures the probability that a randomly selected value from one group will be greater than a randomly selected value from the other group. Cliff's Delta values range from − 1 to 1, where 0 indicates no effect, and values closer to -1 or 1 indicate stronger effects. The Cliff's Delta was computed using the cliff.delta() function from the “effsize” R package. Data integration and batch effect removals were performed with Harmony as previously described 112 . ADT and HTO data were normalized using a centered log ratio transformation, implemented in the function “NormalizeData()” with normalization.method = ‘‘CLR,’’ in Seurat. RNA counts were log-normalized and scaled before PCA and data integration with Harmony. Data visualizations were generated on the log-normalized counts for the feature plots, scatter plots, and violin plots. Heatmaps and dot plot charts were generated on the scaled expression data, as per default in Seurat. scATAC-seq Marker genes distinguishing the different cell clusters were identified using the getMarkerFeatures() function on the GeneScoreMatrix. The Wilcoxon rank-sum test was utilized to assess the significance of predicted differential gene expression between cell clusters. Biases such as TSS Enrichment and fragment counts (log10(nFrags)) were accounted for during the analysis. Genes were considered as markers based on criteria set at a FDR of = 0.58. The FDR was controlled using the Benjamini-Hochberg procedure. To identify marker peaks associated with different cell clusters, a Wilcoxon rank-sum test was employed on the PeakMatrix, accounting for biases such as TSS (Transcription Start Site) Enrichment and fragment counts. Marker peaks were considered significant based on criteria set at a FDR of = 0.58. The FDR was controlled using the Benjamini-Hochberg procedure to reduce the chances of false positives arising from multiple testing. Transcription factor (TF) motif enrichment within the identified marker peaks was assessed using the peakAnnoEnrichment() function. This function performs hypergeometric enrichment of a given peak annotation within the defined marker peaks. Flow cytometry analysis MFI of the GFP signal for the hspx-high and hspx-low populations at different timepoints was calculated using the software FlowJo (v. 10.9). CFU data In this study, CFUs were quantified and analyzed to assess differences in bacterial burden between different conditions/treatments. Log10 transformations were applied to each CFU count prior to further analysis. To ensure the appropriateness of parametric tests, the data were first checked for normality using the Shapiro-Wilk test, a method suited for small sample sizes. This test evaluates the hypothesis that a sample comes from a normally distributed population, which is a critical assumption for the application of a two-sample t-test. Further, to assess the equality of variances between the treatment groups, Levene’s test was performed. Upon confirming that the assumptions of normality and homogeneity of variances were satisfied, a two-sample t-test was conducted to determine if there were statistically significant differences in the mean log-transformed CFU counts between the groups. The results of the t-test, including the p-values, were used to infer the statistical significance of the differences observed. The CFU plots presented in the results section are annotated with summary statistics including the mean and standard deviation (SD) of each group, along with the sample size (n), and the p-value from the t-test. Lead Contact and Materials Availability Further information and requests for resources, reagents and protocols should be directed to and will be fulfilled by the Lead Contact, David G. Russell ( [email protected] ). This study did not generate new unique reagents. Plasmid, bacterial and mouse strains, antibodies and other reagents and protocols used in this study will be available upon request. Declarations Data Availability: The datasets supporting the conclusion of this study are available in the Gene Expression Omnibus (GEO) under accession numbers: GSE245950 (scRNA-seq) and GSE245836 (scATAC-seq). The scRNA-seq datasets for the 3-week timepoint and naïve lung were previously published and are available under accession number: GSE167232. Acknowledgements: All animal protocols were approved by the Institutional Animal Care and Use Committee of Cornell University. The work was supported by grants AI134183, AI155319, and AI162598 to DGR from the National Institutes of Health, USA. We are grateful to Jordan Rhen for technical and organizational support. Author Contributions: D. Pisu and D.G. Russell designed the study. D. Pisu performed experiments. D. Pisu analyzed the scRNA-seq and scATAC-seq data. D. Pisu, J. Mattila and D.G. Russell analyzed and interpreted results. D. Pisu, J. Mattila and D.G. Russell drafted and edited the manuscript. Declaration of Interests: The authors declare no competing interests. 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Mol Microbiol 73:844–857. 10.1111/j.1365-2958.2009.06801.x Granja JM, Corces MR, Pierce SE, Bagdatli ST, Choudhry H, Chang HY, Greenleaf WJ (2021) ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat Genet 53:403–411. 10.1038/s41588-021-00790-6 Liu W, Wang Z, Liu S, Zhang X, Cao X, Jiang M (2023) RNF138 inhibits late inflammatory gene transcription through degradation of SMARCC1 of the SWI/SNF complex. Cell Rep 42:112097. 10.1016/j.celrep.2023.112097 Alam Z, Devalaraja S, Li M, To TKJ, Folkert IW, Mitchell-Velasquez E, Dang MT, Young P, Wilbur CJ, Silverman MA et al (2020) Counter Regulation of Spic by NF-kappaB and STAT Signaling Controls Inflammation and Iron Metabolism in Macrophages. Cell Rep 31:107825. 10.1016/j.celrep.2020.107825 Saliba DG, Heger A, Eames HL, Oikonomopoulos S, Teixeira A, Blazek K, Androulidaki A, Wong D, Goh FG, Weiss M et al (2014) IRF5:RelA interaction targets inflammatory genes in macrophages. 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Supplementary Files Supp.Table1.xlsx Supplementary Table 1 Supp.Table2.xlsx Supplementary Table 2 Supp.Table3.xlsx Supplementary Table 3 Supp.Table4.xlsx Supplementary Table 4 Supp.Figure1a.pdf Supp. Figure 1A: Visualization of Cell Annotations Using the ImmGen Reference. The plot visualizes the annotations of cells from the scRNA-seq timepoint dataset, leveraging the ImmGen reference dataset to assign cell type labels. The SingleR method 115 has been employed to match the gene expression profile of each cell in the scRNA-seq dataset with those from the ImmGen reference, a comprehensive database containing finely annotated immune cell types. The heatmap displays the matching scores for each cell in the scRNA-seq dataset against potential cell types from ImmGen. Each column in the heatmap represents a cell from the dataset, while the rows correspond to the potential cell type labels from ImmGen. The cells are grouped based on their inherent clusters from the scRNA-seq dataset. The color intensity in the heatmap indicates the degree of match between the cell's gene expression profile and the reference profile of the cell type in ImmGen. Yellow shades suggest a stronger match, indicating higher confidence in the assigned cell type label. Supp. Figure 1B: DotPlot illustrating genes involved in monocyte-to-macrophage differentiation processes. Each dot's size corresponds to the percentage of cells within a cluster expressing a given gene, while the color intensity indicates the average expression level, ranging from red (low expression) to blue (high expression). Clusters include different subsets of tissue-resident macrophages and monocyte populations. Supp.Figure2a.pdf Supp. Figure 2A: Flow cytometry analysis of the Mtb smyc’::mCherry/hspx’::GFP reporter strain at 2 wpi in Rag1 -/- and IFN-Y -/- KO mice. Supp. Figure 2B: CD11b Staining Across Timepoints and Infection Status. This figure presents CD11b staining levels in macrophages, stratified by infection status and timepoint. Using violin plots overlaid with box plots (representing the interquartile range and the median), the distribution of staining levels is visualized. Each facet represents a distinct timepoint, and within each, the CD11b protein levels for each infection status, barring 'Uninfected', are shown. Color-coding differentiates infection statuses. Adjusted p-values from pairwise t-tests are annotated within each facet. Supp. Figure 2C: Pathways upregulated at late timepoints. The plots illustrate temporal shifts in pathway expression over inferred time for different clusters. Each cluster is represented by distinct colored points, with the average pathway expression levels depicted on the y-axis and inferred time on the x-axis, ranging from an early to a late timepoint. A fitted line, shown in red, captures the trend of pathway expression over time. Positive regulation of macrophage fusion and regulation of secretion of lysosomal enzymes are among the pathways with increased expression in CD38 + macrophages over time. Supp. Figure 2D: Pathways upregulated in early infection. The plots illustrate temporal shifts in pathway expression over inferred time for different clusters. Each cluster is represented by distinct colored points, with the average pathway expression levels depicted on the y-axis and inferred time on the x-axis, ranging from an early to a late timepoint. A fitted line, shown in red, captures the trend of pathway expression over time. Regulation of receptor recycling, mitotic actomyosin contractile ring assembly and positive regulation of dense core granule transport are among the pathways with elevated expression in CD38 - macrophages early in infection. Supp. Figure 2E: Mtb mRNA Reads Distribution Across Timepoints. This figure displays the distribution of the recovered Mtb mRNA reads at distinct post-infection timepoints. Each panel represents a timepoint, with labels on top. Cells are colored based on the presence of Mtb reads. Supp.Figure3a.pdf Supp. Figure 3: Flow cytometry analysis of infected AM and IM at 2 and 4 wpi. Infected macrophages were first identified based on expression of Mertk, CD64, and mCherry. Within this infected population, cells were further differentiated into AM (SiglecF + ) and IM (SiglecF - ). The proportion of each subset at 2 and 4 wpi is shown. Data from n=5 independent animals. Supp.Figure4a.pdf Supp. Figure 4A: Flow cytometry analysis of the Mtb mCherry signal in BCG-treated and unvaccinated mice at 2 wpi. BCG-vaccinated mice show a clear reduction in the percentage of mCherry + cells, indicating lower bacterial burden. Supp. Figure 4B: The figure illustrates the distribution of cells containing hspx-high and low bacteria across two conditions: BCG-vaccinated and unvaccinated (Control). On the X-axis, the vaccination status is represented, while the Y-axis displays the cell counts. Within each vaccination status, there are bars differentiating between Gfp-high (hspx-high) cells, colored in pink, and Gfp-low (hspx-low) cells, colored in green. The exact cell counts are labeled above each bar. Supp. Figure 4C: Visualization of BCG-specific scWGCNA-derived Module 2 (463 genes) expression levels mapped onto UMAP embeddings. Supp. Figure 4D: This heatmap showcases the expression of selected pro-inflammatory genes in BCG-vaccinated versus unvaccinated cells. Genes are listed on the Y-axis and cells are grouped by their identity on the X-axis. The color gradient—ranging from dark blue (downregulated) through white (average) to yellow (upregulated)—reflects z-scaled gene expression levels. Supp. Figure 4E: Violin and box plots showing Small GTPase gene expression levels in BCG vs. Control conditions. Within each violin plot, the density of the shading illustrates the distribution of gene expression levels, with internal white boxplots providing median and interquartile ranges. Adjusted p-values (FDR method) displayed within each facet highlight the significance of expression differences between the two conditions. Effect size measured as Cliff’s Delta is also displayed directly on each plot, providing a standardized measure of the magnitude of expression differences. Supp.Figure5a.pdf Supp. Figure 5A: This horizontal bar chart visualizes top gene set enrichment results for the scWGCNA "Module 2" specific to BCG-vaccinated macrophages. Gene categories are presented on the Y-axis, ordered by their significance. The X-axis denotes the negative logarithm (base 10) of p-values, representing the statistical significance of each enrichment. The more significant the enrichment, the longer the bar. Supp Figure 5B: Flow cytometry analysis comparing CD38 staining in infected cells from BCG-vaccinated and control mice at 2 weeks post-infection (wpi). Supp. Figure 5C: Flow cytometry analysis of the Mtb mCherry signal in BCG-treated and unvaccinated mice at 4 wpi. BCG-vaccinated mice show a clear reduction in the percentage of mCherry + cells, indicating lower bacterial burden. Supp. Figure 5D: This visualization presents cellular staining for the protein marker CD64 within the scRNA-seq dataset. Cells are colored based on their CD64 staining levels. The gradient coloring extends from light gray, indicating lower expression, to dark red for higher expression. Supp. Figure 5E: Flow cytometry analysis of the relative proportions of infected macrophages, when gating by Mertk and CD64 at 2wpi. In BCG-vaccinated mice, the vast majority of infected macrophages are AMs when using this gating strategy, because of the loss of expression of CD64 in monocyte-derived cells. For this experiment we used a high infection dose of 5x10 3 CFU in line with Mata et al. experiments. Supp. Figure 5F: Flow cytometry analysis of the relative proportions of infected macrophages, when gating only by SiglecF at 2 wpi. In BCG-vaccinated mice, the vast majority of infected cells are of monocyte-origin, in line with the findings from our scRNA-seq dataset. A consistent increase in the percentage of infected AMs in BCG-vaccinated mice compared to the unvaccinated counterparts, underscores their improved ability to restrict Mtb growth. For this experiment we used a high infection dose of 5x10 3 CFU in line with Mata et al. experiments. Supp. Figure 5G: Flow cytometry analysis of the relative proportions of infected macrophages, when gating only by SiglecF at 4 wpi. In BCG-vaccinated mice, the majority of infected cells are of monocyte-origin. A consistent increase in the percentage of infected AMs in BCG-vaccinated mice compared to the unvaccinated counterparts, underscores their improved ability to restrict Mtb growth. For this experiment we used a high infection dose of 5x10 3 CFU in line with Mata et al. experiments. Supp.Figure6a.pdf Supp. Figure 6A: TSS Enrichment Analysis of scATAC-seq Data from Naïve mice. The plot showcases the normalized insertion profile of the single-cell ATAC-seq dataset relative to the Transcription Start Site (TSS). The x-axis represents the distance from the TSS, and the y-axis indicates the normalized insertion profile. A pronounced peak at or near the center is indicative of significant enrichment at transcription start sites, suggesting a high-quality ATAC-seq dataset. Supp. Figure 6B: This UMAP representation highlights the chromatin accessibility patterns of two marker genes, " Mafb " and " Ly6a ", in scATAC-seq data. Each point on the UMAP plot corresponds to a cell, and its color intensity reflects the gene accessibility score for the specified marker genes. Yellow shades indicate higher accessibility, suggesting a potential for higher gene activity. Supp. Figure 6C: Visualization of open chromatin regions near key genes “ Nos2 ”, “ Cd38 ”, “ Slc7a11 ”, “ Ccl5 ”, "Ptgs2," "Cxcl2," and "Il1b" within the single-cell ATAC-seq dataset. The browser-like display is grouped by distinct scATAC-seq clusters, allowing for a comparative analysis of chromatin accessibility peaks across different cell types. The visualization spans 50,000 base pairs both upstream and downstream of the gene locus, ensuring a detailed view of potential regulatory elements, such as enhancers or repressors, in the vicinity of the target genes. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3934768","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":318962548,"identity":"6675e761-5168-4adb-8cb5-a28832d561ab","order_by":0,"name":"David Russell","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDCCAwyMDz6AWcyNB4AEUVqYDWeAWYwNRGthk+YhSQvf+bWPjW1qbPLN2RuBWiqsExsIaZG88dzwcc6xNMudPQeBWs6kE9ZicOMYs3Fuw2EDgxuJDQcY2w4TpYVN2hKk5f5DoJZ/xGg538YmzQi2Beh9IIMYv7AxG/YcSzMwOAN0WMKxdGOCWvjOH2N88KPGxsDg+OGDDz7UWMsS1MIgkYDEScChCBXwHyBK2SgYBaNgFIxkAADY10fPZqNTdAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9748-750X","institution":"Cornell University","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"","lastName":"Russell","suffix":""},{"id":318962549,"identity":"5371018e-2f44-4403-9637-755e196549fe","order_by":1,"name":"Davide Pisu","email":"","orcid":"https://orcid.org/0000-0001-5641-8877","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Davide","middleName":"","lastName":"Pisu","suffix":""},{"id":318962550,"identity":"0310bfaf-7c54-4695-bee2-71d7c3df2aac","order_by":2,"name":"Joshua Mattila","email":"","orcid":"https://orcid.org/0000-0002-6384-1291","institution":"University of Pittsburgh","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Mattila","suffix":""},{"id":318962551,"identity":"8061b64a-57f2-4dec-9f04-e6eb123aa004","order_by":3,"name":"Luana Johnston","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Luana","middleName":"","lastName":"Johnston","suffix":""}],"badges":[],"createdAt":"2024-02-06 19:11:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3934768/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3934768/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-52846-w","type":"published","date":"2024-10-02T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60281693,"identity":"7d628bc2-03b7-4bef-b5d1-3562f494fdb8","added_by":"auto","created_at":"2024-07-15 06:44:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":551781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003escRNA-seq Timepoint Analysis Reveals Distinct Macrophage Origins and Monocyte-to-Macrophage Differentiation Post-Mtb Challenge.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e UMAP visualization representing scRNA-seq data from 49,600 cells collected at 2-, 3-, 4-, and 6-weeks post-Mtb challenge. Cells are clustered based on their transcriptional profiles in an unbiased manner.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003eUMAP visualizations of cells stained for SiglecF and Cd11c protein markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC:\u003c/strong\u003eViolin plot representations of protein expression levels for SiglecF and CD11c in different AM subsets. Statistical significance for each marker was tested using Kruskal-Wallis and post-hoc Dunn tests. Levels of significance are denoted as follows: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ****p \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD:\u003c/strong\u003e Visualization of scWGCNA-derived gene modules expression levels mapped onto UMAP embeddings. \u003cstrong\u003eLeft Panel:\u003c/strong\u003e Expression pattern of genes in Module 5 (110 genes). \u003cstrong\u003eRight Panel:\u003c/strong\u003eExpression pattern of genes in Module 6 (28 genes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE:\u003c/strong\u003e DotPlot illustrating the expression pattern of tissue-resident and monocyte specific markers across macrophage cell clusters. Dot size and color intensity indicate percentage and average expression level, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF:\u003c/strong\u003e Umap plots showing expression levels (in log-normalized counts) for the \u003cem\u003eMafb\u003c/em\u003e (top) and \u003cem\u003eLy6c2\u003c/em\u003egenes (bottom) across all cells.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/d35d1187dca538386bc97826.png"},{"id":60280504,"identity":"141bdce9-863d-4fb5-b38d-316e73c66b4c","added_by":"auto","created_at":"2024-07-15 06:28:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1116032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of moAMs and temporal analysis of pro-inflammatory genes in AM and IM subsets.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e UMAP Visualization of cell clusters from Naïve and 2-weeks Post-infection timepoint. The NOS2\u003csup\u003e+\u003c/sup\u003e IMs, NOS2\u003csup\u003e-\u003c/sup\u003e IMs and moAMs clusters are highlighted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003e Pseudotime analysis of macrophages on the UMAP projection. Key features of the trajectory, including leaves (grey) and branch points (black), are annotated directly on the plot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC:\u003c/strong\u003e DotPlot showing the expression levels of key extravasation and adhesion markers across various macrophage clusters. Dot size and color intensity indicate percentage and average expression level, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD:\u003c/strong\u003e Heatmap of differential gene expression for selected genes between bystander and infected moAMs. Each row represents a gene, and each column represents a cell. N = 1048 cells for moAMs Bystander and n = 1537 cells for moAMs Infected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE: (Left Panel): \u003c/strong\u003eViolin plot showing the aggregate expression level of \u003cem\u003eNos2\u003c/em\u003e based on GFP status. Statistical significance was assessed using a Wilcoxon test. Effect size measured as Cliff’s Delta is also displayed directly on the plot. \u003cstrong\u003e(Right Panel): UMAPs of infected cells\u003c/strong\u003e. In the top plot, cells are color-coded based on their \u003cem\u003eNos2\u003c/em\u003e expression level, while in the bottom plot, they are color-coded based on their GFP status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF:\u003c/strong\u003e UMAP of CD38 staining levels across cell subsets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e: Volcano plot showing differential gene expression in NOS2\u003csup\u003e+\u003c/sup\u003e and NOS2\u003csup\u003e-\u003c/sup\u003e IMs, with key genes of interest labeled.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/710266b842211915b1646299.png"},{"id":60281193,"identity":"cb92a7eb-5f9f-47cc-81fc-e3a84e77986d","added_by":"auto","created_at":"2024-07-15 06:36:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":461816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePro-inflammatory immune responses are associated with rising CD38 expression over time.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Violin plot displaying the distribution of \u003cem\u003eNos2\u003c/em\u003e gene expression levels in infected macrophages, split by timepoints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003e Overlaid flow cytometry histograms (on the left) and a bar chart (on the right) display the MFI of the Mtb GFP signal at various timepoints during the infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC:\u003c/strong\u003e Flow cytometry analysis of the Mtb smyc’::mCherry/hspx’::GFP reporter strain at 2 and 4 wpi in NOS2\u003csup\u003e-/-\u003c/sup\u003e mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD:\u003c/strong\u003e Scatter plot comparing log10-transformed CFU counts between Control and NOS2\u003csup\u003e-/-\u003c/sup\u003e mice. Each point represents an individual observation. Horizontal bars indicate the mean values. Summary statistics displayed for each group include the mean and standard deviation (SD), alongside the sample size (n). A t-test p-value annotation above the plot provides a statistical comparison of the means between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE:\u003c/strong\u003e CD38 Protein Expression in macrophages, stratified by infection status and timepoint, displayed using violin and box plots with annotated adjusted p-values from pairwise t-tests for each timepoint. The effect size measured as Cliff’s Delta is also displayed directly on each plot, providing a standardized measure of the magnitude of differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF:\u003c/strong\u003e Pathways upregulated at late (top) and early (bottom) timepoints.\u003cstrong\u003e \u003c/strong\u003eEach cell cluster is represented by distinct colored points. Average pathway expression levels are on the y-axis, with inferred time from early to late on the x-axis. A red fitted line illustrates the expression trend over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG:\u003c/strong\u003e Scatter plot showing two-dimensional coordinates of single cells from a force-directed layout embedding for infected and bystander groups. Each point represents a cell, with its position on the plot determined based on its expression similarities to other cells. Colors indicate the time point of data collection as per the right-side color bar.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/9452effcdf59daa56d0589a0.png"},{"id":60281197,"identity":"c66cc21b-e43f-4517-aae4-7ecca923fda1","added_by":"auto","created_at":"2024-07-15 06:36:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":432864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrative Analysis of Differential Abundance in MTB Infection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Neighborhood graph projected on the UMAP plot displaying cell cluster relationships by differential abundance in infected and bystander populations. Neighbors with significant Log2 Fold Change (LFC) depletion rates are in red, showing cells reduced during infection, while late-infection enriched neighbors are in blue (FDR \u0026lt; 0.1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003e Vertical scatter plot showing changes in macrophage subset abundance over time, with color-coded data points for statistical significance and change in direction: Blue for early infection increase (FDR \u0026lt; 0.1), Orange for late infection increase (FDR \u0026lt; 0.1), and Light Gray for no significant change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC and D:\u003c/strong\u003e UMAP visualizations of macrophage subpopulations in infected (4C) and bystander (4D) cells across different timepoints of infection. The percentages of AMs and IMs within the total macrophage population at each timepoint are labeled on the plot.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/e5915b3fde3bf245fd9353ee.png"},{"id":60280507,"identity":"60505569-a938-4c31-add9-aba97cbbee89","added_by":"auto","created_at":"2024-07-15 06:28:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":819974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of CD38\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and CD38\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e AMs post Mtb challenge.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Pie charts of infected AM subtypes at 2 and 4 Weeks Post-Infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003e (Top) UMAP visualization showing neighborhood (nhoods) clustering based on differential abundance, where each point represents a neighborhood group positioned to reflect original data structures. The coloring denotes groups with similar LFC depletion rates. (Bottom) Vertical scatter plot showing changes in the LFC depletion rate of NhoodGroup(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e: Bar plots of cell type distribution in Neighborhood Groups 2 and 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e: Ridge plot of \u003cem\u003eNos2\u003c/em\u003e gene expression across AM clusters, segmented by timepoint of infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE:\u003c/strong\u003e Volcano plot of Differential Gene Expression (DGE) in CD38\u003csup\u003e+/-\u003c/sup\u003e AMs, with x-axis for log2 fold changes and y-axis for -log10 adjusted p-values, highlighting top DGE genes for both groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF:\u003c/strong\u003e Horizontal bar chart illustrating Gene Ontology (GO) Processes Enriched in CD38\u003csup\u003e-\u003c/sup\u003e AMs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG:\u003c/strong\u003e Bar chart illustrating the temporal distribution of infected AMs by CD38 staining, with each bar representing the percentage of the specific cell type within the total infected AMs at each timepoint.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH:\u003c/strong\u003e Horizontal bar chart illustrating the Gene Set Enrichment Analysis for CD38\u003csup\u003e+\u003c/sup\u003e AMs.\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/8047e2de613298e97bab138c.png"},{"id":60280512,"identity":"3307157f-f811-4b33-ac33-6c0c63c99a2e","added_by":"auto","created_at":"2024-07-15 06:28:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":439137,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative Analysis of Immune Responses, Cellular Distribution, and Mycobacterium tuberculosis Infection Dynamics in BCG-Vaccinated vs. Unvaccinated Mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Bar chart comparing the proportions of Monocyte-Derived and Tissue-resident cells between BCG-vaccinated and unvaccinated mice at 2 weeks post-infection. N= 3931 cells (5 mice) for the BCG vaccinated group and n=2278 cells (5 mice) for the Unvaccinated Group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003e Flow cytometry analysis of the distribution of the Mtb mCherry and GFP signals from infected cells in BCG-vaccinated and control mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC:\u003c/strong\u003e Scatter plot showing log10-transformed CFU counts in BCG-vaccinated and unvaccinated mice at 2wpi. Points on the plot represent individual CFU counts, with horizontal bars illustrating the mean values for each group. Summary statistics including mean CFU count, standard deviation (SD), and sample size (n) are provided in the plot. A p-value annotation derived from a t-test comparing the differences in mean between the two groups is included in the upper part of the plot. The y-axis is labeled with \"log10 CFU Count\" to reflect the log transformation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD:\u003c/strong\u003e Bar chart displaying the distribution of infected TR-AM subtypes by vaccination status. Each bar's height corresponds to the percentage of the respective TR-AM subtype within the total infected macrophage cell population, with values labeled on each bar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE:\u003c/strong\u003e UMAP visualization of cell clusters in BCG vaccinated and control samples, 2 weeks post-infection. N= 3931 cells for the BCG vaccinated group and n=2278 cells for the Unvaccinated Group. Both groups include cells pooled from 5 different animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF:\u003c/strong\u003e Dot plot representation of genes associated with lysosomal function and phagocytosis/autophagy pathways, between BCG-vaccinated and control mice. Dot size and color intensity indicate percentage and average expression level, respectively.\u003c/p\u003e","description":"","filename":"Binder16.png","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/3308edf1ad34c93fecb8244e.png"},{"id":60280518,"identity":"76f0112b-629a-4293-a162-0111b60a53a4","added_by":"auto","created_at":"2024-07-15 06:28:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":436896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePre-existing differences in the chromatin organization of TR-AMs are linked to differential responses to Mtb infection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Unbiased UMAP Clustering of Chromatin Accessibility Profiles. Cells are spatially organized and colored based on similarities in their chromatin accessibility patterns. Associated cluster names are shown on the plot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB:\u003c/strong\u003e Visualization of unconstrained integration of single-cell ATAC-seq and scRNA-seq datasets, with each point being an individual cell spatially organized by chromatin accessibility profile and colored according to its predicted cell type, based on cell types in the scRNA-seq dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC:\u003c/strong\u003e scATAC Cluster Compositions Post-scRNA Integration. This bar plot depicts the percentage compositions of each scATAC cluster based on the predicted cell labels obtained after integration with the scRNA dataset. The x-axis represents different scATAC clusters, while the y-axis displays the percentage of total cells for each scATAC-seq cluster associated with each predicted scRNA-seq cell label. Each bar is colored distinctly for the three TR-AMs conditions: \"CD38\u003csup\u003e-\u003c/sup\u003e Anti-inflammatory,\" \"CD38\u003csup\u003e+\u003c/sup\u003e Pro-inflammatory,\" \"Mki67\u003csup\u003e+\u003c/sup\u003e TR-AMs,\" and \"Other.\" The actual percentage values are overlaid on the bars.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD:\u003c/strong\u003e Scatter plot depicting TFBS enrichment scores in cluster C7, with each point representing a transcription factor (TF) ranked on the x-axis and plotted against the negative log10 of its adjusted p-value on the y-axis. Top transcription factors are labeled based on their enrichment scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE:\u003c/strong\u003e UMAP plots displaying log-normalized count expression levels for the transcription factors '\u003cem\u003eSmarcc1\u003c/em\u003e', '\u003cem\u003eBach1\u003c/em\u003e', and '\u003cem\u003eRela\u003c/em\u003e' in the infected cell population of the scRNA-seq dataset.\u003c/p\u003e","description":"","filename":"Binder17.png","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/b21211761fe105de40fb3c7e.png"},{"id":66639081,"identity":"f3d7a522-f41f-4ffb-a9a7-57b5977d86c6","added_by":"auto","created_at":"2024-10-15 05:57:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5696514,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/cc5228f3-8bfe-418c-adbd-247d96b87e5b.pdf"},{"id":60282340,"identity":"034338a9-d598-4b9a-9c34-fe164a777c05","added_by":"auto","created_at":"2024-07-15 06:52:07","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1213917,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1\u003c/p\u003e","description":"","filename":"Supp.Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/7a734fb166983630f0479f62.xlsx"},{"id":60280501,"identity":"de30aff7-b7c2-4127-9f19-0a23d077f315","added_by":"auto","created_at":"2024-07-15 06:28:07","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1023767,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 2\u003c/p\u003e","description":"","filename":"Supp.Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/1c790fc2c5c529c072834ddf.xlsx"},{"id":60281691,"identity":"7ffef24a-f392-44ba-a9b6-24c1d6273581","added_by":"auto","created_at":"2024-07-15 06:44:07","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":28695,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 3\u003c/p\u003e","description":"","filename":"Supp.Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/36958e7499bd7ab7de3a0ae2.xlsx"},{"id":60280515,"identity":"f26b9966-ca1e-4c09-9ad3-16dae2e59a64","added_by":"auto","created_at":"2024-07-15 06:28:08","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":112405,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 4\u003c/p\u003e","description":"","filename":"Supp.Table4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/0ef74261d66a5f4f2895bd9f.xlsx"},{"id":60280511,"identity":"1dfed9d6-90fb-4154-99cd-707d5d748f61","added_by":"auto","created_at":"2024-07-15 06:28:07","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":5923034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupp. Figure 1A:\u003c/strong\u003e Visualization of Cell Annotations Using the ImmGen Reference. The plot visualizes the annotations of cells from the scRNA-seq timepoint dataset, leveraging the ImmGen reference dataset to assign cell type labels. The SingleR method\u003csup\u003e115\u003c/sup\u003e has been employed to match the gene expression profile of each cell in the scRNA-seq dataset with those from the ImmGen reference, a comprehensive database containing finely annotated immune cell types. The heatmap displays the matching scores for each cell in the scRNA-seq dataset against potential cell types from ImmGen. Each column in the heatmap represents a cell from the dataset, while the rows correspond to the potential cell type labels from ImmGen. The cells are grouped based on their inherent clusters from the scRNA-seq dataset. The color intensity in the heatmap indicates the degree of match between the cell's gene expression profile and the reference profile of the cell type in ImmGen. Yellow shades suggest a stronger match, indicating higher confidence in the assigned cell type label.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 1B:\u003c/strong\u003e DotPlot illustrating genes involved in monocyte-to-macrophage differentiation processes. Each dot's size corresponds to the percentage of cells within a cluster expressing a given gene, while the color intensity indicates the average expression level, ranging from red (low expression) to blue (high expression). Clusters include different subsets of tissue-resident macrophages and monocyte populations.\u003c/p\u003e","description":"","filename":"Supp.Figure1a.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/f994921db54e49915b16c1bd.pdf"},{"id":60280513,"identity":"08b838f8-fb6f-4261-a10c-3ab50422f48a","added_by":"auto","created_at":"2024-07-15 06:28:08","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2849670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupp. Figure 2A:\u003c/strong\u003e Flow cytometry analysis of the Mtb smyc’::mCherry/hspx’::GFP reporter strain at 2 wpi in Rag1\u003csup\u003e-/-\u003c/sup\u003e and IFN-Y\u003csup\u003e-/-\u003c/sup\u003e KO mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 2B:\u003c/strong\u003e CD11b Staining Across Timepoints and Infection Status. This figure presents CD11b staining levels in macrophages, stratified by infection status and timepoint. Using violin plots overlaid with box plots (representing the interquartile range and the median), the distribution of staining levels is visualized. Each facet represents a distinct timepoint, and within each, the CD11b protein levels for each infection status, barring 'Uninfected', are shown. Color-coding differentiates infection statuses. Adjusted p-values from pairwise t-tests are annotated within each facet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 2C:\u003c/strong\u003e Pathways upregulated at late timepoints.\u003cstrong\u003e \u003c/strong\u003eThe plots illustrate temporal shifts in pathway expression over inferred time for different clusters. Each cluster is represented by distinct colored points, with the average pathway expression levels depicted on the y-axis and inferred time on the x-axis, ranging from an early to a late timepoint. A fitted line, shown in red, captures the trend of pathway expression over time. Positive regulation of macrophage fusion and regulation of secretion of lysosomal enzymes are among the pathways with increased expression in CD38\u003csup\u003e+\u003c/sup\u003e macrophages over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 2D: \u003c/strong\u003ePathways upregulated in early infection.\u003cstrong\u003e \u003c/strong\u003eThe plots illustrate temporal shifts in pathway expression over inferred time for different clusters. Each cluster is represented by distinct colored points, with the average pathway expression levels depicted on the y-axis and inferred time on the x-axis, ranging from an early to a late timepoint. A fitted line, shown in red, captures the trend of pathway expression over time. Regulation of receptor recycling, mitotic actomyosin contractile ring assembly and positive regulation of dense core granule transport are among the pathways with elevated expression in CD38\u003csup\u003e-\u003c/sup\u003e macrophages early in infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 2E: \u003c/strong\u003eMtb mRNA Reads Distribution Across Timepoints. This figure displays the distribution of the recovered Mtb mRNA reads at distinct post-infection timepoints. Each panel represents a timepoint, with labels on top. Cells are colored based on the presence of Mtb reads.\u003c/p\u003e","description":"","filename":"Supp.Figure2a.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/48899451828928f2bf01cab4.pdf"},{"id":60280516,"identity":"7c75d622-6b99-4e43-b8ad-28ba10133da4","added_by":"auto","created_at":"2024-07-15 06:28:08","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1694889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupp. Figure 3: \u003c/strong\u003eFlow cytometry analysis of infected AM and IM at 2 and 4 wpi. Infected macrophages were first identified based on expression of Mertk, CD64, and mCherry. Within this infected population, cells were further differentiated into AM (SiglecF\u003csup\u003e+\u003c/sup\u003e) and IM (SiglecF\u003csup\u003e-\u003c/sup\u003e). The proportion of each subset at 2 and 4 wpi is shown. Data from n=5 independent animals.\u003c/p\u003e","description":"","filename":"Supp.Figure3a.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/ecf073c238ce03a8256bdda6.pdf"},{"id":60280519,"identity":"26149764-ec53-4bbe-87cb-2e94370d21c2","added_by":"auto","created_at":"2024-07-15 06:28:08","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":4117227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupp. Figure 4A:\u003c/strong\u003e Flow cytometry analysis of the Mtb mCherry signal in BCG-treated and unvaccinated mice at 2 wpi. BCG-vaccinated mice show a clear reduction in the percentage of mCherry\u003csup\u003e+\u003c/sup\u003e cells, indicating lower bacterial burden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 4B:\u003c/strong\u003e The figure illustrates the distribution of cells containing hspx-high and low bacteria across two conditions: BCG-vaccinated and unvaccinated (Control). On the X-axis, the vaccination status is represented, while the Y-axis displays the cell counts. Within each vaccination status, there are bars differentiating between Gfp-high (hspx-high) cells, colored in pink, and Gfp-low (hspx-low) cells, colored in green. The exact cell counts are labeled above each bar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 4C:\u003c/strong\u003e Visualization of BCG-specific scWGCNA-derived Module 2 (463 genes) expression levels mapped onto UMAP embeddings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 4D:\u003c/strong\u003e This heatmap showcases the expression of selected pro-inflammatory genes in BCG-vaccinated versus unvaccinated cells. Genes are listed on the Y-axis and cells are grouped by their identity on the X-axis. The color gradient—ranging from dark blue (downregulated) through white (average) to yellow (upregulated)—reflects z-scaled gene expression levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 4E:\u003c/strong\u003e Violin and box plots showing Small GTPase gene expression levels in BCG vs. Control conditions. Within each violin plot, the density of the shading illustrates the distribution of gene expression levels, with internal white boxplots providing median and interquartile ranges. Adjusted p-values (FDR method) displayed within each facet highlight the significance of expression differences between the two conditions. Effect size measured as Cliff’s Delta is also displayed directly on each plot, providing a standardized measure of the magnitude of expression differences.\u003c/p\u003e","description":"","filename":"Supp.Figure4a.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/2a7d38f6a3961193e560cc72.pdf"},{"id":60280510,"identity":"466c1fb0-8817-4a5b-a4cc-ad29f263a627","added_by":"auto","created_at":"2024-07-15 06:28:07","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":4631346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupp. Figure 5A:\u003c/strong\u003e This horizontal bar chart visualizes top gene set enrichment results for the scWGCNA \"Module 2\" specific to BCG-vaccinated macrophages. Gene categories are presented on the Y-axis, ordered by their significance. The X-axis denotes the negative logarithm (base 10) of p-values, representing the statistical significance of each enrichment. The more significant the enrichment, the longer the bar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp Figure 5B:\u003c/strong\u003e Flow cytometry analysis comparing CD38 staining in infected cells from BCG-vaccinated and control mice at 2 weeks post-infection (wpi).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 5C:\u003c/strong\u003e Flow cytometry analysis of the Mtb mCherry signal in BCG-treated and unvaccinated mice at 4 wpi. BCG-vaccinated mice show a clear reduction in the percentage of mCherry\u003csup\u003e+\u003c/sup\u003e cells, indicating lower bacterial burden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 5D:\u003c/strong\u003e This visualization presents cellular staining for the protein marker CD64 within the scRNA-seq dataset. Cells are colored based on their CD64 staining levels. The gradient coloring extends from light gray, indicating lower expression, to dark red for higher expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 5E:\u003c/strong\u003e Flow cytometry analysis of the relative proportions of infected macrophages, when gating by Mertk and CD64 at 2wpi. In BCG-vaccinated mice, the vast majority of infected macrophages are AMs when using this gating strategy, because of the loss of expression of CD64 in monocyte-derived cells. For this experiment we used a high infection dose of 5x10\u003csup\u003e3\u003c/sup\u003e CFU in line with Mata et al. experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 5F:\u003c/strong\u003e Flow cytometry analysis of the relative proportions of infected macrophages, when gating only by SiglecF at 2 wpi. In BCG-vaccinated mice, the vast majority of infected cells are of monocyte-origin, in line with the findings from our scRNA-seq dataset. A consistent increase in the percentage of infected AMs in BCG-vaccinated mice compared to the unvaccinated counterparts, underscores their improved ability to restrict Mtb growth. For this experiment we used a high infection dose of 5x10\u003csup\u003e3\u003c/sup\u003e CFU in line with Mata et al. experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 5G:\u003c/strong\u003e Flow cytometry analysis of the relative proportions of infected macrophages, when gating only by SiglecF at 4 wpi. In BCG-vaccinated mice, the majority of infected cells are of monocyte-origin. A consistent increase in the percentage of infected AMs in BCG-vaccinated mice compared to the unvaccinated counterparts, underscores their improved ability to restrict Mtb growth. For this experiment we used a high infection dose of 5x10\u003csup\u003e3\u003c/sup\u003e CFU in line with Mata et al. experiments.\u003c/p\u003e","description":"","filename":"Supp.Figure5a.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/4cd66418aca64599cec02b56.pdf"},{"id":60280514,"identity":"7f91a190-e0d9-468d-b958-d057f17b5625","added_by":"auto","created_at":"2024-07-15 06:28:08","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":2889878,"visible":true,"origin":"","legend":"\u003cp\u003eSupp. Figure 6A: TSS Enrichment Analysis of scATAC-seq Data from Naïve mice. The plot showcases the normalized insertion profile of the single-cell ATAC-seq dataset relative to the Transcription Start Site (TSS). The x-axis represents the distance from the TSS, and the y-axis indicates the normalized insertion profile. A pronounced peak at or near the center is indicative of significant enrichment at transcription start sites, suggesting a high-quality ATAC-seq dataset.\u003c/p\u003e\n\u003cp\u003eSupp. Figure 6B: This UMAP representation highlights the chromatin accessibility patterns of two marker genes, \"\u003cem\u003eMafb\u003c/em\u003e\" and \"\u003cem\u003eLy6a\u003c/em\u003e\", in scATAC-seq data. Each point on the UMAP plot corresponds to a cell, and its color intensity reflects the gene accessibility score for the specified marker genes. Yellow shades indicate higher accessibility, suggesting a potential for higher gene activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Figure 6C:\u003c/strong\u003e Visualization of open chromatin regions near key genes “\u003cem\u003eNos2\u003c/em\u003e”, “\u003cem\u003eCd38\u003c/em\u003e”, “\u003cem\u003eSlc7a11\u003c/em\u003e”, \u0026nbsp;“\u003cem\u003eCcl5\u003c/em\u003e”, \u003cem\u003e\"Ptgs2,\" \"Cxcl2,\" and \"Il1b\"\u003c/em\u003e within the single-cell ATAC-seq dataset. The browser-like display is grouped by distinct scATAC-seq clusters, allowing for a comparative analysis of chromatin accessibility peaks across different cell types. The visualization spans 50,000 base pairs both upstream and downstream of the gene locus, ensuring a detailed view of potential regulatory elements, such as enhancers or repressors, in the vicinity of the target genes.\u003c/p\u003e","description":"","filename":"Supp.Figure6a.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/fc0607baaa4075e3be15ba32.pdf"},{"id":60281198,"identity":"175492c1-c187-4667-b8f5-d197d8bcebe2","added_by":"auto","created_at":"2024-07-15 06:36:07","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":5515376,"visible":true,"origin":"","legend":"\u003cp\u003eReporting Summary\u003c/p\u003e","description":"","filename":"NCOMMS2408070A1ra.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934768/v1/5c3ad2812fac55a5b148627e.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"CD38+ Alveolar macrophages mediate early control of M. tuberculosis proliferation in the lung","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) remains a major global health issue, as reported in WHO\u0026rsquo;s Global Tuberculosis Report 2022\u003csup\u003e1\u003c/sup\u003e. \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb), the etiological agent of TB, primarily infects the lungs and has evolved to survive the host immune response. Within the lungs, alveolar macrophages (AMs) play a critical role in protecting the airway surfaces upon early infection\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, with recruited monocyte-derived macrophages increasing in number as the infection progresses\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therefore, understanding the specific role of AMs during Mtb infection is key to uncovering disease mechanisms and guiding the development of effective vaccines.\u003c/p\u003e \u003cp\u003eIn recent years, our understanding of the interactions between AMs and Mtb has grown considerably. Bulk transcriptomic studies, alongside dual RNA-seq, have demonstrated that AMs can facilitate Mtb replication and dissemination in the lung\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Recent advances in single-cell transcriptomics have uncovered the presence of heterogenous populations within the AM and IM lineages, each exhibiting varying responses to Mtb infection\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, innovative vaccination approaches, including subcutaneous, intravenous (iv) and pulmonary immunization using live BCG, have been found to activate AMs and innate immune cells, and provide lasting protection against Mtb\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, despite these advancements, the specific AM populations and properties underlying this protective effect remain unidentified.\u003c/p\u003e \u003cp\u003eIn this study, we leverage our established multi-modal single-cell RNA sequencing (scRNA-seq) protocol\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e to identify the specific AM subsets that provide protection against Mtb. Employing the murine TB model, we evaluate macrophage phenotypes at 2-, 3-, 4-, and 6-weeks post-Mtb challenge to understand their roles in the early stages of infection and after BCG intranasal immunization.\u003c/p\u003e \u003cp\u003eOur findings traced the evolving lung immune response to Mtb infection. We observe macrophage populations transitioning from anti-inflammatory to pro-inflammatory states over time. Our analysis revealed the most pronounced phenotypic changes occurring within the resident AM populations and the recruited monocyte-derived AMs (moAMs), underscoring their critical roles in the immune response to Mtb infection. A crucial discovery was the identification of CD38, an extracellular NADase linked to macrophage activation, inflammation and infection\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, as a potential marker for protective responses against Mtb. We observed distinct phenotypes for SiglecF\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs, SiglecF\u003csup\u003e\u0026minus;\u003c/sup\u003e CD38\u003csup\u003e+\u003c/sup\u003e moAMs, and SiglecF\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e TR-AMs throughout the disease process. Initially, CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e TR-AMs, displaying a muted pro-inflammatory response, were the main infected cell type. As infection progressed, CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e TR-AM numbers decreased, while CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs and the newly recruited moAMs rose in prevalence. In the later stages of infection, these CD38\u003csup\u003e+\u003c/sup\u003e AM subsets emerged as the dominant infected host AM populations, demonstrating enhanced capacity to restrict Mtb's growth.\u003c/p\u003e \u003cp\u003eSingle-cell Assay for Transposase-Accessible Chromatin using sequencing (ScATAC-seq) analysis revealed that CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs have a unique chromatin structure that is distinct from CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e TR-AMs. These cells were already present in the lungs of na\u0026iuml;ve mice prior to infection, indicating an inherent epigenetic predisposition modulating their respective responses to Mtb infection. Additionally, intranasal immunization with live BCG, resulted in an increase in CD38\u003csup\u003e+\u003c/sup\u003e macrophages in the lungs. This increase, the result of both an influx of CD38\u003csup\u003e+\u003c/sup\u003e monocyte-derived macrophages and polarization of existing reactive pro-inflammatory CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs, offers significant protection against Mtb challenge.\u003c/p\u003e \u003cp\u003eTo date vaccine development against tuberculosis has met with a modest success\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and it is seriously impaired by our lack of reliable biomarkers that are predictive of vaccine efficacy and outcome\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The identification and phenotypic characterization of those macrophage subsets best equipped to mediate protection against Mtb represents a significant advance in defining the immune response that we wish to drive with new immunotherapeutic interventions.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003ch2\u003eOntogeny and intra-lineage diversity are key determinants in macrophage responses to Mtb infection.\u003c/h2\u003e\n\u003cp\u003eTo better understand macrophage behavior during Mtb infection, we conducted \u0026nbsp;an extensive multi-modal scRNA-seq analysis of macrophage phenotypes at 2-, 3-, 4-, 6-weeks post-Mtb challenge, which spans the transition from innate to adaptive immune responses\u003csup\u003e2,10,32-36\u003c/sup\u003e. Our aim was to determine how distinct macrophage populations respond post-Mtb infection, with the goal of identifying the subsets that are responsible for controller responses and those that favor Mtb replication and expansion. Consistent with our previous work\u003csup\u003e9\u003c/sup\u003e, we infected mice with the \u003cem\u003ehspx’\u003c/em\u003e::gfp/smyc’::mCherry Mtb reporter strain. This strain expresses mCherry constitutively and GFP in response to host mediated immune-related stress\u003csup\u003e35\u003c/sup\u003e. For each infected host cell, we were able to assess the fitness status of intracellular Mtb, quantify the surface marker expression and profile their transcriptome\u003csup\u003e9\u003c/sup\u003e. \u0026nbsp;After QC, our dataset comprised of 49600 cells, the significant majority of which were identified as macrophages (Figure 1A and Supp. Figure 1A).\u003c/p\u003e\n\u003cp\u003eRecent studies, including ours, have shown that both resident alveolar macrophages (AMs) and recruited interstitial macrophages (IMs), are ontogenically diverse\u0026nbsp;\u003csup\u003e6,8,37,38\u003c/sup\u003e and comprised of phenotypically-distinct subpopulations\u003csup\u003e9,39-42\u003c/sup\u003e. These subpopulations exhibit markedly different inflammatory responses to Mtb infection\u003csup\u003e9\u003c/sup\u003e. In the current study, we extend these findings, further characterizing the discrete AM and IMs subpopulations based on their origin and inflammatory profiles.\u003c/p\u003e\n\u003cp\u003eAMs are often identified by their expression of surface markers SiglecF and CD11c\u003csup\u003e6,43,44\u003c/sup\u003e, and in our dataset we could identify AM subpopulations by SiglecF and CD11c staining (Figure 1B). Interestingly, we also noticed a population of cells that cluster with AMs by UMAP analysis, but do not stain for either SiglecF or CD11c (Figure 1A and 1C). To determine the origin of these cells, we focused on the early infection stages (Naïve and 2 weeks post-infection). We performed unbiased weighted gene correlation network analysis (WGCNA) on the resulting 13,125 cells to identify gene co-expression modules that define this population\u003csup\u003e45\u003c/sup\u003e. The analysis identified two gene expression modules that mirrored the differential staining of the SiglecF and CD11c antibodies (Figure 1D). Module 6 consists of a 28-gene co-expression set and includes genes commonly used to define tissue resident alveolar macrophages—such as \u003cem\u003eCd9, Mrc1\u003c/em\u003e (CD206), \u003cem\u003eLpl, Trf, and Chil3\u003c/em\u003e (Ym1) (Figure 1E)\u003csup\u003e8,9,46,47\u003c/sup\u003e. In contrast, Module 5, represented by a larger 110-gene set and expressed by the SiglecF/CD11c double negative population, is enriched in genes associated with the monocytic lineage (Figure 1E) and monocyte-to-macrophage differentiation (Supp Figure 1B). These AM-like cells express \u003cem\u003eMafB\u003c/em\u003e—a driver of monocyte-to-macrophage differentiation—and \u003cem\u003eLy6c2\u003c/em\u003e, a marker for cells of monocyte origins (Figure 1F)\u003csup\u003e48-52\u003c/sup\u003e. We therefore designated this population as moAMs (monocyte-derived AMs). In the lungs of naïve mice moAMs are absent, implying these monocytes migrate there and differentiate to perform AM-related functions after Mtb infection (Figure 2A). Trajectory and pseudotime analyses corroborate this hypothesis, revealing that moAMs, along with the classical NOS2\u003csup\u003e+\u003c/sup\u003e and NOS2\u003csup\u003e-\u003c/sup\u003e IMs, represent distinct cell fates (grey leaves) that originate from a population of infiltrating monocytes (root 2 - white circle - and branching point 11 – black circle -), characterized by a unique transcriptional signature associated with leukocyte adhesion and extravasation (Figures 2B and 2C)\u003csup\u003e53-64\u003c/sup\u003e. Uninfected bystander moAMs express genes associated with macrophage functions including phagocytosis, lipid metabolism, and RNA/protein synthesis, indicative of roles beyond mere homeostasis (Figure 2D)\u003csup\u003e65-67\u003c/sup\u003e. This perspective is supported by the contrasting gene expression in Mtb-infected moAMs, which pivot to a pro-inflammatory state, as evidenced by the upregulation of genes such as \u003cem\u003eAcsl1, Hmox1, Saa3, Ptgs2, Slc7a11, Clec4e\u0026nbsp;\u003c/em\u003eamong others(Figure 2D)\u003csup\u003e9\u003c/sup\u003e. This transition is marked by elevated \u003cem\u003eNos2\u003c/em\u003e expression and association with \u003cem\u003ehspx’\u003c/em\u003e::GFP-high bacteria (Figure 2E). Such findings align with prior research which described the role of interferon-g\u0026nbsp;in deactivating enhancer regions bound by the transcription factor Maf. This mechanism is crucial for suppressing M2 genes and increasing activation of monocyte-derived macrophages\u003csup\u003e68\u003c/sup\u003e. \u0026nbsp;This moAM cohort expresses CD38 (Figure 2F), hence their designation as CD38\u003csup\u003e+\u003c/sup\u003e moAMs.\u003c/p\u003e\n\u003cp\u003eNext, we focused on TR-AMs and identified two subpopulations based on their CD38 expression. The CD38\u003csup\u003e+\u003c/sup\u003e population exhibits increased \u003cem\u003eNos2\u003c/em\u003e expression and is associated with stressed (\u003cem\u003ehspx’\u003c/em\u003e::GFP high) bacteria (Figure 2E and 2F). Notably, these TR-AMs are dual positive for SiglecF and CD11c (Figure 1B). They also express the TR-AM gene signature from Module 6 (Figure 1D), leading us to annotate them as CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs. Importantly, trajectory and pseudotime analyses establish the CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs as a distinct lineage from moAMs, as evidenced by their origin from root 1 (white circle) (Figure 2B). Additionally, we also identified CD38\u003csup\u003e-\u003c/sup\u003e subpopulations of TR-AMs, associated with \u003cem\u003ehspx’\u003c/em\u003e::GFP-low Mtb, indicating bacteria experiencing minimal host-derived stress (Figures 2E and 2F).\u003c/p\u003e\n\u003cp\u003eIn examining the IM subpopulations, we observed a similar level of heterogeneity. While both IM populations appear to originate from infiltrating monocytes (Figure 2B), the NOS2\u003csup\u003e+\u003c/sup\u003e IMs are CD38\u003csup\u003e+\u003c/sup\u003e, whereas the NOS2\u003csup\u003e-\u003c/sup\u003e IM lack CD38 expression (Figure 2F). Looking at the transcriptional profiles of these populations revealed that CD38\u003csup\u003e+\u003c/sup\u003e IMs, akin to CD38\u003csup\u003e+\u003c/sup\u003e moAMs and TR-AMs, display a gene expression profile consistent with classic pro-inflammatory responses, which were previously linked to effective tuberculosis control\u003csup\u003e6,9\u003c/sup\u003e (Figure 2G) (Suppl. Table 1).\u003c/p\u003e\n\u003cp\u003eConversely, the IM subpopulation that is CD38\u003csup\u003e-\u003c/sup\u003e not only lack \u003cem\u003eNos2\u003c/em\u003e expression, but is also associated with \u003cem\u003ehspx’\u003c/em\u003e::GFP-low Mtb (Figure 2E and 2F). Relative to NOS2\u003csup\u003e+\u003c/sup\u003e IMs, these cells show amplified expression of genes linked to anti-inflammatory responses. This includes transcripts for complement proteins \u003cem\u003eC1q, Ccl8, Ms4a7\u003c/em\u003e among others, which have been previously characterized (Figure 2G)\u003csup\u003e9,69-73\u003c/sup\u003e. Furthermore, this subset still shows a partial expression of extravasation and adhesion markers shared with infiltrating monocytes (Figure 2C). This suggests that they represent recently arrived interstitial monocytes that have just been infected and have not yet undergone immune activation (Suppl Table 1). In summary, our analysis identified distinct subpopulations of AMs and IMs with varying ontogeny and inflammatory profiles. Importantly, we discovered a population of monocyte-derived AMs capable of transitioning to a pro-inflammatory state upon Mtb infection, and two subpopulations of TR-AMs based on CD38 expression, which are associated with different bacterial phenotypes following Mtb infection.\u003c/p\u003e\n\u003ch2\u003ePro-inflammatory immune responses are associated with rising CD38 expression in macrophages over time.\u003c/h2\u003e\n\u003cp\u003eTo understand the temporal evolution of macrophage immune responses following Mtb infection, we examined the expression patterns of pro-inflammatory gene signatures and associated markers over time. From our past studies, there was a discernible correlation between \u003cem\u003eNos2\u003c/em\u003e expression in host macrophages and bacterial stress (Figure 2E)\u003csup\u003e9\u003c/sup\u003e. Building on this, we tracked the temporal trends of \u003cem\u003eNos2\u003c/em\u003e expression within the infected macrophage populations. As the infection progresses, we observed an increase in nitric oxide production, reflected by both an increase in the level of \u003cem\u003eNos2\u003c/em\u003e expression per cell (intensity) and the number of cells expressing \u003cem\u003eNos2\u003c/em\u003e (prevalence), as shown in Figure 3A. Flow cytometry data confirmed this trend; the median fluorescence intensity (MFI) of the GFP signal from \u003cem\u003ehspx’\u003c/em\u003e::GFP-infected cells increased over time, indicating heightened bacterial stress as the infection proceeded (Figure 3B). Importantly, infecting Nos2-KO mice with the \u003cem\u003ehspx\u003c/em\u003e’::GFP Mtb reporter strain resulted in minimal GFP induction by Mtb at both 2 and 4 weeks post-infection (Figure 3C). The limited induction of GFP expression by Mtb in Nos2-KO mice was also accompanied by an increase in bacterial load (Figure 3D). Finally, flow cytometry analysis at 2 wpi of Rag1 and IFN-γ KO mice revealed no induction of hspx’::GFP (Supplementary Figure 2A), confirming that production of nitric oxide by pro-inflammatory macrophages is critically dependent on the adaptive immune response.\u003c/p\u003e\n\u003cp\u003eConsistent with these data, our surface marker analysis mirrored the trends observed in \u003cem\u003eNos2\u003c/em\u003e RNA levels. Specifically, CD38 protein levels – a marker associated with inflammation\u003csup\u003e20,22\u003c/sup\u003e – increase in infected macrophages during the latter stages (Figure 3E). Parallel trends were also seen with the marker CD11b (Suppl Figure 2B). To gain deeper insights into the molecular mechanisms and biological processes driving these phenotypes, we performed a time-dependent pathway analysis\u003csup\u003e74\u003c/sup\u003e. This analysis identified 68 pathways that exhibited variations in expression as the infection progressed (p.adj \u0026lt; 0.05)(Table 1). Pathways showing increased representation over time were predominantly associated with inflammatory and anti-bacterial responses and were overly represented in CD38\u003csup\u003e+\u003c/sup\u003e macrophages (Figure 3F and Supp figure 2C). Conversely, pathways that dominated early stages of infection were mostly associated with CD38\u003csup\u003e- \u0026nbsp;\u003c/sup\u003emacrophages, and aligned with processes linked to M2 polarization, tissue homeostasis and bacterial survival, such as negative regulation of protein kinase c signaling\u003csup\u003e75\u003c/sup\u003e, suppression of apoptosis and cell proliferation among others (Figure 3F and Supp figure 2D). Confirming these observations, our analysis revealed an increased recovery of Mtb mRNA reads from CD38\u003csup\u003e+\u0026nbsp;\u003c/sup\u003emacrophage populations over time, indicative of active bacterial degradation within these cells. Specifically, we noted low levels of Mtb reads at 2 weeks post-infection, which significantly increased from 3 to 6 weeks, as illustrated in Supplementary Figure 2E.\u003c/p\u003e\n\u003cp\u003eIntriguingly, when focusing on bystander macrophages, our analysis revealed no change in their CD38 staining levels, irrespective of the timepoint examined, contrasting starkly with their infected counterparts (Figure 3E). To validate this, we leveraged a force-directed layout to conduct unbiased graph-based clustering of both cell types\u003csup\u003e76\u003c/sup\u003e. While infected cells clustered according to duration of infection, bystander cells grouped based on their ontogeny without temporal distinctions (Figure 3G). This pronounced difference emphasizes that the inflammatory responses we observe aren't merely a result of a broad immune activation of host macrophages over time; rather, they are finely tuned and specifically driven by active Mtb infection.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our findings highlight a dynamic shift in the phenotype and relative proportion of infected macrophage populations over the course of infection. This transition emphasizes the crucial role of CD38\u003csup\u003e+\u0026nbsp;\u003c/sup\u003emacrophages, which appear to be closely associated with the control of infection.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eThe relative proportion of Mtb-infected cells shifts from AMs to IMs over time.\u003c/h2\u003e\n\u003cp\u003eTo quantify the changes in the proportion of infected macrophage populations over time, we employed a recently developed statistical framework designed for differential abundance testing (DAB)\u003csup\u003e77\u003c/sup\u003e. We used this approach to assess whether changes in abundance of both infected and bystander cells occur over the course of infection. Our analysis identified variations in the abundance of 400 neighbors across different timepoints in infected AMs and IMs subpopulations (with FDR \u0026lt; 0.1). This contrasted with bystander populations that exhibited minimal variation (Figure 4A).\u003c/p\u003e\n\u003cp\u003eMtb-infected neighbors belonging to the CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs subpopulations showed a marked decline in numbers late in infection. Conversely, neighbors within the NOS2\u003csup\u003e+\u003c/sup\u003e IMs showed a significant increase in abundance during the same later timepoints (Figure 4B). Observations at the broader population level further highlighted this trend: at 2 weeks post-infection (wpi), AMs constituted 80% of all the infected macrophages, but by 3wpi this percentage was reduced to approximately 39%, and by the 4th and 6th wpi, it dropped to around 15% (Figure 4C). Overall, our data highlight a substantial shift in abundance from TR-AMs to IMs among the infected macrophages as the infection advances, as also confirmed by flow cytometry analysis (Supp. Figure 3). This trend sharply contrast the patterns seen in bystander AMs and IMs, whose proportions remain consistent across the analyzed timepoints (Figure 4A). The stability in the proportion of bystander macrophages in the Mtb-infected lung from the 2 weeks post-infection (wpi) suggests that, within our infection model, monocyte-derived cells have already been recruited to the lung by this time but remain largely uninfected (Figure 4D), similar to a previous report\u003csup\u003e78\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis premise is further supported by examining the distinct timelines of infection rates between CD38\u003csup\u003e+\u003c/sup\u003e IMs and moAMs. Despite pseudotime analysis indicating a shared origin for both populations from the early monocyte cluster (as illustrated in Figure 2B), CD38\u003csup\u003e+\u003c/sup\u003e IMs only became the more abundant infected group by the 3rd week post-infection (Figures 4A and 4C). In contrast, a substantial proportion of CD38\u003csup\u003e+\u003c/sup\u003e moAMs were infected at a much earlier stage, by 2wpi (Figures 4C and 2A). Recent studies provide context to these observations. The initial delay in the infection of monocyte-derived IM may be attributed to the early-stage preference of the infection for the lung's alveolar space during the onset of tuberculosis. As the disease progresses, leading to the translocation of infected AMs from the alveolar to the interstitial lung space\u003csup\u003e2,6,8,10\u003c/sup\u003e, the infection rate of CD38\u003csup\u003e+\u003c/sup\u003e \u003cem\u003eNos2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e IMs increases, leading to the overall shift in the abundance of infected macrophages from AMs to IMs as described above.\u003c/p\u003e\n\u003cp\u003eIn summary, while the total bystander IM and AM populations in the lung remain constant from 2wpi onwards, our data indicates a decline in the number of newly infected TR-AMs as the infection shifts from the alveolar to the interstitial lung space. In the established infection, our data demonstrate that monocyte-derived IMs are the dominant host cell population accounting for the majority of new infection events.\u003c/p\u003e\n\u003ch2\u003eThe early infection timepoints are dominated by CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs that are relatively nonresponsive to tuberculosis infection.\u003c/h2\u003e\n\u003cp\u003eWork by Rothchild et al. highlighted that murine AMs exhibit a robust anti-inflammatory response during the early stages of tuberculosis infection, up to 10 dpi. This early-phase response is postulated to be pivotal to their role in the initial infection process\u003csup\u003e2,6,8,10\u003c/sup\u003e. Building on this previous work, our focus on the AM populations revealed that the CD38\u003csup\u003e-\u003c/sup\u003e AM subset dominates this early infection landscape. Specifically, they account for approximately 70% of the Mtb-infected host cells at 14 dpi (Figure 5A). To gain a more nuanced understanding of the CD38\u003csup\u003e-\u003c/sup\u003e AMs and the impact of their changing numbers, we employed differential abundance testing (DAB) to group previously identified neighbors based on their log-fold change (LFC) depletion rates, as an alternative approach to the traditional cluster-based categorization (Figure 5B)\u003csup\u003e77\u003c/sup\u003e. Through this approach, we identified two neighbor groups (group 2 and 5) exhibiting significant depletion rates in late infection (\u0026gt; 5 LFC). Examining the cell types associated with these high LFC depletion rate groups (FDR \u0026lt;0.1), we found that 98% of cells from group 2 belonged to the CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs subsets (spanning CD38\u003csup\u003e-\u003c/sup\u003e TR-AM_1, CD38\u003csup\u003e-\u003c/sup\u003e TR-AM_2, CD38\u003csup\u003e-\u003c/sup\u003e TR-AM_3). For group 5, 57% were associated with the CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs and 33% with CD38\u003csup\u003e+\u003c/sup\u003e moAMs (Figure 5C). The over-representation of CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs in groups marked by pronounced depletion rates, identified through our DAB approach and independently confirmed by compositional analysis through scCODA\u003csup\u003e79\u003c/sup\u003e (Supp. File 1) , suggests an innate susceptibility of these cells to Mtb infection. This vulnerability becomes more obvious when compared with the unchanging relative abundance of CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs in bystander populations across different infection timepoints (Figure 4A and 4D). Coupled with the observed decline in the overall count of infected AMs as the infection unfolds (Figure 4C), it becomes evident that very few CD38\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eTR-AMs become infected in the later stages. This underscores the idea that the CD38\u003csup\u003e-\u003c/sup\u003e TR-AM clusters, dominant at 14 dpi, are poorly equipped to survive Mtb infection. This hypothesis is supported by the absence of \u003cem\u003eNos2\u003c/em\u003e mapping reads across all infection timepoints in CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs (Figure 5D), excepting cells bordering the MoAM in the CD38\u003csup\u003e-\u003c/sup\u003e TR-AM_1 cluster (likely signifying a common scRNA-seq occurrence of cells being misattributed among neighboring clusters).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDGE analysis of the CD38\u003csup\u003e-\u003c/sup\u003e AMs further supports this hypothesis. We found CD38\u003csup\u003e-\u003c/sup\u003e AMs to be associated with known markers of AM populations (\u003cem\u003eChil3, Lpl, Marco, Mrc1, Trf\u003c/em\u003e)\u003csup\u003e8,9\u003c/sup\u003e (Figure 5E). Examining the transcriptional profile of these populations we observed an upregulation of genes involved with lipid metabolism such as \u003cem\u003eFabp4, Mgll, and Lpl\u003c/em\u003e\u003cem\u003e\u003csup\u003e8,80\u003c/sup\u003e\u003c/em\u003e. Additionally, we noted increased expression in genes linked to the electron transport chain (like \u003cem\u003emt-Nd1, mt-Nd2, mt-Nd4, mt-Cytb, and mt-Atp6\u003c/em\u003e) which play pivotal roles in cellular energy metabolism. This metabolic transcriptional signature was further complemented by the upregulation of genes connected to fatty acid uptake and transport (e.g., \u003cem\u003eDbi\u003c/em\u003e)\u003csup\u003e81\u003c/sup\u003e, lipid droplet formation (e.g., \u003cem\u003eCidec, Plin2\u003c/em\u003e)\u003csup\u003e82,83\u003c/sup\u003e, and protection against oxidative stress (e.g., \u003cem\u003eGpx1, Gpx4\u003c/em\u003e)\u003csup\u003e84\u003c/sup\u003e (Supp Table 2). Collectively, this data indicates a shift towards fatty acid oxidation (FAO) metabolism. Importantly, the upregulation of genes involved in metabolic and homeostatic functions stands in contrast to a diminished expression of pro-inflammatory genes, critical in combating tuberculosis infection (Figure 5E) (Supp. Table 2). Moreover, Gene Ontology (GO) functional enrichment analysis of the transcriptional profile of CD38\u003csup\u003e-\u003c/sup\u003e AMs also supports these observations. The functional profile of the CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs aligns with alveolar macrophage characteristics that have been shown to promote Mtb growth\u003csup\u003e6\u003c/sup\u003e. These include increased oxidative phosphorylation and fatty acid metabolism (Figure 5F). In summary, our data suggests that the broad range of anti-inflammatory responses often associated with AM population are mediated through CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs, which are highly susceptible and sensitive to Mtb infection during the early phases of TB.\u003c/p\u003e\n\u003ch2\u003eCD38\u003csup\u003e+\u003c/sup\u003e AMs are key contributors in controlling tuberculosis infection.\u003c/h2\u003e\n\u003cp\u003eWe re-clustered the AM subsets based on their surface expression of CD38 protein. Consistent with the previous observations, CD38\u003csup\u003e+\u003c/sup\u003e cells only constituted 12% of the total infected AMs at 2 weeks, but this proportion increased to 89% and 93% at the 4 and 6-week timepoints, respectively (Figure 5G). Intriguingly, DAB testing revealed unchanging abundance of infected CD38\u003csup\u003e+\u003c/sup\u003e AMs across timepoints (Figure 4A), and mo_AMs and CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs constitute the majority of the infected alveolar macrophages in the later stages of infection (Figure 4B-4C). This stability in numbers, even amidst a pronounced decline in the overall infected AMs (Figure 4C), implies that these cellular subsets might be inherently more resilient to Mtb infection. This interpretation aligns with their observed \u003cem\u003eNos2\u0026nbsp;\u003c/em\u003eexpression patterns. Both CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs and mo_AMs exhibit a bimodal \u003cem\u003eNos2\u003c/em\u003e expression, with increasing cell counts and \u003cem\u003eNos2\u003c/em\u003e expression intensity, suggesting their antimicrobial capacity increases over time (Figure 5D). The elevated CD38 expression and pro-inflammatory pathways in AMs in latter stages, as described earlier, are predominantly represented by these two CD38\u003csup\u003e+\u003c/sup\u003e populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTrajectory and pseudotime analyses provide additional insights into these populations. While mo_AMs, CD38\u003csup\u003e-\u003c/sup\u003e TR-AM_1, and CD38\u003csup\u003e-\u003c/sup\u003e TR_AM_2 appear as mature endpoints (grey circles 12,18,17,7,5 and root 3, white circle), CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs seem to arise from enhanced polarization of the CD38\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eTR-AM_3 subset (root 1, white circle and branching point 12,17, black circles, Figure 2B). Thus, we hypothesize that the decreased presence of the CD38\u003csup\u003e-\u003c/sup\u003e TR-AM_3 subset in subsequent infection stages is likely a consequence of these cells transitioning to CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs, as highlighted in the subsequent paragraphs. Finally, enrichment analysis of the genes upregulated by CD38\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eAMs reveals up-regulation of the same pathways that are associated with control of Mtb infection (Figure 5H). These findings suggest that the divergent behavior of CD38\u003csup\u003e+\u003c/sup\u003e and CD38\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eAMs in response to Mtb infection is programmed into the cell subsets prior to infection, with the CD38\u003csup\u003e+\u003c/sup\u003e AMs playing a potentially crucial role in managing Mtb growth and spread.\u003c/p\u003e\n\u003ch2\u003eIntrapulmonary BCG vaccination amplifies the CD38\u003csup\u003e+\u003c/sup\u003e macrophage population resulting in enhanced control of Mtb infection.\u003c/h2\u003e\n\u003cp\u003eRecent investigations, including those by Mata et al. 2021\u003csup\u003e14\u003c/sup\u003e, have shown that intrapulmonary BCG vaccination prior to Mtb infection induces protective responses in lung resident macrophages. To extend this observation, we assessed how pulmonary BCG administration modulates the responses of CD38\u003csup\u003e+/-\u003c/sup\u003e macrophages to Mtb infection. Mice were vaccinated intranasally with live BCG and after a two-month period, were infected with the reporter \u003cem\u003esmyc’\u003c/em\u003e::mCherry/\u003cem\u003ehspx’\u003c/em\u003e::GFP Mtb Erdman for a duration of two weeks. Subsequently, we integrated scRNA-seq datasets from these vaccinated mice with our existing timepoint analysis data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of infected macrophage populations from both BCG-vaccinated (n=3931 cells) and unvaccinated (n=2278 cells) mouse lungs, two weeks post-Mtb infection, revealed marked differences. In the BCG-vaccinated cohort, the monocyte-derived macrophages, associated with \u003cem\u003ehspx’\u003c/em\u003e::GFP-high Mtb (Figures 1F and 2E), constituted the vast majority of infected macrophages, at 74.43%. This is in stark contrast to the unvaccinated cohort, where such cells accounted for just 43.19% (Figure 6A). Flow cytometry data from the sorted populations reinforced these findings. Macrophages from the BCG-treated group mounted a more effective response, limiting Mtb replication more efficiently. This is underscored by a marked reduction of the mCherry signal in infected cells of BCG-vaccinated mice, which was approximately ~2 log\u003csub\u003e10\u003c/sub\u003e times lower than that in infected cells from unvaccinated mice. Moreover, there was a substantial difference in the proportion of \u003cem\u003ehspx’\u003c/em\u003e::GFPstressed bacteria between the two cohorts: GFP-high cells represented 22.6% of the total infected cells in the unvaccinated group compared to 77.78% in their BCG-vaccinated counterparts (Figure 6B). The distribution of \u003cem\u003ehspx’:\u003c/em\u003e:GFP\u003csup\u003e+\u003c/sup\u003e cells between the two groups, as revealed in our scRNA-seq dataset (Supp. Figure 4B), aligned with our flow cytometry data. Complementing these findings, we observed a marked decrease in the overall percentage of infected cells in BCG treated mice (0.45%) compared to unvaccinated mice (2.08%) (Figure 6B), as also confirmed by independent flow cytometry analysis (Supp Figure 4A). This was also supported by an overall reduction in bacterial burden in BCG-treated mice, as assessed by CFU counts (Figure 6C). These differences in cellular responses between BCG-treated and unvaccinated mice highlight significant alterations in their macrophage populations. BCG-treated mouse lungs were dominated by highly pro-inflammatory monocytes, and there was a marked reduction in infected CD38\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eTR-AMs compared to unvaccinated controls (Figure 6D and 6E). The reduced proportion of infected CD38\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eTR-AMs in BCG-treated mice is likely the result of increased recruitment of pro-inflammatory monocytes, in addition to the transition of the CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs_3 into their CD38\u003csup\u003e+\u003c/sup\u003e counterparts (Figure 6E).\u003c/p\u003e\n\u003cp\u003eUsing WGCNA, we identified a unique co-expression module of 463 genes exclusive to the macrophage populations of BCG-vaccinated mice (Supp. Figure 4C) (Supp. Table 3). This module was highly enriched in pro-inflammatory genes, including \u003cem\u003eNos2\u003c/em\u003e and \u003cem\u003eCd38\u0026nbsp;\u003c/em\u003e(Supp Figure 4D). Further analysis revealed significant presence of genes involved in small GTPase signal transduction, encompassing Rab GTPases, guanine nucleotide exchange factors (GEFs) and GTPase activators (GAPs) (Supp. Figure 4E). Rab GTPases are known for their roles in endosomal trafficking, while GAPs and GEFs are vital for membrane transport, phagocytosis, and controlling of the actin cytoskeleton\u003csup\u003e85-87\u003c/sup\u003e. Their increased expression suggests modifications in intracellular trafficking within the BCG-treated macrophages. Altered Mtb intracellular trafficking and increased lysosomal fusion can limit bacterial growth\u0026nbsp;\u003csup\u003e88-90\u003c/sup\u003e and amplify killing mechanisms due to immune activation and increased autophagy\u003csup\u003e91-95\u003c/sup\u003e. Our data confirms the previous findings as we observe that increased intracellular trafficking is tightly linked to increased expression of genes involved in lysosomal and autophagy functions in BCG-treated macrophages (Figure 6F). Additionally, genes facilitating macrophage migration and tissue invasion saw heightened expression in macrophages from BCG-treated lungs (Supp.Table 3). This observation aligns with the gene-set enrichment analysis of the 463 co-expressed genes from the scWGCNA module. Here, cell migration, motility, programmed cell death and autophagy emerged as the pathways enriched in BCG-treated macrophages, in both GO and KEGG analysis (Supp Figure 5A) (Supp Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe increased expression of pro-inflammatory gene signatures involved in control of Mtb infection in BCG-treated macrophages aligns with their observed phenotype. A hallmark feature across BCG-treated mice was the increased expression of CD38 (Supp. Figure 5B). This immune control phenotype was still evident at 4 weeks post-infection, corroborating Mata et al.'s findings (Supp Figure 5C). Intriguingly, we observed discrepancies between the two studies regarding the origin of infected macrophages after BCG vaccination. In our study, the majority of infected macrophages in BCG-treated mice were monocyte-derived, while previous data indicated Mtb was mostly confined to AMs after BCG vaccination\u003csup\u003e14\u003c/sup\u003e. We believe the disparity was due to weak CD64 staining on pro-inflammatory monocyte-derived cells (Supp Figure 5D). To test this hypothesis, we re-infected BCG-treated and control mice with a high infection dose (5x10\u003csup\u003e3\u003c/sup\u003e CFU), similarly to Mata et al.\u003csup\u003e14\u003c/sup\u003e The published CD64 flow cytometry gating approach will identify most infected macrophages as AMs in BCG-treated mice\u003csup\u003e14\u003c/sup\u003e (Supp Figure 5E). However, when gating only on SiglecF, expressed uniquely by AMs (Figure 1B), the results indicate the majority of infected cells are monocyte-derived (Supp Figure 5F), as in our scRNA-seq dataset. Regardless of the gating method, we found a consistent increase in the percentage of infected AMs in BCG-vaccinated mice compared to the unvaccinated counterparts, underscoring their improved ability to restrict Mtb growth, both at 2wpi (Supp Figure 5F) and at 4wpi (Supp figure 5G)\u003csup\u003e14\u003c/sup\u003e. This aligns with our scRNA-seq results and highlights the fundamental role of the increased polarization of the CD38\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eTR-AMs_3 transitioning to CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs for enhanced Mtb control in BCG-treated AMs (Figure 6E). In conclusion, our data suggests that the heightened defense against Mtb reinfection observed after intranasal BCG vaccination is due to both the increased presence of CD38\u003csup\u003e+\u003c/sup\u003e monocyte-derived macrophages and the activation of resident CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs.\u003c/p\u003e\n\u003ch2\u003ePre-existing differences in the chromatin organization of CD38\u003csup\u003e+\u0026nbsp;\u003c/sup\u003evs CD38\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eAMs are linked to differential responses to Mtb infection.\u003c/h2\u003e\n\u003cp\u003eOur scRNA-seq analysis revealed distinct differences in AM populations between naïve and Mtb-infected mice. Specifically, we observed that CD38\u003csup\u003e+\u003c/sup\u003e moAMs are recruited to the lung in response to Mtb infection (Figure 2A). In contrast, CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs were already present prior to infection, predominantly exhibiting a less active CD38\u003csup\u003e-\u003c/sup\u003e phenotype, which we previously defined as CD38\u003csup\u003e-\u003c/sup\u003e TR-AM_3 (Figure 2A and 2B).\u003c/p\u003e\n\u003cp\u003eTo investigate the chromatin landscape and potential epigenetic regulation of AM subsets prior to infection, we performed scATAC-seq on CD45\u003csup\u003e+\u003c/sup\u003e cells isolated from the lungs of naïve mice. Unbiased clustering based on differential chromatin accessibility identified 10 distinct clusters (Figure 7A). Integrating this scATAC-seq dataset with our timepoint-specific scRNA-seq data, which includes naïve, bystander, and infected cells, revealed that variations in chromatin organization before infection align closely with the diverse transcriptional phenotypes observed during tuberculosis infection. Using gene scores, we inferred the potential gene expression profiles for each cell in the scATAC-seq sample, based on the accessibility of regulatory elements adjacent to each gene. We then performed data integration with the scRNA-seq dataset, as previously described\u003csup\u003e96\u003c/sup\u003e (Figure 7B).\u003c/p\u003e\n\u003cp\u003eGiven that our scATAC-seq sample comprised only cells from naïve mice, we anticipated that the inferred gene expression profiles from the scATAC-seq dataset would predominantly align with those of naïve cells from our scRNA-seq dataset. Surprisingly, we found that the inferred gene expression of cells from cluster C7 mirrored that of the pro-inflammatory CD38\u003csup\u003e+\u003c/sup\u003e subsets from our scRNA-seq data (Figure 7B and 7C), which by transcriptional profiling are not present in naïve mice (Figure 2A). In contrast, clusters C6 and C8 aligned with CD38\u003csup\u003e-\u003c/sup\u003e TR-AMs, while cluster C5 correlated with Mki67\u003csup\u003e+\u003c/sup\u003e AMs (Figures 7B and 7C). To validate that cluster C7 represented CD38\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eTR-AMs and not monocyte-derived AMs, we probed for open chromatin within the promoter regions of monocyte markers \u003cem\u003eMafb\u003c/em\u003e and \u003cem\u003eLy6a\u003c/em\u003e, whose expression is restricted to monocyte-derived macrophages in our scRNA-seq dataset, as noted earlier (Figure 1F). We found high levels of open chromatin for these markers only in monocyte-derived cells, but not in cluster C7 (Supp. Figure 6B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further understand why the inferred gene expression of cluster C7 aligns with pro-inflammatory populations in scRNA-seq, we first assessed transcription factor dynamics, performing marker peak and motif enrichment analysis (FDR \u0026lt; 0.1, log2FC \u0026gt; 0.5) to identify transcription factor binding sites (TFBS) that are enriched across the different scATAC-seq clusters.\u0026nbsp; Cluster C7 exhibited highly significant enrichment for binding sites of transcription factors such as \u003cem\u003eSmarcc1\u003c/em\u003e, \u003cem\u003eBach1\u003c/em\u003e, and \u003cem\u003eRela\u003c/em\u003e, known to drive pro-inflammatory gene activation in macrophages\u003csup\u003e97-99\u003c/sup\u003e (Figure 7D). We further validated the expression of these TFs in our scRNA-seq dataset and found them to be uniquely expressed by CD38\u003csup\u003e+\u003c/sup\u003e pro-inflammatory macrophages following Mtb infection (Figure 7E).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, our analysis of open chromatin peaks within the regulatory regions (± 5k from TSS) of pro-inflammatory genes that define CD38\u003csup\u003e+\u003c/sup\u003e AMs, such as \u003cem\u003eNos2, Cd38, Slc7a11, Ccl5, Ptgs2, Il1b, and Cxcl\u003c/em\u003e2, revealed higher open chromatin in cells from cluster C7 compared to clusters C6 and C8, further validating that CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs are pre-primed for a pro-inflammatory response (Supp Figure 6C).\u003c/p\u003e\n\u003cp\u003eOverall, our analysis demonstrates that CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs are present in naïve mouse lungs before infection, but are transcriptionally quiescent and have an epigenetic profile markedly distinct from their CD38\u003csup\u003e-\u003c/sup\u003e TR-AM counterparts. These CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs have increased chromatin accessibility in promoter regions of pro-inflammatory genes, with significant enrichment of transcription factor binding sites that have the potential to drive pro-inflammatory macrophage activity and control Mtb growth. Our findings support the hypothesis that the response of different TR-AM subsets to Mtb infection is largely predetermined by their intrinsic chromatin organization prior to infection.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur understanding of immune protection against tuberculosis largely comes from studying immune failure\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. Experimental infections in immune deficient mouse strains have informed us which pathways, when compromised, increase susceptibility to infection and disease\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Similarly, several mutations in the human population have been linked to increased incidence or severity of disease\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e,\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. However, if disease outcome is determined by the biology of the host macrophages, and if different macrophage populations are responsible for control or promotion of bacterial growth, focusing solely on immune failure offers a limited view of disease control. This focus has resulted in our reliance on Interferon-g Release Assays (IGRA), Mycobacterial Growth Inhibition Assays (MGIA) and similar biological readouts for vaccine development, which have proven to be non-predictive of immune protection\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur current study uses a mouse challenge model with fluorescent fitness reporter bacteria to define and characterize different macrophage populations in the infected mouse lung. We've previously found that recruited pro-inflammatory monocyte-derived IMs effectively control Mtb,\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e while AMs exhibit diverse phenotypes. We posit that the variability among these macrophage subsets significantly influences disease progression in tuberculosis.\u003c/p\u003e \u003cp\u003eIn early infection, Mtb primarily resides in CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e AMs, which show a muted response, as noted by Rothchild et al\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. As infection progresses, the AM landscape changes significantly with the recruitment of moAMs and activation of a subset of TR-AMs, leading to increased bacterial control. These macrophage subsets transition the lung environment from immune homeostasis to a pro-inflammatory state, effectively curtailing Mtb growth. The shifts in macrophage phenotypes are rooted in their intrinsic epigenetic programming, as revealed by ScATAC-seq analyses. CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs, are already present in na\u0026iuml;ve lungs, exhibit chromatin landscapes predisposed for a pro-inflammatory response, indicating epigenetic priming as a key factor in their infection response. The diverse chromatin organization of the different TR-AMs subsets before infection suggests the potential for manipulating their epigenetic programs, opening new avenues to enhance macrophage function in TB.\u003c/p\u003e \u003cp\u003eFurthermore, examination of post-infection phenotypic changes in AM subsets, particularly after intrapulmonary BCG administration, provides crucial insights into potential strategies for reprogramming these macrophages to our advantage. Our focus was not on promoting BCG as a long-term vaccination strategy, but rather on understanding how transient changes in macrophage function post-BCG administration can inform potential therapeutic targets. We observed transcriptional shifts resulting in the polarization of CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e TR-AM_3 towards the CD38\u003csup\u003e+\u003c/sup\u003e TR-AM phenotype and increased inflammatory activation of monocyte-derived macrophages in BCG-treated mice. These transcriptional responses are associated with augmented expression of pathways related to intracellular trafficking and lysosomal/autophagic functions, suggesting that intranasal BCG administration triggers the pre-activation of genetic programs inherent to the epigenetic profile of pro-inflammatory TR-AM subsets. This promotes a phenotype more effective in restricting Mtb replication. Additionally, BCG also increases the recruitment of monocyte-derived macrophages, resulting in fewer unresponsive CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e TR-AMs being infected with Mtb, further strengthening the lung myeloid populations' ability to counter the Mtb challenge. These results extend the recent reports that BCG reprograms lung AMs to better control Mtb\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. While our findings, conducted in SPF mice, do not fully capture the complex genetic and environmental influences found in human populations, the changes in macrophage phenotype observed during infection and post-BCG vaccination offer a clear path towards improved tuberculosis control. We propose that the dynamic nature of these macrophage subpopulations plays a major role in the early events following Mtb infection and throughout the course of the disease. Through understanding the underlying molecular mechanisms and the broader immune context driving the distinct responses of CD38\u003csup\u003e+\u003c/sup\u003e and CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e AMs to Mtb infection, we could guide the screening of therapeutics aimed at improving macrophage control of Mtb early in infection. However, factors such as genetic diversity, nutritional status, co-infections, and early-life BCG vaccination can significantly alter immune responses; therefore, these results will require validation under more complex conditions in humans.\u003c/p\u003e \u003cp\u003eThe significant shifts in macrophage phenotypes we've observed, especially in the context of BCG vaccination and immune function, emphasize the functional resolution of the analytical tools employed in this current study. The WGCNA method, a focal point of this and ongoing studies, has produced gene expression modules that functionally categorize various macrophage subpopulations, in both mouse and NHP infections. These modules, alongside newly identified CD markers, facilitate integration with skin and lung challenge approaches for more accurate phenotype identification of tissue-resident and monocyte-derived macrophages in relation to disease or vaccination status. We believe this represents a viable avenue to the development of predictive biomarkers for immune protection.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eMtb and BCG strains\u003c/p\u003e \u003cp\u003eThe parental strain employed for all experiments was \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e Erdman (ATCC 35801). Fluorescent reporter strains including smyc\u0026prime;::mCherry, smyc\u0026prime;::mCherry/hspx\u0026rsquo;::GFP, and hsp60\u0026prime;::GFP have been reported\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan additionalcitationids=\"CR105\" citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. Both the \u003cem\u003eM. tuberculosis\u003c/em\u003e strain and BCG (Pasteur) were cultivated at 37\u0026deg;C until they reached the mid-log phase in MiddleBrook 7H9 broth enriched with 10% OADC (Becton, Dickinson and Company), 0.2% glycerol, and 0.05% tyloxapol (Sigma-Aldrich). For the selection of fluorescent strains, Hygromycin B (50 mg/ml) was utilized. For mouse infections, bacterial aliquots were prepared in 10% glycerol, titrated, and preserved at \u0026minus;\u0026thinsp;80\u0026deg;C, following the protocol detailed in Pisu et al., 2023\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMice\u003c/p\u003e \u003cp\u003eC57BL/6J WT, B6.129P2-Nos2tm1Lau/J (NOS2\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e), B6.129S7-Ifngtm1Ts/J (IFNγ\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e), B6.129S7-Rag1tm1Mom/J (Rag1\u003csup\u003e\u0026minus;/\u0026minus;)\u003c/sup\u003e mice were purchased from The Jackson Laboratory. The mice used in this study were 6\u0026ndash;8 wk old. All mice were maintained in a specific pathogen\u0026ndash;free animal biosafety level 3 facility at Cornell University. Animal care was in accordance with the guidelines of the Association for Assessment and Accreditation of Laboratory Animal Care. All animal procedures were approved by the Institutional Animal Care and Use Committee of Cornell University.\u003c/p\u003e \u003cp\u003eMice infection and lung cells isolation\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eMtb\u003c/em\u003e infections, mice were anesthetized and intranasally inoculated with 1.5X10\u003csup\u003e3\u003c/sup\u003e CFUs of the Erdman strains (\u003cem\u003esmyc\u0026prime;\u003c/em\u003e::mCherry, \u003cem\u003ehspx\u0026prime;\u003c/em\u003e::GFP/\u003cem\u003esmyc\u0026prime;\u003c/em\u003e::mCherry, or \u003cem\u003ehsp60\u0026prime;\u003c/em\u003e::GFP) resuspended in 30 \u0026micro;l of PBS containing 0.05% Tween 80. The inoculum dose was verified by plating various dilutions of the bacterial stocks used for infection on 7H10 agar plates supplemented with OADC Enrichment and glycerol. These plates were incubated at 37\u0026deg;C, and after 3 weeks, colonies were counted. At 2, 3, 4, and 6 weeks post-infection (w.p.i.), mice were euthanized. Lungs were aseptically removed and immersed in PBS containing 5% FBS and Collagenase IV (250U/mL). To preserve the gene expression profiles of both the host and bacteria, samples were immediately processed using a GentleMACS tissue dissociator (Miltenyi Biotec) and maintained on ice. The dissociated lung material was subsequently strained through a 70-\u0026micro;M mesh, and red blood cells were lysed using ammonium-chloride-potassium (ACK) lysis buffer (Lonza)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor BCG vaccinations, mice received an intranasal dose of 2x10\u003csup\u003e6\u003c/sup\u003e CFU of BCG (Pasteur) bacilli in 30 \u0026micro;L of PBS containing 0.05% Tween 80. Post-infection, these mice were housed in pathogen-free cages at a biosafety level 2 facility at Cornell University, in preparation for re-infection with \u003cem\u003eM. tuberculosis\u003c/em\u003e Erdman. After a period of two months (60 days) from the initial vaccination, these mice underwent a secondary intranasal challenge. This challenge involved approximately 1.5x10\u003csup\u003e3\u003c/sup\u003e or 5x10\u003csup\u003e3\u003c/sup\u003e CFU of either \u003cem\u003ehspx\u0026prime;\u003c/em\u003e::GFP/\u003cem\u003esmyc\u0026prime;\u003c/em\u003e::mCherry or \u003cem\u003esmyc\u0026prime;\u003c/em\u003e::mCherry Mtb Erdman respectively, with the bacteria also resuspended in 30 \u0026micro;L of PBS containing 0.05% Tween 80. At specified intervals post-infection, specifically at 2 and 4 weeks, the lungs of these mice were aseptically removed and immersed in a solution of PBS containing 5% FBS and 250U/mL of Collagenase IV. The harvested lung tissues were then processed for subsequent scRNA-seq or flow cytometry analyses.\u003c/p\u003e \u003cp\u003eSorting of the murine lung suspensions for scRNA-seq analysis\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInfected populations\u003c/h2\u003e \u003cp\u003eTo generate single cell suspensions for cell sorting, we followed the steps 4\u0026ndash;18 of the previously published protocol\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. In brief, cells from infected mice (n\u0026thinsp;=\u0026thinsp;5/timepoint) were washed in PBS containing 5% FBS, resuspended in sorting buffer (PBS, 1% FBS, 5 mM EDTA, and 25 mM Hepes), filtered through a 40-\u0026micro;M strainer, and sorted. Throughout the sorting, samples were kept at 4\u0026deg;C and directly collected into Cell Staining Buffer (BioLegend). Mice infected with either \u003cem\u003esmyc\u0026prime;\u003c/em\u003e::mCherry or \u003cem\u003ehsp60\u0026prime;\u003c/em\u003e::GFP were used as a control to define the sorting gates for the \u003cem\u003ehspx\u0026prime;\u003c/em\u003e::GFP/\u003cem\u003esmyc\u0026prime;\u003c/em\u003e::mCherry-infected cells. For the BCG analysis, we followed the same protocol sorting n\u0026thinsp;=\u0026thinsp;5 mice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBystander populations\u003c/h2\u003e \u003cp\u003eSingle-cell suspensions from mice infected with smyc\u0026prime;::mCherry (n\u0026thinsp;=\u0026thinsp;3 / timepoint) were incubated for 20 min with fluorophore-bound CD45 antibodies (104; BD). After two PBS washes, samples were resuspended in the sorting buffer, filtered via a 40-\u0026micro;M strainer, and sorted. During sorting, samples were consistently held at 4\u0026deg;C and collected directly into Cell Staining Buffer (BioLegend).\u003c/p\u003e \u003cp\u003escRNA-seq libraries preparation and sequencing\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSample Preparation and Staining\u003c/h2\u003e \u003cp\u003eSorted cells were centrifuged at 500 g for 5 min and then resuspended in 50 \u0026micro;l of cell staining buffer containing 0.25 \u0026micro;g of TruStain FcX PLUS (BioLegend), followed by a 10 min incubation at 4\u0026deg;C. An ADT plus HTO antibody cocktail mix (50 \u0026micro;l) was then added to the samples, and the cells were further incubated for 30 min at 4\u0026deg;C. After two washes in cell staining buffer, differentially tagged samples (e.g., \u003cem\u003ehspx\u0026prime;\u003c/em\u003e::GFPhigh/\u003cem\u003ehspx\u0026prime;\u003c/em\u003e::GFPlow) were combined and resuspended in 1\u0026times; Dulbecco\u0026rsquo;s PBS. The samples were then fixed by slowly adding ice-cold methanol to a final concentration of 90% (vol/vol) and stored at \u0026minus;\u0026thinsp;20\u0026deg;C overnight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample Rehydration and mRNA Library Preparation\u003c/h2\u003e \u003cp\u003ePost-fixation, the samples were brought out of the BSL3 facility, equilibrated on ice for 15 min, and washed twice with rehydration buffer (1\u0026times; Dulbecco\u0026rsquo;s PBS with 1.0% BSA [Thermo Fisher Scientific] and 0.5 U/\u0026micro;l RNase Inhibitor [Sigma-Aldrich]). The cell count was determined prior to loading onto the 10\u0026times; chip. For mRNA library preparation, we adapted the 10\u0026times; protocol (CG000206 Rev D), making a minor alteration in step 2.2. Specifically, we incorporated 1 \u0026micro;l of ADT and HTO additive primers (0.2 \u0026micro;M stock) following the method described by Stoeckius et al\u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e. HTO and ADT libraries were prepared according to BioLegend's standard protocols.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLibrary Generation and Sequencing\u003c/h2\u003e \u003cp\u003eThe mRNA, HTO, and ADT libraries underwent quality control assessment using an Agilent Fragment Analyzer. Their concentrations were determined using the QX200 digital PCR system from Bio-Rad. Libraries were pooled in the same sequencing run at specific ratios: 90% mRNA, 5% ADT, and 5% HTO. Sequencing was performed on the NextSeq2000 (Illumina) using the 50-bp P3 NextSeq kit. The cycle distribution was: read 1 (28 cycles), i7 index (8 cycles), and read 2 (52 cycles). Sequencing depth exceeded 50,000 reads/cell.\u003c/p\u003e \u003cp\u003escATAC-seq nuclei isolation, library preparation and sequencing\u003c/p\u003e \u003cp\u003eMurine na\u0026iuml;ve lung sorting was performed as described in Pisu et al\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. For nuclei isolation, sorted cells were centrifuged at 300 rcf for 5 minutes at 4\u0026deg;C and then resuspended in 150uL of PBS supplemented with 0.04% BSA. A 100\u0026micro;L aliquot of this cell suspension was transferred to a 0.2mL flat-cap tube, centrifuged again under the same conditions, and subsequently resuspended in 50uL of Lysis Buffer (containing 10mM Tris-HCL, 10mM NaCl, 3mM MgCl2, 0.1% Tween 20, 0.1% Nonidet P40 Substitute, 0.01% Digitonin, and 1% BSA). This was incubated on ice for 4 minutes. Post-incubation, 50\u0026micro;L of Wash Buffer (comprising 10mM Tris-HCL, 10mM NaCl, 3mM MgCl2, 1% BSA, and 0.1% Tween 20) was added to the lysed cells. The nuclei suspension was then centrifuged at 500 rcf for 5 minutes at 4\u0026deg;C. The nuclei were washed with 45\u0026micro;L of a 1:20 dilution of the Nuclei Buffer (10x Genomics, PN-2000153/2000207) and centrifuged again using the same conditions. Finally, the isolated nuclei were resuspended in a volume of Diluted Nuclei Buffer to obtain a concentration ranging from 3080 to 7700 nuclei/\u0026micro;L. This was used as input for the 10X protocol (CG 000209 Rev D) targeting a recovery of 10,000 nuclei.\u003c/p\u003e \u003cp\u003eThe transposition reaction and library construction were performed following the protocol from 10X (CG000209 REV D). Sequencing was conducted on a NextSeq 500 with the parameters: Read 1N: 50 cycles, i7 index: 8 cycles, i5 index: 16 cycles, and Read 2N: 50 cycles.\u003c/p\u003e \u003cp\u003eAntibodies Used for scRNA-seq\u003c/p\u003e \u003cp\u003eFor our scRNA-seq timepoint experiments, we used a range of TotalSeq (BioLegend) murine antibodies in our antibody cocktail mix, each at a concentration of 0.5 \u0026micro;g/sample. These antibodies included SiglecF (custom-made, clone S17007L), CD64 (cat. # 139325), Ly6G (cat. # 127655), CD11c (cat. # 117355), CD14 (cat. # 123333), Ly6G-Ly6C (cat. # 108459), CD63 (cat. # 143915), F4/80 (cat. # 123153), CD38 (cat. # 102733), TLR4 (cat. # 117614), CD11b (cat. # 101265), CD16/32 (cat. # 101343), CD86 (cat. # 105047), CD1d (cat. # 123529), CD3 (cat. # 100251), CD4 (cat. # 100569), and CD8a (cat. # 100773). In addition, for hashing purposes, we used BioLegend's Hashtag 1 murine (cat. # 155801), Hashtag 2 murine (cat. # 155803) antibodies.\u003c/p\u003e \u003cp\u003eFlow cytometry analysis\u003c/p\u003e \u003cp\u003eLung cell suspensions were counted and incubated for 30 min in the dark at room temperature with fluorophore-conjugated antibodies, washed twice with PBS 1\u0026times;, and fixed in 4% paraformaldehyde. Antibody panels and Fluorescence Minus One controls were generated as appropriate. For this study, we used fluorochrome-conjugated mAbs specific to mouse SiglecF (E50-2440; Becton Dickinson), CD64 (X54-5/7.1; BioLegend), MerTK (2B10C42; BioLegend), CD38 (90; Biolegend) and CD45 (104; Becton Dickinson), along with the following reporter strains: smyc\u0026prime;::mCherry (mCherry), hsp60\u0026prime;::GFP (GFP), and hspx\u0026prime;::GFP/smyc\u0026prime;::mCherry. Cells were analyzed with a Symphony A3 (BD Biosciences). Data were analyzed using FlowJo software (version 10.9; BD).\u003c/p\u003e \u003cp\u003eQuantification of bacterial loads\u003c/p\u003e \u003cp\u003eAt 2 weeks post-infection (for BCG-vaccinated) and at 4 weeks post-infection (for B6.129P2-Nos2tm1Lau/J (NOS2\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e)) mice were sacrificed and the lung lobes homogenized in PBS containing 0.05% tyloxapol (Sigma-Aldrich). Bacterial loads were determined by plating serial dilutions of the homogenates on 7H10 agar. Plates were incubated at 37\u0026deg;C and colonies enumerated 3\u0026ndash;4 weeks after.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eData Acquisition and QC\u003c/h2\u003e \u003cp\u003eSequencing data derived from each run underwent processing using distinct software tailored to the library type. mRNA libraries were processed using the CellRanger software (v. 6.0) from 10X Genomics and the mouse mm10 genome (GRCm38). The ADT and HTO libraries were processed using CITE-Seq-Count (v. 1.4.3), available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hoohm.github.io/CITE-seq-Count/\u003c/span\u003e\u003cspan address=\"https://hoohm.github.io/CITE-seq-Count/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This processing yielded raw count matrices for both mRNA and proteins. Downstream data analysis was conducted in Seurat (v. 4.3), following methods outlined by Stuart et al\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e. Cells with less than 200 unique genes or with over 30% of the total reads belonging to mitochondria were filtered out. The HTOs multiplexed samples were then demultiplexed using the MULTIseqDemux() function in Seurat\u003csup\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e. This step allowed for the removal of doublets and empty droplets and ensured the correct assignment of identities to each sample.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eSingle-Cell RNA Sequencing Data Preprocessing\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eAll scRNA-seq datasets from individual timepoints were preprocessed using Seurat's regularized negative binomial regression, where both the number of counts and the percentage of mitochondrial reads were regressed out, as per Hafemeister and Satija\u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e. For analysis of the macrophage populations in the uninfected na\u0026iuml;ve lungs we used the previously generated datasets from Pisu et al\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Metadata columns, namely \u0026ldquo;\u003cem\u003eTimepoint\u003c/em\u003e\u0026rdquo;, \u0026ldquo;\u003cem\u003eBatch\u003c/em\u003e\u0026rdquo;, \u0026ldquo;\u003cem\u003eInfection Status\u003c/em\u003e\u0026rdquo;, and \u0026ldquo;\u003cem\u003eVaccination Status\u003c/em\u003e\u0026rdquo;, were added to each dataset prior to subsequent steps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eData Merging and Integration\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eDatasets were combined into a unified Seurat object. The raw counts contained in the RNA slot of the merged object were used as an input for Harmony integration\u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. Raw counts underwent log-normalization, followed by the identification of the top 3,000 variable genes. These genes were scaled and centered. PCA was then performed on these values. Integration in Harmony incorporated \u0026ldquo;\u003cem\u003eBatch\u003c/em\u003e\u0026rdquo;, \u0026ldquo;\u003cem\u003eInfection Status\u003c/em\u003e\u0026rdquo;, \u0026ldquo;\u003cem\u003eTimepoint\u003c/em\u003e\u0026rdquo;, and \u0026ldquo;\u003cem\u003eVaccination Status\u003c/em\u003e\u0026rdquo; as covariates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eCluster Detection and Annotation\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eUsing the Harmony-aligned embeddings, graph-based cluster detection was achieved utilizing principal components (PCs) that had been marked statistically significant by the jackstraw method\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. For community detection, we employed the Louvain algorithm\u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e. Cell types for each identified cluster were annotated using both reference-based\u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e and canonical marker genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eTrajectory and Pseudotime Analysis\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThis analysis was carried out in Monocle (v3.0)\u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e. The integrated Seurat object was converted using the SeuratWrappers package in R (v. 0.2.0). For unbiased trajectory and pseudotime analysis of the macrophage populations, all cells classified as macrophages were assigned to the same partition, and trajectory/pseudotime analysis was conducted as previously described\u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e. White circles indicate the root origins of the trajectory, grey circles indicate destination fates and black circles indicate branching points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGeneration of a force layout embedding (FLE)\u003c/h2\u003e \u003cp\u003eTo visualize infected and bystander cells in a force layout embedding (FLE) we used the methods described in Waddington-OT\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Briefly, using the Pegasus library (pegasus), a PCA was performed on the expression data using the function pg.pca(). This was followed by the determination of nearest neighbors using pg.neighbors(). Subsequently, a diffusion map was generated through the function pg.diffmap() to visualize the transition between cellular states. To visualize the data, the FLE algorithm implemented in Pegasus was employed by invoking pg.fle(). The resulting 2D coordinates were then extracted for further visualization. A scatter plot was generated using matplotlib to represent cells in the 2D space derived from the FLE calculations. Cells were color-coded based on their respective timepoints (weeks).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTime-dependent pathway analysis\u003c/h2\u003e \u003cp\u003eFor our time-dependent pathway analysis, we employed the Tempora package\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. First, the integrated Seurat object was subset to only contain macrophage clusters. Subsequently, the \u0026ldquo;ImportSeuratObject()\u0026rdquo; function was employed to prepare the data for Tempora, where clusters and timepoints were explicitly defined. Given the necessity of pathway enrichment analysis, gene set files were fetched from the BaderLab online resource. We specifically selected gene set files that incorporated all pathways, excluding those inferred from electronic annotations (IEA). The downloaded gene matrix transposed (GMT) file was then leveraged for pathway enrichment calculations using the GSVA method with \u0026ldquo;CalculatePWProfiles()\u0026rdquo;. To construct the cellular trajectory, the \u0026ldquo;BuildTrajectory()\u0026rdquo; function was applied, with the number of principal components set to 11 and a statistical significance threshold of \u0026lt;\u0026thinsp;0.05. The trajectory visualization was achieved using the \u0026ldquo;PlotTrajectory()\u0026rdquo; function, and subsequently, generalized additive models (GAMs) were employed to identify significant time-varying pathways through the \u0026ldquo;IdentifyVaryingPWs()\u0026rdquo; function. The temporal dynamics of these pathways were illustrated using a custom ggplot function, \u0026ldquo;ggplotVaryingPWs()\u0026rdquo; for improved visualizations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA co-expression analysis\u003c/h2\u003e \u003cp\u003eWGCNA is an analytical pipeline used extensively by developmental biologists, that support the iterative, unbiased assembly of gene expression modules that define cell populations of interest. To perform weighted gene co-expression network analysis on our scRNA-seq datasets, we employed the scWGCNA package\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Pseudocells were computed using the \u0026ldquo;calculate.pseudocells()\u0026rdquo; function from the scWGCNA package. This method aggregates single cells into pseudocells to reduce the complexity of the dataset. A fraction of 0.2 of the cells were used as seeds, and for each seed, 10 nearest neighbors were aggregated based on the Harmony dimensional reduction. For each analysis, the selected single cell data was normalized using the \u0026ldquo;LogNormalize()\u0026rdquo; method with a scaling factor of 10,000. Variable features were identified using the Variance Stabilizing Transformation (VST) method, and the top 3,000 features were retained.\u003c/p\u003e \u003cp\u003eSubsequently, pseudocells (pcells) and the variable genes (var.genes) identified in the previous step were used in the \u0026ldquo;scWGNA()\u0026rdquo; function to identify modules of co-expressed genes. Membership tables were inspected to understand the genes belonging to each module, and average expressions of each co-expression module per cell were analyzed. Eigengenes for the co-expression modules were computed using the \u0026ldquo;scW.eigen()\u0026rdquo; function. This function collapses the expression profile of each module into a single representative profile known as the module eigengene. The average expression of each module was then visualized on a UMAP plot using a customized \u0026ldquo;scW.p.expression()\u0026rdquo; function for improved visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Abundance Testing with Milo\u003c/h2\u003e \u003cp\u003eWe used the neighborhood-based statistical framework \u0026ldquo;Mylo\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e to test for changes in the abundance of infected and bystander macrophage populations across timepoints. To determine the best parameters for running the model both the neighborhood cell distribution and the distribution of uncorrected P-values were assessed. A k-nearest neighbors (k-NN) graph was constructed on the data using the \u0026ldquo;buildGraph()\u0026rdquo; function, with \u003cem\u003ek\u003c/em\u003e set to 10, and using Harmony as the dimensionality reduction method. The neighborhoods were then defined on this graph using the \u0026ldquo;makeNhoods()\u0026rdquo; function, using the same \u003cem\u003ek\u003c/em\u003e and \u003cem\u003ed\u003c/em\u003e parameters as before. Subsequently, cells were counted within these neighborhoods based on their originating samples using the \u0026ldquo;countCells()\u0026rdquo; function. The neighborhoods were tested for differential abundance using the \u0026ldquo;testNhoods()\u0026rdquo; function. This test took into account a design matrix (consisting of a sample identifier and a variable for the timepoint) and the Harmony dimensions. Results were then sorted by the spatial False Discovery Rate (SpatialFDR). The neighborhood graph was further constructed using the \u0026ldquo;buildNhoodGraph()\u0026rdquo; function. Visualizations of the neighborhood graph highlighting the differential abundance results were then generated. Following this, neighborhoods were annotated based on cell identity, and a histogram was plotted to visualize the fraction of identified cells. Neighborhoods with an identity fraction less than 0.4 were labeled as \"Mixed\". Bee swarm plots were generated to further visualize the data based on these identities. Finally, neighborhoods were grouped using the \u0026ldquo;groupNhoods()\u0026rdquo; function, with a max.lfc.delta of 2. The resultant grouped neighborhoods were visualized on a UMAP plot and further explored through bee swarm plots based on their LFC differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003escCODA\u003c/h2\u003e \u003cp\u003eWe used scCODA to investigate changes in cell composition across different timepoints during Mtb infection. The cell count data was reshaped to match the format required for scCODA. This involved mapping the original timepoints to new categories (e.g., \u0026ldquo;2 Weeks\u0026rdquo; and \u0026ldquo;3 Weeks\u0026rdquo;' to \u0026ldquo;Early Timepoint\u0026rdquo;, \u0026ldquo;4 Weeks\u0026rdquo; and \u0026ldquo;6 Weeks\u0026rdquo; to \u0026ldquo;Late Timepoint\u0026rdquo;) and summing counts when necessary to consolidate the data into these new categories. The reshaped data was loaded into a pandas DataFrame, where each column represented a different timepoint and rows represented individual cell types. This DataFrame was then converted to an AnnData object, which is the data structure required by scCODA for compositional data analysis. The analysis focused on comparing the composition of macrophage subsets at these defined timepoints to identify significant changes in their proportions that could be linked to infection progression. The compositional analysis model was created using scCODA's \u0026ldquo;CompositionalAnalysis\u0026rdquo; class, specifying \u0026ldquo;Timepoint\u0026rdquo; as the covariate and \u0026ldquo;automatic\u0026rdquo; as the reference cell type. The model was then run to perform Hamiltonian Monte Carlo (HMC) sampling and generating posterior distributions for the compositional changes between the specified groups.\u003c/p\u003e \u003cp\u003eResults from the scCODA model were summarized to determine which cell types showed significant changes in proportion relative to the reference group across the study's timepoints.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePathway enrichment analysis\u003c/h2\u003e \u003cp\u003ePathway enrichment analysis was performed using G::profiler\u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. For each analysis, we created an ordered by fold change (FC) (for DGE) or membership value (for scWGCNA modules) list of genes as a query, selecting only those genes where adjusted P value (p-adj)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The analysis was performed using the g:SCS method for multiple testing correction, the gene ontology (GO), KEGG and Reactome databases as a data source, and the default settings for the other parameters in G::profiler. Only pathways enriched with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Manual exploration of the gene lists for each analysis has also been performed to identify relevant themes for genes whose function is described in the literature (e.g., Small GTPase signal transduction). For this purpose, we only considered genes whose FC was absolute\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and p-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, or membership value for scWGCNA modules\u0026thinsp;\u0026gt;\u0026thinsp;0.4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003escATAC-seq chromatin accessibility analysis\u003c/h2\u003e \u003cp\u003eWe utilized the ArchR software\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e to perform integrative single-cell chromatin accessibility analysis. For the analysis we used the precompiled version of the mm10 genome in ArchR. Quality filtering parameters for data pre-processing included filtering out cells with TSS enrichment scores below 4 and less than 1000 unique fragments. As an additional step for quality control, we performed doublet identification and removal. Doublet scores were added to the Arrow files using the \u0026ldquo;addDoubletScores()\u0026rdquo; function with parameters \u003cem\u003eknnMethod\u003c/em\u003e\u0026thinsp;=\u0026thinsp;UMAP, \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10 and \u003cem\u003eLSIMethod\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1. Post doublet identification, cells suspected to be doublets were filtered using \u0026ldquo;filterDoublets()\u0026rdquo;, reducing the initial nuclei count from 9371 to 8493. Subsequently, dimensionality reduction has been performed using the Iterative Latent Semantic Indexing (LSI) on the insertion count matrix with the \u0026ldquo;addIterativeLSI()\u0026rdquo; function in ArchR for 4 iterations. Clustering was performed on the IterativeLSI dimensions using Seurat with a resolution of 0.7. UMAP embeddings were added with the addUMAP() function using parameters \u003cem\u003enNeighbors\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30, \u003cem\u003eminDist\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5 and \u003cem\u003emetric\u003c/em\u003e\u0026thinsp;=\u0026thinsp;cosine. The UMAP was plotted and color-coded by clusters.\u003c/p\u003e \u003cp\u003eTo identify marker genes for each cluster, gene expression for marker genes was estimated from chromatin accessibility data by using gene scores. A gene score is considered as a prediction of how highly expressed a gene will be based on the accessibility of regulatory elements in the vicinity of the gene\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. To create the gene scores, we used distance-weighted accessibility models as defined in ArchR and implemented in the function \u0026ldquo;addGeneScoreMatrix()\u0026rdquo;. Marker genes were then identified for each cluster using the Gene Score Matrix with a Wilcoxon test method. The markers for each cluster were filtered using an FDR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.05 and Log2FC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.58. Imputed weights were added using the function \u0026ldquo;addImputeWeights()\u0026rdquo; to visualize the marker genes e.g., \"\u003cem\u003eMafb\u003c/em\u003e\" and \u0026ldquo;\u003cem\u003eLy6a\u003c/em\u003e\u0026rdquo; on the UMAP. To visualize the local chromatin accessibility around specific genes on a per cluster basis, the \u0026ldquo;plotBrowserTrack\u0026rdquo; function was employed, considering 5,000 base pairs both upstream and downstream of the start of the genes of interest.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eIntegration of scRNA-seq and scATAC-seq data\u003c/h2\u003e \u003cp\u003eSingle-cell timepoint RNA-seq data (scRNA-seq) was utilized for integration with ATAC-seq data. Unconstrained integration, a completely agnostic approach that takes all of the cells in the scATAC-seq experiment and attempt to align them to any of the cells in the scRNA-seq experiment, was used. The \u0026ldquo;addGeneIntegrationMatrix()\u0026rdquo; function was used to generate a gene integration matrix, which was named \"GeneIntegrationMatrix\". For visualization, a palette was derived from the scRNA-seq data's cell type categories. An embedding plot was generated using the \u0026ldquo;plotEmbedding()\u0026rdquo; function, colored by predicted cell groups to label and visualize the scATAC-seq clusters with the cell types predicted from our scRNA-seq dataset.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eTranscription Factor (TF) Analysis\u003c/h2\u003e \u003cp\u003eFor TF analysis, the following steps were performed: 1) A reproducible peak set was determined using the \u0026ldquo;addReproduciblePeakSet()\u0026rdquo; function, with MACS2 as the peak caller\u003csup\u003e\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e. The \u0026ldquo;getMarkerFeatures()\u0026rdquo; function was employed to identify marker peaks unique to an individual cluster or a small group of clusters in an unsupervised fashion, using the above calculated peak matrix and accounting for biases such as TSS Enrichment and fragment counts. Significant marker peaks were determined with criteria set at FDR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.05 \u0026amp; Log2FC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.58. 2) To assess whether marker peaks or differential peaks were enriched for binding sites of specific transcription factors, we performed motif and feature enrichment analysis. We annotated the ArchR project with motif information using the \u0026ldquo;addMotifAnnotations()\u0026rdquo; function, employing the \"cisbp\" motif set. Analysis of enrichment of motifs within marker peaks was performed with the \u0026ldquo;peakAnnoEnrichment()\u0026rdquo; function, using the following criteria FDR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.1 \u0026amp; Log2FC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.5. The enriched transcription factor binding sites (TFBS) for each scATAC-seq cluster were then visualized in a ranked scatter plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003escRNA-seq\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was performed using the nonparametric Wilcoxon rank-sum test as implemented in Seurat\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e. Only genes with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 between two comparisons were considered statistically significant. Unless otherwise specified, the Wilcoxon rank-sum test followed by FDR correction has also been used to compare the distribution of a specific gene expression among two groups of cells in different plots and visualizations. For plots where the distribution of values (eg: CD38 protein level) have been compared, for each timepoint, across two conditions (eg: Bystander vs Infected) pairwise t-tests has been performed to determine which pairs of groups are different from each other. The p-values from the pairwise t-tests are adjusted for multiple comparisons using the Benjamini-Hochberg (BH) method. The Kruskal-Wallis test has been used to analyze the differences among group medians in a sample (eg: if the medians of protein expression levels differ significantly across clusters). The Dunn's test has been used following the Kruskal-Wallis test to determine which specific groups' distributions differ from each other and the Bonferroni method has been used to adjust p-values for multiple comparisons. In addition, effect sizes for differences in expression between groups were quantified using Cliff's Delta, which measures the probability that a randomly selected value from one group will be greater than a randomly selected value from the other group. Cliff's Delta values range from \u0026minus;\u0026thinsp;1 to 1, where 0 indicates no effect, and values closer to -1 or 1 indicate stronger effects. The Cliff's Delta was computed using the cliff.delta() function from the \u0026ldquo;effsize\u0026rdquo; R package. Data integration and batch effect removals were performed with Harmony as previously described\u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. ADT and HTO data were normalized using a centered log ratio transformation, implemented in the function \u0026ldquo;NormalizeData()\u0026rdquo; with normalization.method = \u0026lsquo;\u0026lsquo;CLR,\u0026rsquo;\u0026rsquo; in Seurat. RNA counts were log-normalized and scaled before PCA and data integration with Harmony. Data visualizations were generated on the log-normalized counts for the feature plots, scatter plots, and violin plots. Heatmaps and dot plot charts were generated on the scaled expression data, as per default in Seurat.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003escATAC-seq\u003c/h2\u003e \u003cp\u003eMarker genes distinguishing the different cell clusters were identified using the getMarkerFeatures() function on the GeneScoreMatrix. The Wilcoxon rank-sum test was utilized to assess the significance of predicted differential gene expression between cell clusters. Biases such as TSS Enrichment and fragment counts (log10(nFrags)) were accounted for during the analysis. Genes were considered as markers based on criteria set at a FDR of \u0026lt;\u0026thinsp;=\u0026thinsp;0.05 and a Log2 Fold Change (Log2FC) of \u0026gt;\u0026thinsp;=\u0026thinsp;0.58. The FDR was controlled using the Benjamini-Hochberg procedure. To identify marker peaks associated with different cell clusters, a Wilcoxon rank-sum test was employed on the PeakMatrix, accounting for biases such as TSS (Transcription Start Site) Enrichment and fragment counts. Marker peaks were considered significant based on criteria set at a FDR of \u0026lt;\u0026thinsp;=\u0026thinsp;0.05 and a Log2FC of \u0026gt;\u0026thinsp;=\u0026thinsp;0.58. The FDR was controlled using the Benjamini-Hochberg procedure to reduce the chances of false positives arising from multiple testing. Transcription factor (TF) motif enrichment within the identified marker peaks was assessed using the peakAnnoEnrichment() function. This function performs hypergeometric enrichment of a given peak annotation within the defined marker peaks.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry analysis\u003c/h2\u003e \u003cp\u003eMFI of the GFP signal for the hspx-high and hspx-low populations at different timepoints was calculated using the software FlowJo (v. 10.9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eCFU data\u003c/h2\u003e \u003cp\u003eIn this study, CFUs were quantified and analyzed to assess differences in bacterial burden between different conditions/treatments. Log10 transformations were applied to each CFU count prior to further analysis. To ensure the appropriateness of parametric tests, the data were first checked for normality using the Shapiro-Wilk test, a method suited for small sample sizes. This test evaluates the hypothesis that a sample comes from a normally distributed population, which is a critical assumption for the application of a two-sample t-test. Further, to assess the equality of variances between the treatment groups, Levene\u0026rsquo;s test was performed. Upon confirming that the assumptions of normality and homogeneity of variances were satisfied, a two-sample t-test was conducted to determine if there were statistically significant differences in the mean log-transformed CFU counts between the groups. The results of the t-test, including the p-values, were used to infer the statistical significance of the differences observed. The CFU plots presented in the results section are annotated with summary statistics including the mean and standard deviation (SD) of each group, along with the sample size (n), and the p-value from the t-test.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLead Contact and Materials Availability\u003c/h3\u003e\n\u003cp\u003eFurther information and requests for resources, reagents and protocols should be directed to and will be fulfilled by the Lead Contact, David G. Russell ([email protected]). This study did not generate new unique reagents. Plasmid, bacterial and mouse strains, antibodies and other reagents and protocols used in this study will be available upon request.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The datasets supporting the conclusion of this study are available in the Gene Expression Omnibus (GEO) under accession numbers: GSE245950 (scRNA-seq) and GSE245836 (scATAC-seq). The scRNA-seq datasets for the 3-week timepoint and naïve lung were previously published and are available under accession number: GSE167232.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e All animal protocols were approved by the Institutional Animal Care and Use Committee of Cornell University. \u0026nbsp;The work was supported by grants AI134183, AI155319, and AI162598 to DGR from the National Institutes of Health, USA. \u0026nbsp;We are grateful to Jordan Rhen for technical and organizational support. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eD. Pisu and D.G. Russell designed the study. D. Pisu performed experiments. D. Pisu analyzed the scRNA-seq and scATAC-seq data. D. Pisu, J. Mattila and D.G. Russell analyzed and interpreted results. D. Pisu, J. Mattila and D.G. Russell drafted and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOrganization W (2022) Global Tuberculosis Report\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen SB, Gern BH, Delahaye JL, Adams KN, Plumlee CR, Winkler JK, Sherman DR, Gerner MY, Urdahl KB (2018) Alveolar Macrophages Provide an Early Mycobacterium tuberculosis Niche and Initiate Dissemination. 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Genome Biol 9:R137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/gb-2008-9-9-r137\u003c/span\u003e\u003cspan address=\"10.1186/gb-2008-9-9-r137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3934768/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3934768/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTuberculosis, caused by \u003cem\u003eM.tuberculosis\u003c/em\u003e (Mtb), remains an enduring global health challenge, especially given the limited efficacy of current therapeutic interventions. Much of existing research has focused on immune failure as a driver of tuberculosis. However, the crucial role of host macrophage biology in controlling the disease remains underappreciated. While we have gained deeper insights into how alveolar macrophages (AMs) interact with Mtb, the precise AM subsets that mediate protection and potentially prevent tuberculosis progression have yet to be identified. In this study, we employed multi-modal scRNA-seq analyses to evaluate the functional roles of diverse macrophage subpopulations across different infection timepoints, allowing us to delineate the dynamic landscape of controller and permissive AM populations during the course of infection.\u003c/p\u003e\n\u003cp\u003eOur analyses at specific time-intervals post-Mtb challenge revealed macrophage populations transitioning between distinct anti- and pro-inflammatory states. Notably, early in Mtb infection, CD38\u003csup\u003e-\u003c/sup\u003e AMs showed a muted response. As infection progressed, we observed a phenotypic shift in AMs, with CD38\u003csup\u003e+\u003c/sup\u003e monocyte-derived AMs (moAMs) and a subset of tissue-resident AMs (TR-AMs) emerging as significant controllers of bacterial growth. Furthermore, scATAC-seq analysis of naïve lungs demonstrated that CD38\u003csup\u003e+\u003c/sup\u003e TR-AMs possessed a distinct chromatin signature prior to infection, indicative of epigenetic priming and predisposition to a pro-inflammatory response. BCG intranasal immunization increased the numbers of CD38\u003csup\u003e+\u003c/sup\u003e macrophages, substantially enhancing their capability to restrict Mtb growth.\u003c/p\u003e\n\u003cp\u003eCollectively, our findings emphasize the pivotal, dynamic roles of different macrophage subsets in TB infection and reveal rational pathways for the development of improved vaccines and immunotherapeutic strategies.\u003c/p\u003e","manuscriptTitle":"CD38+ Alveolar macrophages mediate early control of M. tuberculosis proliferation in the lung","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 06:28:02","doi":"10.21203/rs.3.rs-3934768/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"719efce5-c6d1-4600-aab1-c02076a1f7cd","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33728702,"name":"Biological sciences/Immunology/Infectious diseases/Tuberculosis"},{"id":33728703,"name":"Biological sciences/Microbiology/Bacteriology"}],"tags":[],"updatedAt":"2024-10-15T05:46:55+00:00","versionOfRecord":{"articleIdentity":"rs-3934768","link":"https://doi.org/10.1038/s41467-024-52846-w","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-10-02 04:00:00","publishedOnDateReadable":"October 2nd, 2024"},"versionCreatedAt":"2024-07-15 06:28:02","video":"","vorDoi":"10.1038/s41467-024-52846-w","vorDoiUrl":"https://doi.org/10.1038/s41467-024-52846-w","workflowStages":[]},"version":"v1","identity":"rs-3934768","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3934768","identity":"rs-3934768","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}