Cellular sources and targets of type I interferons that drive susceptibility to tuberculosis

Mycobacterium tuberculosis ( Mtb ) causes 1.5 million deaths annually. Active tuberculosis correlates with a neutrophil-driven type I interferon (IFN) signature, but the cellular mechanisms underlying tuberculosis pathogenesis remain poorly understood. We found interstitial macrophages (IMs) and plasmacytoid dendritic cells (pDCs) are dominant producers of type I IFN during Mtb infection in mice and non-human primates, and pDCs localize near human Mtb granulomas. Depletion of pDCs reduces Mtb burdens, implicating pDCs in tuberculosis pathogenesis. During IFN-driven disease, we observe abundant DNA-containing neutrophil extracellular traps (NETs) known to activate pDCs. Single cell RNA-seq reveals type I IFNs act on IMs to impair their responses to IFN γ , a cytokine critical for Mtb control. Cell type-specific disruption of the type I IFN receptor suggests IFNs act on IMs to inhibit Mtb control. We propose pDC-derived type I IFNs, driven by NETs, act on IMs to drive bacterial replication, further neutrophil recruitment, and active tuberculosis disease. were Data were pathology 8 lung samples and 8 node samples were analyzed. Each sample had previously culture Mycobacterium tuberculosis The samples were formalin-fixed and paraffin-embedded. Sections were cut and stained with anti-CD123 (6h6, Thermo Fisher Scientific), anti-CD303 (124B3.13, Dendritics), or hematoxylin and eosin 94,95 . Primary antibodies were detected by immunoperoxidase staining with the LSAB+ System and a standard DAB reaction following manufacturer’s instructions (DakoCytomation). Sections were counterstained with hematoxlyin prior to mounting and microscopy. pDCs were assessed in multiple each singe and CX3CR1 (SA011F11, BioLegend). All samples were also stained with a unique anti-mouse TotalSeq-A Hashtag antibody (1-6; BioLegend) to allow up to 6 populations to be multiplexed in a single lane on a 10X Genomics Chromium Next GEM Chip 96 . Enriched cells from infected mice were also stained with PE-labeled B220 (RA3-6B2, Tonbo), PE-labeled CD90.2 (30-H12, Tonbo), and BV785-labeled CD45.2 (104, BioLegend) anti-mouse antibodies. Enriched cells from naïve mice were stained with Pacific Blue-labeled B220 (RA3-6B2, BioLegend), Pacific Blue-labeled CD90.2 (53-2.1, BioLegend), PE-labeled F4/80 (BM8, Thermo Fisher Scientific), and BV786-labeled CD45.2 (104, BioLegend) anti-mouse antibodies. All post-enrichment antibody staining was performed on ice for 45 minutes in the presence of True-Stain Monocyte Blocker (BioLegend). Following staining, cells were resuspended in PBS with 2% Newborn Calf Serum and Sytox Blue Dead Cell Stain (Thermo Fisher Scientific). Cells were sort purified using a 100 µm microfluidic sorting chip in a 4 laser SH-800 cell sorter (Sony) on the purity setting. The isolated populations from infected lungs were Mtb -infected cells and bystander myeloid cells. Macrophages and a mixture of neutrophils and monocytes were isolated from the naïve lungs and the macrophages were combined with neutrophil/monocyte mixture at a 1:2 ratio for better macrophage representation in the resulting dataset.


Introduction
Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis disease, caused 1.5 million deaths in 2020 1 . Treatment requires a minimum 4-6 month course of antibiotics, or up to 2 years for increasingly prevalent multi-drug resistant strains. Moreover, the only approved vaccine for Mtb has variable or no efficacy in adults 2 . The pathophysiology of tuberculosis remains poorly understood. The mouse model has been essential to identify most of the host factors known to control tuberculosis in humans, including tumor necrosis factor and interferonγ [3][4][5] . Nevertheless, the use of mice has been criticized for poorly recapitulating key aspects of human disease 6 .
In humans, active tuberculosis disease is associated with the induction of anti-viral type I interferons (IFNs) [7][8][9][10] . A causal role for type I IFNs in driving human tuberculosis is supported by the finding that a partial loss-of-function mutation in type I IFN receptor (IFNAR) is associated with Mtb resistance in humans 11 . Conversely, infection with type I IFN inducing viruses is associated with worse Mtb infection outcomes in humans. For example, influenza infection correlates with an increased risk of death among patients with pulmonary tuberculosis, and infants with cytomegalovirus have an increased risk of tuberculosis disease [12][13][14] . A type I IFN response has the potential to be a driver of progression to active Mtb in humans, as type I IFNs can antagonize the critical, protective IFNγ response during mycobacterial infection 5,15,16 . Nevertheless, a causal and mechanistic link between type I IFNs and tuberculosis disease are extremely difficult to establish in human or non-human primate studies.
Mice are able to model the viral induced loss of Mtb control seen in humans, with chronic lymphocytic choriomeningitis virus (LCMV), acute LCMV, pneumonia virus of mice, and influenza A virus co-infection with Mtb all exacerbating tuberculosis disease 17-20 . Additionally, virus-induced type I IFN drove the loss of Mtb control during the acute LCMV and influenza coinfections 17,18 . However, co-infection studies make it challenging to understand the cellular mechanism behind Mtb susceptibility, as perturbations such as type I IFN receptor blockade simultaneously impact viral and bacterial control. Therefore, an ideal platform for studying how type I IFN induces loss of Mtb control would be mice in which Mtb infection is sufficient to induce a hyper type I IFN response. However, C57BL/6 (B6) mice, the most used model for Mtb infection, generate a weak type I IFN response in response to Mtb infection without overt manipulation such as intranasal administration of poly I:C 9,21,22 . Indeed, IFNAR deletion from unmanipulated B6 mice does not impact bacterial burdens in the lungs [23][24][25] . Therefore, it has been challenging to decipher the cellular mechanism behind type I IFN production during Mtb infection and the effect of type I IFN on bacterial control.
Unlike B6 mice, C3H and 129 mice exhibit a type I interferon driven susceptibility to Mtb 26 , but there are limited genetic tools in these mouse strains, making mechanistic studies difficult. We have recently discovered that congenic B6 mice with the 'super susceptibility to tuberculosis 1' region from C3H mice (B6.Sst1 S ) 27,28 are highly susceptible to Mtb infection due to their strong type I IFN response. IFNAR deletion fully rescues the susceptibility of B6.Sst1 S mice at early timepoints and enhances survival 29 . We recently identified Sp140 as the gene responsible for the Sst1 S phenotype and confirmed that the early Mtb susceptibility of Sp140 -/mice is also rescued by IFNAR deletion 30 . As Sp140 -/mice were generated on a pure C57BL/6J background, this mouse model allows for mechanistic studies of the type I IFN response during Mtb infection.
In the present study, we leveraged Sp140 -/mice to identify the cellular mechanisms by which type I IFN drives Mtb susceptibility. Single cell RNA-sequencing (scRNA-seq) identified interstitial macrophages (IMs) as a major type I IFN producer. A sensitive genetic reporter of type I IFN production corroborated the scRNA-seq findings, and also revealed that plasmacytoid dendritic cells (pDCs) are an additional source of type I IFN during Mtb infection. Type I IFN production by pDCs appears to drive disease since pDC depletion rescued the susceptibility of Sp140 -/mice. Type I IFN conferred susceptibility by acting on IMs, dampening their ability to respond to IFNγ 16,31,32 . Loss of bacterial control in Sp140 -/mice leads to an influx of neutrophils and abundant production of DNA-rich neutrophil extracellular traps (NETs), ligands known to promote type I IFN production by pDCs 33,34 . Our findings suggest a new model of tuberculosis pathogenesis in which type I IFNs impair responses to IFNγ to drive an initial loss of bacterial control, which in turn initiates a positive feedback loop of NET production and type I IFN expression by pDCs, leading to uncontrolled bacterial replication and active tuberculosis disease.

Macrophages and Neutrophils exhibit a variety of activation states during Mtb infection
To further characterize the Mtb-infected myeloid cells and dissect the cellular mechanism of type I IFN driven Mtb susceptibility, we compared B6 and Sp140 -/innate immune responses by performing scRNA-seq on myeloid cells from Mtb infected or uninfected lungs 25 days after infection. For this experiment, CD64 + and Ly6G + cells were magnetically enriched, sort purified, and processed for library generation with the 10X Genomics platform. To ensure proper cell clustering, mRNA transcripts and protein expression for select lineage markers were simultaneously measured by CITE-seq (Supplementary Fig. 3) 41 . Resulting datasets were analyzed with Seurat V4, and Weighted Nearest Neighbor (WNN) analysis was used to cluster cells based on mRNA and protein expression 42 . Analyzed datasets were then visualized by uniform manifold approximation and projection (UMAP) reduction on the WNN clustered data (wnnUMAP) 43 . The resulting dataset consists of 6,604 B6 and 13,668 Sp140 -/cells, almost exclusively consisting of myeloid cells ( Fig. 2A). Major cell types were annotated based on protein or mRNA expression of lineage defining markers, such as Siglec F protein expression for identifying AMs (Fig. 2B) 44 . Individual clusters within a major cell type, like the 10 clusters of neutrophils, were annotated based on expression of maturation and activation markers (Supplementary Fig. 4). Each cluster is represented in the B6 and Sp140 -/datasets, however the proportions of some clusters, including the ratio of IFN stimulated gene (ISG) + IM to ISG -IM, shifts between genotypes (Fig. 2C). The largest changes in composition are seen when comparing cells from naïve lungs to bystander and Mtb infected cells from Mtb infected lungs (Fig. 2D). For example, AMs are abundant in naïve lungs but are rare among the Mtb infected cells 25 days post-infection, as also seen by flow cytometry (Fig. 1F, 2D). To establish whether the differences in immune response between Sp140 -/and B6 mice occurred in response to Mtb infection, we compared naïve Sp140 -/and B6 lungs by scRNA-seq. Consistent with the normal cellular profile of naïve Sp140 -/mice ( Supplementary Fig. 2), fewer than 10 differentially expressed genes were identified between the two genotypes in AMs, IMs, monocytes, and neutrophils ( Supplementary Fig. 5). These results suggest that the immune compartment of B6 and Sp140 -/mice is highly similar at baseline, and the type I IFN-driven changes in the genotypes occur after Mtb infection.

Bystander IMs and pDCs are the primary sources of type I IFN during Mtb infection
. CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022. 10.06.511233 doi: bioRxiv preprint To determine the cellular mechanism of type I IFN-driven Mtb susceptibility, we first sought to identify which cells produce type I IFN following infection. In general, our scRNAseq analysis revealed that very few cells were Ifnb1 positive, which may reflect a lack of sensitivity of scRNAseq, and/or the transient and stochastic expression pattern of this gene (Fig. 3A) [45][46][47][48] . Mtb induced expression of Ifnb1 in infected and bystander mononuclear phagocytes, with a slight bias towards Ifnb1 production by IMs compared to monocytes, and no production by AMs (Fig. 3A). While there was no major difference in the cell types producing Ifnb1 between B6 and Sp140 -/cells, a greater number and frequency of Sp140 -/cells expressed Ifnb1 compared to B6 cells (Fig. 3B). Additionally, Ifnb1 expressing cells in Sp140 -/mice had higher per cell expression of Ifnb1 than B6 cells (Fig. 3C). Consistent with these findings, a prior scRNA-seq study of Mtb infected and naïve lungs from non-human primates largely mirrors our findings in mice 49 . IMs were also the dominant IFNB1-expressing cells in non-human primates with active tuberculosis, and IMs did not express IFNB1 in naïve or latently infected lungs (Fig. 3D). These results suggest that mice accurately model the Mtb induced type I IFN production seen in nonhuman primates.
The type I IFN producers identified in the scRNA-seq datasets were validated using a genetic reporter of type I IFN production, called I-Tomcat mice (from Dan Stetson, manuscript in preparation). These mice express TdTomato and Cre downstream of Ifnb1; therefore, any cell that expresses Ifnb1 will also express TdTomato and Cre (Fig. 3E). While TdTomato expression was sufficient to identify Ifnb1 expression by I-Tomcat bone marrow-derived macrophages following in vitro stimulation with poly I:C (Supplementary Fig. 6), TdTomato + cells were not detected 25 days after Mtb infection (Fig. 3F). Even though type I IFN drives the Mtb susceptibility of Sp140 -/mice 25 days post-infection, it is unclear when the type I IFN production occurs (Fig. 1A). It is possible that type I IFN is an early and/or transient event, which would be missed by analyzing a single timepoint with the I-Tomcat mice. To address this issue, we crossed I-Tomcat mice with the Ai6 Cre reporter mouse line to generate I-Tomcat Ai6 mice (Fig. 3E) 50 . In these mice, any cell that has ever expressed Ifnb1 will constitutively express ZsGreen. Mtb infected I-Tomcat Ai6 mice clearly contained populations of reporter-positive myeloid cells, while demonstrating low background among cell populations that are not expected to be reporter positive (e.g., ~0.1% of T cells were Ai6 + ) (Fig. 3G). Consistent with the scRNAseq analysis, IMs and monocytes were the primary Ifnb1 expressing cells in B6 and Sp140 -/mice (Fig. 3H). Interestingly, Sp140-deficient mice exhibited elevated Ai6 + expression frequency in all cell types, suggesting SP140 broadly modulates the sensitivity for inducing Ifnb1 expression (Fig. 3H). In addition to corroborating the scRNA-seq data, the I-Tomcat mice also identified pDCs as a major type I IFN producing cell population. Lung pDCs are very rare and were therefore not represented in our scRNA-seq dataset, demonstrating the power of using genetic reporters to study rare events and cell populations. Despite their scarcity, pDCs are known to be extremely robust producers of type I IFNs on a per cell basis 51 .
While we expected IMs to be a major type I IFN producing population given the scRNAseq results, we were surprised that the majority of the Ai6 + IMs were Mtband most Mtb + IMs were Ai6 - (Fig. 3G). These results suggest that direct infection of IMs is neither required nor sufficient for IFN-β production. To examine this phenomenon in greater detail, we performed confocal microscopy and histo-cytometry analysis of Mtb-infected I-Tomcat Ai6 and Sp140 -/-I-Tomcat Ai6 lungs 52,53 . While lesions of diseased tissue were clearly identifiable in I-Tomcat Ai6 mice, the size and myeloid cell influx into the diseased tissue were greatly exacerbated in Sp140 -/-I-Tomcat Ai6 (Fig. 4A). Additionally, Ai6 expressing cells were identifiable throughout the lungs, with an increased propensity to localize in diseased rather than healthy tissue (Fig.  4A, Supplementary Fig. 7). Within diseased tissue, Ai6 expressing cells were primarily located near Mtb harboring cells in I-Tomcat Ai6 and Sp140 -/-I-Tomcat Ai6 lungs (Fig. 4B). Similar to the flow cytometry results, SIRP + macrophages were a major Ai6 expressing cell population, with a higher frequency of SIRP + macrophages expressing Ai6 than CD4 + T cells in the . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint diseased tissue but not healthy tissue (Fig. 3H, 4B, 4C). Direct infection by Mtb was not a major driver of IFN-β expression, as ~2-3% of infected macrophages were Ai6 + and ~12-15% of Ai6 + cells were Mtb infected, in line with the frequencies seen in IMs by flow cytometry (Fig. 3H, 4D). These results suggest that IM localization to Mtb rich regions provides the activating signals required for IFN-β expression, while direct infection of IMs is not required for IFN-β expression.
pDCs significantly contribute to the Mtb susceptibility of Sp140 -/animals While pDCs have a well-established role in anti-viral immunity in the lung, limited work has assessed their contribution during Mtb infection 54,55 . pDCs may have been previously overlooked because of their scarcity in the lung. Despite this scarcity, pDCs can have major effects due to the extremely high levels of interferons produced per cell 51 . Consistent with our finding that pDCs are a major type I IFN producer in Mtb infected mouse lungs, Khader and colleagues described the presence of pDCs in lungs of non-human primates with active pulmonary TB 49 . However, the lack of genetic tools in non-human primates precluded functional studies of pDCs during TB. Therefore, we decided to take advantage of our experimentally tractable mouse model to assess whether pDCs affect Mtb control. The contribution of pDCs was initially tested by depleting pDCs using an anti-PDCA-1 antibody 56-58 . This strategy efficiently depleted pDCs and resulted in a partial rescue of Mtb control in Sp140 -/mice ( Fig.  5A, 5B). However, PDCA-1 is known to be upregulated by cells other than pDCs in inflammatory environments, thus antibody depletion could have been protective against Mtb by depleting non-pDC cells 57 . We therefore also tested the contribution of pDCs by using a genetic pDC depletion strategy in which we generated Sp140 -/mice with diphtheria toxin receptor (DTR) expressed downstream of the human BDCA2 promoter (pDC-DTR) 59 . As a single copy of Sp140 is sufficient to restore wild-type control of Mtb, the Sp140 +/littermate controls are representative of wild-type, Mtb restrictive animals. DT administration efficiently ablated pDCs in Sp140 -/and Sp140 +/mice, with the depletion specifically affecting pDCs (Fig. 5C,  Supplementary Fig. 8). Genetic pDC depletion was able to fully rescue bacterial control in Sp140 -/mice, while depletion in Sp140-sufficient animals (that do not exhibit an exacerbated type I IFN response) did not affect lung bacterial burden, as expected (Fig. 5D). These results demonstrate a novel contribution by pDCs in limiting Mtb control in animals with a hyper type I IFN response.
To understand why pDCs contribute to the susceptibility of Sp140 -/but not Sp140sufficient animals, we examined ligand availability for the pDCs. We focused on DNA-rich NETs as a potential pDC-activating ligand because extracellular DNA is a potent pDC-activating ligand, and NETs have been described as a stimulus for type I interferon production by pDCs in mice and humans in the context of autoimmunity 33,34,60 . Additionally, another Mtb susceptible mouse model with a hyper type I interferon response identified the presence of NETs in the lungs of susceptible mice and humans with active Mtb disease 61 . We assessed NET production in Sp140 -/and B6 mice by staining for citrullinated H3 in the lungs of Mtb infected mice (Fig.  5E). Sp140 -/mice had over a 100-fold increase in NET staining as compared to B6 animals, indicating that Sp140 -/mice have substantially more ligand to activate type I interferon production by pDCs as compared to wild-type hosts (Fig. 5F).
While our results demonstrate a role for pDCs during Mtb infection in mice and the Khader lab identified a correlation between pDCs and active Mtb in non-human primates, the role of pDCs during human Mtb infection has yet to be examined 49 . Therefore, we analyzed human lung and lymph node biopsies taken from Mtb culture-positive patients for the presence of pDCs near Mtb granulomas (Fig. 5G). Based on CD303 and CD123 staining, pDCs localized to the lymphocytic cuff surrounding Mtb granulomas in human lungs and lymph nodes (Fig. 5H,  5I, Supplementary Fig. 8). Of the 8 patient samples analyzed, 5 lung samples and 7 lymph node samples had pDCs in the same 400× field as an Mtb granuloma (Supplementary Table   . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint 1). The majority of the pDCs in the lung samples were distributed as individual cells, while lymph node pDCs were primarily grouped together in clusters of over 20 cells or scattered individually (Supplementary Table 1). These results demonstrate that pDCs, though generally an extremely rare cell population, are nevertheless located near Mtb-infected cells in granulomas in human lung and lymph nodes. These results, along with our results in mice and previous studies in non-human primates 49 , implicate pDCs as a plausible source of type I IFN that drives active tuberculosis in humans.

Neutrophils and IMs are the major sensors of type I IFNs during Mtb infection
Having identified pDCs, IMs, and monocytes as the major type I IFN producers during Mtb infection, we next sought to identify which cells responded to this type I IFN. As expected, IFNAR was uniformly expressed by all lung myeloid cells, and therefore not informative for identifying IFN responsive cells (Fig. 6A) 62 . However, comparing differentially expressed genes in B6 and Sp140 -/neutrophils and IMs showed a clear induction of IFN stimulated genes in cells from Sp140 -/animals (Fig. 6B). A major complication, however, is that many genes induced by type I IFN are also induced by IFNγ, and most existing studies do not distinguish the two. Therefore, we sought to develop type I IFN-specific and IFNγ-specific transcriptional signatures. We analyzed RNA-sequencing on human macrophages stimulated with IFNγ, IFN-β, tumor necrosis factor, transforming growth factor-β, or nothing ( Supplementary Fig. 9) 63 . After converting the gene names to mouse gene symbols, the signatures were applied to the mouse lung myeloid scRNA-seq dataset. Strength of signature expression in naïve mice was used to define the threshold for classifying cells as an IFNγ or type I IFN responder ( Supplementary  Fig. 9). As expected, naïve mice had very few cells responding to either cytokine, while bystander and Mtb infected cells responded strongly to type I and 2 IFNs (Fig. 6C). Interestingly, the type I IFN response was limited to IMs and neutrophils, even though monocytes and AMs were responsive to IFNγ. Potentially, differences in the localization of these cells could explain their differences in cytokine responsiveness. As expected, neutrophils and IMs from Sp140 -/mice exhibited a significant increase in type I IFN signaling relative to cells from B6 lungs (Fig. 6D, 6E). Consistent with considerable prior work demonstrating that type I IFNs impair responsiveness to IFNγ 16,31,32,64 , the Sp140 -/mice harbored a distinct population of IMs that exhibited the signature of type I IFN-responsiveness but lacked the signature of IFNγ responsiveness (note the distinct population of blue IMs in Fig. 6D, 6E). Since IFNγ is critical for control of intracellular Mtb replication, these results suggest that type I IFN inhibits Mtb control at least in part by opening a niche of susceptible IMs that fail to respond to IFNγ. As neutrophils and IMs were the primary sensors of type I IFN, these are the cell types we tested to determine which cell population mediates the type I IFN-driven susceptibility of Sp140 -/mice. Using LysM Cre Ifnar1 fl/fl mice, we deleted IFNAR expression on myeloid cells in Sp140 -/mice. Myeloid cell-specific deficiency in IFNAR was sufficient to rescue bacterial control and reduce lung neutrophil numbers to the same extent as global IFNAR deletion (Fig.  7A, 7B). As LysM cre is activate in neutrophils, monocytes, and macrophages, this strategy could not dissociate the contribution of type I IFN signaling in neutrophils from that in mononuclear phagocytes. Therefore, we isolated the contribution of neutrophil sensing of type I IFN by deleting IFNAR solely on neutrophils by using Mrp8 Cre Ifnar1 fl/fl mice. Neutrophil-specific deficiency in type I IFN signaling was insufficient to rescue the Mtb susceptibility or the increase in lung neutrophils exhibited by Sp140 -/mice (Fig. 7C, 7D). Taken together, these results are consistent with a model in which type I IFNs act on IMs to inhibit IFNγ signaling in these cells, thereby reducing their ability to restrict Mtb growth.

Discussion
. CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The dominant gene signature identified in humans with active tuberculosis disease is a type I IFN signature. Type I IFNs are a critical for effective anti-viral immunity 8 but considerable data from humans and animal models indicate type I IFNs impair Mtb control 7,9,10,65 . Interestingly, not all IFNs promote Mtb disease, as IFNγ (also called type II IFN) is protective during tuberculosis 5 . Indeed, type I IFNs can exacerbate bacterial infections by directly antagonizing IFNγ signaling. The mechanism of antagonism is poorly understood but seems to be due at least in part to the downregulation of surface expression of the IFNγ receptor 31,32 . The antagonism of IFNγ signaling by type I IFNs is conserved in humans, and has been shown to be relevant during Mycobacterium leprae infections 16 . Potentially, antagonism of IFNγ signaling underlies the tuberculosis-susceptibility of humans that exhibit a type I IFN signature, as humans with active Mtb exhibit reduced IFNγ receptor expression on their monocytes 15 . Additionally, type I IFNs can antagonize IL-1 signaling, an additional pathway that is also critical for Mtb control, through the induction of IL-1 receptor antagonist and eicosanoid imbalance 22,29,66,67 . Thus, the type I interferon response may be a key driver of progression to active tuberculosis. There is support in the literature for this hypothesis, as infants in their first year of life that acquire cytomegalovirus infection, a virus known to induce type I IFN, have a high risk of developing tuberculosis disease 12,13,[68][69][70] . Additionally, influenza infection, which also induces type I IFN, correlates with an increased risk of death in pulmonary tuberculosis patients 14 . While the correlation between viral infection and exacerbated tuberculosis disease is tantalizing, a major limitation of studying human disease is the difficulty in establishing the underlying causal mechanisms that account for the correlation. Thus, we turned to a mouse model of Mtb infection to establish the cellular mechanisms by which type I IFN drives Mtb susceptibility.
The commonly used B6 mouse model does not exhibit a strong type I IFN response after Mtb infection. Consistent with the modest type I IFN response of B6 mice, IFNAR deletion on the B6 genetic background does not impact survival or bacterial burdens in the lungs after Mtb infection [23][24][25]29 . Therefore, we sought a different mouse model that recapitulated two key aspects of human disease, the hyper type I IFN response and the accompanying neutrophilic inflammation 7,71,72 . Previously, we identified B6.Sst1 S mice as a mouse model that exhibits type I IFN-driven susceptibility to Mtb infection 29 . We then defined Sp140 as the gene responsible for the susceptibility of these mice 30 . As was the case for B6.Sst1 S animals, IFNAR deletion in Sp140 -/mice rescues susceptibility. Additionally, Sp140 -/mice have more lung neutrophils after Mtb infection than Sp140 -/-Ifnar1 -/animals ( Fig. 1D, 7B, 7D). Therefore, Sp140 -/mice recapitulate the fundamental type I IFN and neutrophilic character of human active Mtb disease, and can serve as a platform for understanding the cellular mechanism of type I IFN-driven Mtb susceptibility.
Sp140 -/mice provide a unique model of the aberrant type I IFN response, as they do not require repeated administration of TLR agonists, viral co-infection, or other perturbations of the innate immune system for type I IFN production 17,18,21,22,61 . Other groups have also modeled the type I IFN response by infecting B6 mice with a lineage 2 clinical Mtb strain, such as HN878 73,74 . However, IFNAR deletion had no impact on survival or bacterial control at early time points in B6 mice infected with HN878, unlike Sp140 -/mice infected with Mtb Erdman 30,75,76 . Thus, we believe the Sp140 -/mouse model uniquely recapitulates the hyper type I IFN response exhibited by humans, and provides a tool to study the mechanistic basis of the aberrant type I IFN response.
We sought to use the Sp140 -/mice to address two key questions: (1) what cells produce type I IFN-producing during Mtb infection? and (2) which cells respond to the type I IFN to mediate Mtb disease? Flow cytometry and imaging of I-Tomcat Ai6 Ifnb1 reporter mice identified IMs and pDCs as the major IFN-β producers during Mtb infection. Imaging provided insight into why these cells expressed type I IFN, as the frequency of type I IFN-expressing . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint macrophages was enriched relative to CD4 + T cells in diseased tissue but not in healthy tissue. This result suggests that proximity to Mtb dictates access to activating signals required to induce IFN-β expression by macrophages. However, most IFN-β expressing IMs were not infected with Mtb, and most infected IMs were not Ifnb1 or reporter positive, indicating direct infection is insufficient and may not be the main driver of type I IFN expression in vivo. Imaging demonstrated that B6 and Sp140 -/macrophages expressed IFN-β at similar frequencies in diseased tissue. This result appears to contrast with flow cytometry analysis of whole lungs, which demonstrates a higher frequency of IMs expressing IFN-β in Sp140 -/mice relative to B6 mice. The apparent discrepancy between the flow cytometry and imaging data is likely explained by the fact that Sp140 -/mice have considerably more diseased tissue than B6 mice. Additionally, the Ai6 signal from I-Tomcat Ai6 mice identifies cells that have expressed IFN-β, but does not indicate the level of IFN-β expression in these cells. Thus, even if macrophages are driven to express IFN-β at similar frequencies in B6 and Sp140 -/mice, the macrophages in Sp140 -/mice could produce more IFN-β per cell, as seen in the scRNA-seq data (Fig. 3C).
Expression of type I IFNs by pDCs during Mtb infection was particularly noteworthy as limited work exists on the effect of pDCs on Mtb control. The role of pDCs in viral control is well established, with considerable evidence that pDC production of type I IFN during the early phases of viral infections significantly contributes to viral control 54,55,59 . In bacteria, the potential for pDCs to impact control has become appreciated more recently, with pDCs demonstrating a protective function against Citrobacter rodentium, Chlamydia pneumoniae, and Klebsiella pneumoniae [77][78][79][80] . However, few studies have examined the contribution of pDCs during Mtb infection, and these studies have not yet led to a clear understanding. The number of pDCs in the blood was reduced in Mtb infected humans, but lung pDC numbers or function were not assessed 81,82 . In non-human primates, there was a correlation between active pulmonary Mtb and the influx of pDCs and IFN-responsive macrophages into the lungs of the rhesus macaques 49 . Another group also identified pDCs in tuberculosis granulomas of a different nonhuman primate, cynomolgus macaques, but the frequency of pDCs among all cells in the granulomas did not correlate with bacterial burden of the granuloma 83 . A major issue in the studies using NHPs or humans is the difficulty in depleting or otherwise functionally assessing the role of pDCs. We therefore generated Sp140 -/-pDC-DTR mice to study the contribution of pDCs to bacterial control. These mice permit specific depletion of pDCs during Mtb infection and demonstrated that pDCs contribute significantly to the susceptibility of Sp140 -/mice. Additionally, we identified pDCs in the lymphocytic cuff surrounding Mtb granulomas in human lungs and lymph nodes. Together, these results suggest that disease progression driven by pDC production of type I IFN drives disease in mice, and is likely conserved across non-human primates and humans.
While pDC depletion rescued Sp140 -/mice, it had no impact on the bacterial burdens in B6 animals. This result was not surprising given that very few myeloid cells in B6 mice expressed a type I IFN signature and Ifnar1 deficiency also has only modest effects in the B6 background 25,29 . As pDCs are present in B6 and Sp140 -/mice, we speculated that the difference in pDC type I IFN production in these mouse strains could be due in part to differences in the availability of activating ligands. As seen in another Mtb susceptible mouse model, and in Mtb infected human lungs 61 , Sp140 -/mice had a significant enrichment in NET production compared to B6 mice. Potentially, these NETs, which are DNA-rich products of neutrophils, act as ligands for TLR9 on the pDCs, as described in mouse and human autoimmunity 33,34,60,84 . Given that NET formation was also identified in Mtb granulomas in human lung sections, it is possible that pDC sensing of NETs contributes to the type I IFN response detected in humans with active Mtb disease 61 .
Having defined the cells producing type I IFNs in vivo after Mtb infection, we then sought to identify which cells responded to the type I IFN. To do this, we had to first develop transcriptional signatures that distinguish the response to type I IFN from the closely related . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint response to IFNγ. Applying these signatures to our scRNA-seq data, we identified neutrophils and IMs as type I IFN sensors. While both cell populations harbor Mtb, it is possible that type I IFN driven susceptibility to Mtb is caused by signaling in only one of these populations. In a GM-CSF blockade model of type I IFN driven Mtb susceptibility, neutrophil-specific deletion of IFNAR rescued bacterial control 61 . We employed the same genetic strategy as this prior report, but were unable to detect any rescue of Sp140 -/mice when neutrophils lacked IFNAR. This was surprising as we saw that Sp140 -/neutrophils expressed a type I IFN gene signature by scRNA-seq. However, GM-CSF is critical for maintaining lung alveolar macrophages and the responsiveness of lung monocytes and macrophages to infections, including Mtb infection [85][86][87][88] . Therefore, it is possible that impairing lung macrophages by GM-CSF blockade shifted the impact of type I IFN on Mtb control from macrophages to neutrophils. In support of this idea, we also identified IMs as a type I IFN-sensing population, and deletion of IFNAR on macrophages rescued Sp140 -/mouse bacterial control. These results suggest that during a normal (GM-CSF sufficient) response, type I IFN signaling in macrophages reduces their ability to restrict Mtb in a cell-intrinsic manner, potentially by inhibiting IFNγ signaling in the macrophages 15,31,32 .
Overall, our results suggest a model in which type I IFNs impair responsiveness to IFNγ, leading to an initial loss of bacterial control. Bacterial replication then leads to neutrophil influx and NET production within the diseased tissue. We propose that DNA-rich NETs are sensed by pDCs, likely via TLR9, causing them to produce type I IFN, which further antagonizes IFNγ signaling and reduces the IMs ability to restrict Mtb growth. Given the correlations between our results and findings in rhesus macaques and humans with active Mtb, we believe that our proposed mechanism of type I IFN driven loss of Mtb control is conserved across species. These findings open the door for the development of therapies targeting NET production or pDC function as host-directed strategies for treating Mtb infection.
R.E.V. consults for Ventus Therapeutics and Tempest Therapeutics.  A linear regression performed on log transformed data was used to calculate significance and R 2 for (C). An unpaired t test was used to determine significance for (A), a two-way ANOVA with Sidak's multiple comparisons test was used to calculate significance for (D), (E), and (F). **p < 0.01, ***p < 0.001, ****p < 0.0001.     (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made AMs from bystander and Mtb-infected B6 (n = 3) and Sp140 -/-(n = 3) lungs that are responsive to IFNγ (red), type I IFN (blue), both (purple), or neither (white). Lungs were analyzed 25 days after Mtb-Wasabi infection. Statistical significance in (B) was calculated with the Wilcoxon Rank-Sum test and by two-way ANOVA with Tukey's multiple comparisons test in (E). *p < 0.05, ***p < 0.001, ****p < 0.0001. Pooled data from twothree independent experiments are shown. Statistical significance was calculated by one-way ANOVA with Tukey's multiple comparison test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Mtb infections
Mtb strain Erdman was a gift of S. A. Stanley. Frozen aliquoted stocks were produced after passing the strain in vivo to ensure virulence. Mtb expressing Wasabi (Mtb-Wasabi) and Mtb-mCherry were generated using Mtb that had been passaged 2 or fewer times in vitro. For these fluorescent strains, Mtb was grown in Middlebrook 7H9 liquid medium supplemented with 10% albumin-dextrose-saline, 0.4% glycerol, and 0.05% Tween-80 for 5 days at 37°C. The cells were pelleted and washed in 10% glycerol to remove salt. The bacteria were then electroporated with . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint 1 µg DNA using a 2 mm electroporation cuvette and the following settings: 2500 volts, 1000 Ohms, 25 µF. The pTEC15 plasmid (a gift from Lalita Ramakrishnan; Addgene plasmid # 30174), which expresses Wasabi under the control of the Mycobacterium Strong Promoter, was electroporated into Mtb to generate Mtb-Wasabi 35 . The pMSP12::mCherry plasmid (a gift from Lalita Ramakrishnan; Addgene plasmid # 30167), which expresses mCherry under the control of the Mycobacterium Strong Promoter, was electroporated into Mtb to generate Mtb-mCherry. Following electroporation, bacteria were grown on 7H11 plates supplemented with 10% oleic acid, albumin, dextrose, and catalase, 0.5% glycerol, and either 200 µg / mL Hygromycin for Mtb-Wasabi or 50 µg / mL Kanamycin for Mtb-mCherry for 3-4 weeks at 37°C. Individual colonies where then propagated in 10 mL inkwell flask cultures using 7H9 medium supplemented with 10% albumin-dextrose-saline, 0.4% glycerol, 0.05% Tween-80, and either Hygromycin for Mtb-Wasabi or Kanamycin for Mtb-mCherry for 7 days at 37°C. The inkwell cultures were expanded into a 100 mL culture using the same 7H9 supplemented media with antibiotics and cultured for 4-5 days at 37°C. Once the bacteria were in log phase, the culture was filtered with a 5 µm syringe filter and frozen in 1 mL aliquots in 10% glycerol. For infection, a frozen aliquot was diluted in distilled H 2 0, and 9 mL of diluted culture was loaded into the nebulizer of a inhalation exposure system (Glas-Col, Terre Haute, IN) to deliver ~20-100 bacteria per mouse as determined by measuring CFU in lungs 1 day post-infection.

Tissue Processing for CFU and Flow cytometry
Mice were harvested at various days post-infection (as described in figure legends) to measure CFUs by plating and innate immune populations by flow cytometry. All lung lobes were harvested into a gentleMACS C tube (Miltenyi Biotec) containing 3 mL of RPMI media with 70 µg / mL of Liberase TM (Roche) and 30 µg / mL of Dnase I (Roche). Samples were processed into chunks using the lung_01 setting on the gentleMACS (Miltenyi Biotec) and incubated for 30 minutes at 37°C. Tissue was then homogenized into a single cell suspension by running the samples on the lung_02 setting on the gentleMACS. The digestion was quenched by adding 2 mL of PBS with 20% Newborn Calf Serum (Thermo Fisher Scientific) and filtered through 70 µm SmartStrainers (Miltenyi Biotec).
For measuring plasmacytoid dendritic cell numbers, spleens were harvested into a 12 well plate with 1 mL of PBS with 2% Newborn Calf Serum and 0.05% sodium azide in each well. The spleens were sandwiched between 100 uM mesh filters and mashed into a single cell suspension with the back of a syringe plunger. The single cell suspensions were filtered through 70 µm SmartStrainers (Miltenyi Biotec).

Measuring Bacterial Burden
To measure CFU, 50 µL was taken from each single cell suspension and then serially diluted in phosphate-buffered saline (PBS) with 0.05% Tween-80. Serial dilutions were plated on 7H11 plates supplemented with 10% oleic acid, albumin, dextrose, and catalase and 0.5% glycerol. Colonies were counted after 3 weeks.

Flow Cytometry
For flow cytometry, lung single cell suspensions were pelleted and resuspended in 500 µL of PBS with 2% Newborn Calf Serum and 0.05% Sodium azide and 100-150 µL were stained with antibodies for analysis. Spleen single cell suspensions were pelleted and resuspended in 5 mL of PBS with 2% Newborn Calf Serum and 0.05% Sodium azide, of which 50 µL were stained with antibodies. Single cell suspensions were stained for 45 minutes to an hour at room temperature with the following antibodies: TruStain FcX PLUS (S17011E, BioLegend), BUV496-. CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022.

Immunostaining human lymph nodes and lungs
Human lung and lymph node samples were acquired from the surgical pathology archives of Emory University Hospital with appropriate institutional approval. 8 lung samples and 8 lymph node samples were analyzed. Each sample had been previously culture verified for Mycobacterium tuberculosis infection. The samples were formalin-fixed and paraffin-embedded. Sections were cut and stained with anti-CD123 (6h6, Thermo Fisher Scientific), anti-CD303 (124B3.13, Dendritics), or hematoxylin and eosin 94,95 . Primary antibodies were detected by immunoperoxidase staining with the LSAB+ System and a standard DAB reaction following manufacturer's instructions (DakoCytomation). Sections were counterstained with hematoxlyin prior to mounting and microscopy. pDCs were assessed in multiple 400× fields for each section to calculate the frequency of samples containing pDCs and the clustering of pDCs within each sample, defined as either singe cells, loose clusters of 5-20 cells, or tight clusters of more than 20 cells.

Image processing and histo-cytometry analysis
Image analysis was performed using Chrysalis software 53 . Briefly, a compensation matrix was generated by automatic image-based spectral measurements on single color-stained controls in ImageJ by using Generate Compensation Matrix script. This compensation matrix was used to perform linear unmixing on three-dimensional images with Chrysalis. Chrysalis was also used for further image processing, including rescaling data and generating new channels by performing mathematical operations using existing channels. For histo-cytometry analysis, Imaris 9.9.1 (Bitplane) was used for surface creation to digitally identify cells in images based on protein expression 52 . Statistics for the identified cells were exported from Imaris and then imported into FlowJo version 10 (BD Biosciences) for quantitative image analysis. (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

Sorting
The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint BioLegend), PE-labeled F4/80 (BM8, Thermo Fisher Scientific), and BV786-labeled CD45.2 (104, BioLegend) anti-mouse antibodies. All post-enrichment antibody staining was performed on ice for 45 minutes in the presence of True-Stain Monocyte Blocker (BioLegend). Following staining, cells were resuspended in PBS with 2% Newborn Calf Serum and Sytox Blue Dead Cell Stain (Thermo Fisher Scientific). Cells were sort purified using a 100 µm microfluidic sorting chip in a 4 laser SH-800 cell sorter (Sony) on the purity setting. The isolated populations from infected lungs were Mtb-infected cells and bystander myeloid cells. Macrophages and a mixture of neutrophils and monocytes were isolated from the naïve lungs and the macrophages were combined with neutrophil/monocyte mixture at a 1:2 ratio for better macrophage representation in the resulting dataset.

Single cell RNA: Library generation and sequencing.
The scRNA-sequencing libraries were generated using the v3.1 chemistry Chromium Single Cell 3' Reagent Kit (10X Genomics) largely following the manufacturer protocol with the following minor modifications. Cells were loaded into 3 different lanes on a Chromium Next GEM Chip. Lane 1 was loaded with Mtb-infected cells from all 3 B6 and 3 Sp140 -/lungs. Lane 2 was loaded with bystander myeloid cells from the 3 infected B6 lungs as well as the myeloid cell mixture from the 2 naïve B6 lungs. Lane 3 was loaded with bystander myeloid cells from the 3 infected Sp140 -/lungs as well as the myeloid cell mixture from the 2 naïve Sp140 -/lungs. All 3 lanes of the Chromium Next GEM Chip were super-loaded with 29000 cells with a target of 14800 single cells per lane, as hashtag barcoding allows for a lower effective multiplet rate due to the ability to identify most of the multiplets (https://satijalab.org/costpercell/) 96 . 0.5 U/µL RNaseOUT Recombinant Ribonuclease Inhibitor (Invitrogen) was added to single cell RT master mix during the loading step and 1 µL of ADT and HTO additive primers (0.2 µM stock) were added during the cDNA amplification, as recommended by the CITE-seq and Cell Hashing Protocol (https://cite-seq.com/protocols/) 41 . Following cDNA, ADT, and HTO purification, samples were decontaminated by 2 rounds of centrifugation through 0.2 µM filter microcentrifuge tubes and then removed from the BSL3. Library preparations were completed outside of the BSL3 following the 10X Genomics protocol for the cDNA and the CITE-seq and Cell Hashing Protocol for the ADT and HTO libraries. Quality control of the libraries was performed with a Fragment Analyzer (Agilent). The mRNA, ADT, and HTO libraries were pooled at the following proportions: 85% mRNA, 9% ADT, and 6% HTO. Libraries were sequenced on a NovaSeq 6000 (Illumina) using two lanes of a S1 flow cell and the following cycles read 1 (28 cycles), i7 index (10 cycles), i5 index (10 cycles), read 2 (90 cycles).

ScRNA-seq: data processing.
Raw sequencing reads for the mRNA libraries were processed into raw count matrices with CellRanger version 4.0.0 (10X Genomics). The ADT and HTO libraries were processed into raw count matrices with CITE-Seq-Count version 1.4.3 (https://hoohm.github.io/CITE-seq-Count/) 97 . The raw counts for mRNA, ADT, and HTO were analyzed using Seurat v4.1.1 using default settings for normalizing the data, finding variable features, and scaling the data 42 . HTO demultiplexing was performed with the HTODemux function. Data was filtered to only include single cells with between 200 and 4500 genes and less than 5% mitochondrial reads. The resulting datasets were integrated together using 30 dimensions for the FindIntegrationAnchors function and 30 dimensions for the IntegrateData function. The data was then scaled and analyzed by PCA with 30 principal components followed by UMAP analysis with 30 dimensions. Clustering was performed by using 30 dimensions with the FindNeighbors function and a resolution of 0.8 for the FindClusters function.
. CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made To improve resolution for clustering innate immune cells, weighted nearest neighbor analysis was used to combine the protein data (ADTs) and the mRNA data when clustering cells. For this analysis, variable ADT features were identified and then normalized using centered log ratio transformation and a margin of 2. The normalized ADT data was then scaled and analyzed by PCA. The ADT and mRNA data was then combined with the FindMultiModalNeighbors function using 30 dimensions for the mRNA and 10 for the protein. The resulting dataset was analyzed by UMAP and clusters were identified with the FindClusters function using algorithm 3 and a resolution of 1.5 43 .

Type I IFN and IFNγ Gene Signature Analysis
For generating the type I IFN and IFNγ gene signatures, we utilized a published RNA-seq dataset (GEO: GSE20251) of primary human macrophages that were unstimulated or stimulated with 10 ng / mL of TNF, IFNγ, IFN-β, or transforming growth factor-β for 24 hours and then processed for RNA-sequencing 63 . Gene expression was compared across all stimulation conditions and filtered to only include genes that were differentially expressed by an adjusted pvalue of less than 0.001. The list was also filtered to only include genes with a normalized average expression of greater than 2. A gene specificity index was calculated based on the ratio of expression of a gene within a stimulation group over the average expression across all groups. The list was further filtered to only include genes with a gene specificity index greater than 1.5. Gene names were then converted to mouse symbols and any genes that did not have a mouse homolog where removed. The gene signature for IFNγ and type I IFN were then generated by finding all genes uniquely induced by IFNγ or type I IFN, respectively, from the filtered gene list. The UCell R package was used to score cells in the scRNA-seq dataset for their expression of either gene signature 98 .

Statistical analysis
Statistical significance was determined using Prism (GraphPad) software for unpaired two-tailed Student t test when comparing two populations, one-way or two-way ANOVA tests with Tukey's or Sidak's multiple comparisons test when comparing multiple groups. Prism (GraphPad) was also used to calculate linear correlations and R 2 .

Data and code availability
Raw and processed single cell RNA-sequencing data is deposited at GEO: XXXXXX. Code for scRNA-sequencing analysis is available on Github: https://github.com/dmitrikotov/Sp140-Type-I-Inteferon.

Supplementary Tables
Supplementary Table 1 pDC Distribution Tissue Cases Cases with CD303 + cells (% total)

Single cells (% total)
Supplementary Table 1. Quantification of the number of human lung and lymph nodes with pDCs in the same field of view as Mtb granulomas, as well as enumeration of the pDC distribution.
. CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. performed on log transformed data was used to calculate significance and R 2 for (C). An unpaired t test was used to determine significance 0.001, ****p < 0.0001. Gated on live CD45 + B220 -CD90.2cells . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made   . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022.   . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made   . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 18, 2022. ; https://doi.org/10.1101/2022.10.06.511233 doi: bioRxiv preprint