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One trajectory arose directly from the F2: universal lineage.
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Single_Cell
A second trajectory originated from F1: superficial fibroblasts, transitioning to F7: myofibroblasts via an intermediate F6: inflammatory myofibroblast state (Fig. 5a–c and Extended Data Fig. 7b ).
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These two inferred trajectories are consistent with in vivo lineage tracing studies in mice .
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To investigate these predicted trajectories in real time, we leveraged a human skin wound dataset of 58,823 cells ( Methods ) .
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Skin tissue had been collected from healthy human volunteers at baseline (pre-wound) and subsequently from healing wounds.
[ { "end": 11, "label": "Tissue", "start": 0, "text": "Skin tissue" } ]
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At baseline, myofibroblasts were not present, but on day 1 post-wounding, a small number of F6: inflammatory myofibroblasts were observed (Fig. 5d and Extended Data Fig. 7c ).
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By day 7, F6: inflammatory myofibroblasts were the predominant population.
[ { "end": 41, "label": "CellType", "start": 27, "text": "myofibroblasts" } ]
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By day 30, F7: myofibroblasts had become the predominant population, consistent with a role in established fibrosis/scarring.
[ { "end": 29, "label": "CellType", "start": 15, "text": "myofibroblasts" } ]
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Overall, our results point toward F6: inflammatory myofibroblasts as an intermediate differentiation state toward F7: myofibroblasts in human skin, with potential plasticity in fibroblast origin (Fig. 5d,e and Extended Data Fig. 7e ).
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We next investigated if the skin fibroblast subtypes we identified were conserved across other human tissues.
[ { "end": 43, "label": "CellType", "start": 28, "text": "skin fibroblast" } ]
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Previous studies have reported fibroblast states that are found across human tissues .
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However, because these studies each defined fibroblast subtypes with different nomenclature and gene markers, it is unclear how these populations relate to each other and to the skin fibroblast populations reported in our study.
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To answer this and perform an overarching analysis across tissues and diseases, we undertook two approaches.
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The first was to assess the expression of marker genes for cross-tissue fibroblast subtypes in our skin data .
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This identified that reported cross-tissue populations from previous studies are likely present in human skin, consistent with F2: universal, F3: FRC-like, F6: inflammatory myofibroblast, and F7: myofibroblast subtypes (Fig. 6a and Extended Data Fig. 8a ).
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The second approach was to integrate published datasets of ~5.8 million cells from human skin, lung, intestine, synovium, endometrium, heart and nasal mucosa ( Methods and Fig. 6b ).
[ { "end": 93, "label": "Tissue", "start": 83, "text": "human skin" }, { "end": 99, "label": "Tissue", "start": 95, "text": "lung" }, { "end": 110, "label": "Tissue", "start": 101, "text": "intestine" }, { "end": 120, "label": "Tissue", "start": ...
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This approach uses the whole transcriptome profile, instead of restricted marker genes, and thus more comprehensively defines cell state similarity.
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Approximately 1 million fibroblasts were selected for downstream analysis based on expression of canonical marker genes (Fig. 6b ).
[ { "end": 35, "label": "CellType", "start": 24, "text": "fibroblasts" } ]
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In the cross-tissue integrated dataset, we were able to discern shared fibroblast states across tissues, as well as fibroblasts that were unique to certain tissues (Fig. 6b,c and Extended Data Fig. 8b ).
[ { "end": 127, "label": "Tissue", "start": 116, "text": "fibroblasts" } ]
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In addition to known cross-tissue populations identified above (Fig. 6a ), we found evidence for F2/3: perivascular ( CXCL12 , APOC1 and PPARG ) and F5 : NGFR (Schwann-like; SCN7A , NGFR , ITGA6 and EBF2 ) fibroblasts across human tissues, including in lung, gut and nasal mucosa (Fig. 6c,d and Extended Data Fig. 8b,c )...
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Further interrogation of labeled intestine and lung datasets supported these results (Supplementary Note 3 ).
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F1: superficial showed variable gene expression by tissue, which may reflect distinct epithelia patterning across sites (Extended Data Fig. 8d ).
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Overall, our results point toward the presence of previously reported cross-tissue fibroblast states in skin, despite major differences in the biophysical properties of different human tissues.
[ { "end": 108, "label": "Tissue", "start": 104, "text": "skin" } ]
Single_Cell
We additionally suggest F2/3: perivascular and F5: NGFR (nerve-associated) fibroblasts as novel cross-tissue populations.
[ { "end": 42, "label": "CellType", "start": 30, "text": "perivascular" }, { "end": 86, "label": "CellType", "start": 75, "text": "fibroblasts" } ]
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Fibroblast-mediated processes such as fibrosis and maintenance of immune cell niches are observed across multiple human tissues.
[ { "end": 84, "label": "Tissue", "start": 66, "text": "immune cell niches" } ]
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We therefore asked whether disease-associated fibroblast states identified in skin were similarly enriched by disease category in non-skin tissues ( Methods ).
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We focused on F3: FRC-like and F6: inflammatory myofibroblasts based on their conserved states across tissues and potential immune-interacting roles from pathway enrichment analysis (Extended Data Figs. 2a and 5c ).
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Then, to predict functional interactions with immune cells, we utilized our skin data to perform cell–cell communication analysis.
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Across tissues, we observed that F3: FRC-like fibroblasts were present in both inflammatory disorders and fibrotic processes with immune-mediated pathology, including lung (COVID-19 and interstitial lung disease) and intestine (inflammatory bowel disease (IBD)) (Fig. 7a,b ) .
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While FRC-like fibroblasts were not reported in the Human Lung Cell Atlas (HLCA), following re-clustering we identified an F3: FRC-like population in lung (Extended Data Fig. 8e,f ), consistent with a previous report .
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We also confirmed equivalence of F3: FRC-like fibroblasts to T reticular cells (an FRC subset) in IBD (Supplementary Note 3 and Extended Data Fig. 8g ).
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Receptor–ligand analysis suggested interactions of skin F3: FRC-like fibroblasts with migrating dendritic cells (MigDCs) ( CCL19 - CCR7 ) and T cell subsets ( CXCL12 - CXCR4 ) (Extended Data Fig. 9a ), suggesting that F3: FRC-like fibroblasts maintain T lymphoid populations and facilitate T cell–dendritic cell interact...
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We corroborated these findings using NicheCompass for niche identification in inflamed atopic dermatitis skin , which revealed CCR7 MigDCs and CXCR4 T cells within the F3 superficial perivascular niche (Fig. 7c and Extended Data Fig. 9b ).
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F6: inflammatory myofibroblasts were abundant in cancer and inflammation but relatively uncommon in established fibrosis (Fig. 7a,b ), consistent with skin data (Fig. 4b ).
[ { "end": 31, "label": "CellType", "start": 17, "text": "myofibroblasts" } ]
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Inflammatory myofibroblasts are well described in IBD , and we confirmed equivalence of these cells to skin F6: inflammatory myofibroblasts (Supplementary Note 3 and Extended Data Fig. 8g ).
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To further assess the clinical relevance of F6 in IBD, we used an scRNA-seq dataset with clinical metadata .
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F6: inflammatory myofibroblasts were significantly elevated in inflamed tissue, compared to non-inflamed tissue, and their prevalence correlated with clinical inflammation severity scores (Fig. 7e ).
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Receptor–ligand analysis suggested that F6: inflammatory myofibroblasts recruit and maintain neutrophils ( CXCL5/6/8 - CXCR2 and CSF3 - CSF3R ), macrophages/monocytes ( CCL5/26 - CCR1 and CSF3 - CSF3R ) and B cells ( CXCL13 / CXCR5 ) in the skin (Fig. 7c and Methods ).
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These genes were highly expressed in the skin during wound healing, acne and hidradenitis suppurativa, as well as in IBD and lung cancer (Fig. 7b ), suggesting a similar mechanism for recruitment of immune cells across tissues.
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Overall, our results suggest that F3: FRC-like ( CCL19 CD74 TNFRSF13B and IL33 / IL15 ) and F6: inflammatory myofibroblasts ( IL11 MMP1 CXCL5 IL7R ) mediate distinct immune niches driving pathology in the skin and other tissues (Fig. 7f ).
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Given the similar transcriptomic profiles of adult skin F3: FRC-like and intestinal T reticular cells, we hypothesized that these fibroblasts had similar origins in their respective tissues.
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Intestinal FRCs are found in the Peyer’s patch and are thought to arise from prenatal lymphoid tissue organizer (LTo) cells ; however, the skin does not harbor the equivalent of Peyer’s patch and the origin of F3: FRC-like cells remains unknown.
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To explore F3 ontogeny from a developmental perspective, we first integrated adult and prenatal skin fibroblasts and identified the corresponding fibroblast populations (Supplementary Note 4 ).
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This identified that adult F3: FRC-like fibroblasts correlated with prenatal skin CCL19 fibroblasts (Fig. 8a,b ).
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We next queried whether prenatal CCL19 fibroblasts were equivalent to LTo-like cells by jointly integrating human prenatal skin and intestinal data .
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Notably, prenatal skin CCL19 cells and prenatal intestinal mesenchymal LTo cells clustered together (Fig. 8c ).
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Prenatal skin CCL19 cells expressed known mesenchymal LTo markers , including CCL21 , CXCL13 , MADCAM1 , FDCSP and TNFSF11 (RANKL) (Fig. 8c ), suggesting that prenatal skin CCL19 cells may give rise to adult skin F3: FRC-like cells in a manner analogous to intestinal LTo cells.
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We next investigated the LTo gene program in adult skin F3: FRC-like fibroblasts.
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The LTo gene program (including CXCL13 and FDCSP ) was not expressed in healthy adult skin but could be upregulated in specific skin diseases (Fig. 8d,e ), particularly hidradenitis suppurativa.
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Tertiary lymphoid structures (TLS), for which CXCL13 is an important chemokine , are not well recognized in adult human skin, but have recently been reported in hidradenitis suppurativa specifically , suggesting that the LTo gene program contributes to this process.
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We next asked whether F3: FRC-like fibroblasts were unique to adult human skin, as LTo-like fibroblasts have not been reported in mouse embryonic skin .
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Our comparative analysis showed that adult F3: FRC-like cells correspond to mouse Ccl19 fibroblasts (Fig. 8f ).
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Mouse Ccl19 fibroblasts were found predominantly in lymphoid organs (Extended Data Fig. 10a ), but were also present in other tissues such as lung (Fig. 8g ), whereas F3: FRC-like fibroblasts were relatively abundant in healthy human skin, the equivalent Ccl19 fibroblasts were notably rarer in healthy mouse skin (Fig. ...
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In summary, we suggest F3: FRC-like fibroblasts are enriched in human skin and not observed or absent in murine skin.
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We harmonize skin fibroblast subtype nomenclature in health and disease, spatially resolve distinct fibroblast anatomical niches, and identify conserved fibroblast subtypes in human diseases affecting multiple tissues .
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FRCs are the paradigm of immunomodulatory fibroblasts , maintaining discrete immune structures and facilitating specific immune cell interactions in lymphoid organs, with increasing evidence that they are present across human tissues .
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Our data suggest that F3: FRC-like fibroblasts are located in the superficial perivascular niche in human skin and have an analogous role to T zone reticular FRCs, mediating T-DC interactions.
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In keeping with previous murine studies , we find that F3: FRC-like fibroblasts show a uniquely high prevalence in human skin.
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This enrichment of F3: FRC-like fibroblasts in human skin would explain both the absence of fibroblasts in murine skin TLS-like structures and why the prominent perivascular infiltrate structures that characterize many human inflammatory skin diseases are not reported in mice .
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Inflammatory myofibroblasts were recently reported in a large-scale integration of predominantly CAFs and have been independently described in IBD .
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We identify that the same inflammatory myofibroblast phenotype ( IL11 MMP1 CXCL8 IL7R ) can be observed in early human skin wounds, skin cancer and inflammatory skin diseases with scarring risk.
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Consistent with the immune milieu in early wounds, acne and hidradenitis suppurativa, we suggest that these fibroblasts recruit immune cells such as neutrophils and monocytes.
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Neutrophils are also reported to be recruited by inflammatory myofibroblasts in IBD .
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Our study also suggests that F6: inflammatory myofibroblasts are an intermediate myofibroblast differentiation state in human skin, which agrees with recent work in mouse skin and lung .
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Further work validating myofibroblast trajectories in human skin is needed as current trajectory inference methods are limited for predicting multiple cell states converging to a final phenotype, and lineage plasticity is likely in fibroblasts, with both tissue-specific and universal populations suggested to give rise ...
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Notably, skin could serve as an exemplar tissue to further investigate myofibroblast development in humans in vivo given the ability to sample tissue temporally with low morbidity.
[ { "end": 13, "label": "Tissue", "start": 9, "text": "skin" } ]
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A limitation of our study is that we relied on the uncertainty mechanism incorporated in scPoli to identify disease-associated populations.
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Our cross-tissue analysis using semi-supervised integration may underestimate tissue-specific differences between fibroblasts, and further investigation using methods such as contrastive analysis may be valuable.
[ { "end": 125, "label": "CellType", "start": 114, "text": "fibroblasts" } ]
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LTo-like cells in prenatal skin were rare and more definitive lineage tracing methods are required to understand prenatal to adult fibroblast transitions.
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In summary, our annotated skin fibroblast dataset of 357,276 cells provides a foundational resource for fibroblast transcriptomic states in health and across distinct disease categories in skin tissue.
[ { "end": 200, "label": "Tissue", "start": 189, "text": "skin tissue" } ]
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Further comments on annotation can be made via the centralized community annotation platform ( https://celltype.info/project/388 ).
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Raw scRNA-seq data were downloaded and aligned using STARsolo (GRCh38-2020-A reference) unless already available in local storage .
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We included publicly available data generated from fresh skin biopsies using the 10x Genomics Chromium platform.
[ { "end": 70, "label": "Tissue", "start": 57, "text": "skin biopsies" } ]
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We collected essential metadata (sample ID, dataset ID, site status, patient status, sex and anatomic location) as more extensive metadata are being collected as part of the Human Skin Cell Atlas.
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CellBender v.0.3 was used to correct for ambient messenger RNA .
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To remove low-quality cells, we included only cells with >200 genes, >1,000 and <300,000 total unique molecular identifiers and a mitochondrial gene percentage of <15%.
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We calculated doublet scores using scrublet on a per sample basis using scrublet and removed cells with a doublet score of >0.3 (ref. ).
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Integration of all cells was performed using scVI using raw counts .
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For feature (highly variable gene (HVG)) selection, we did not consider the following genes: mitochondrial genes; cell cycle genes, from https://github.com/haniffalab/skin_fibroblast_atlas/blob/main/misc/cc_genes.csv ); hypoxic genes; and ribosomal genes.
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We selected 6,000 HVGs as features, and batch-aware HVG selection was performed by setting the batch key to sample ID.
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In future analyses post-integration, all genes were considered.
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The following hyperparameters were used for the scVI model: number of layers: 2; number of latent dimensions: 30; gene likelihood: zero-inflated negative binomial distribution, and dispersion: gene-batch.
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An early stopping patience of five epochs was used.
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The batch key was ‘sampleID’ and no other covariates were passed to the model for correction.
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We constructed a k -nearest neighbors ( k -NN) graph ( k = 30) using the scVI embedding and performed community detection (Leiden algorithm) with resolution 0.1 for the dataset with all skin cells.
[ { "end": 196, "label": "CellType", "start": 186, "text": "skin cells" } ]
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Visualization in two dimensions was performed using UMAP with the initialized positions from PAGA implemented in scanpy .
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We then selected the fibroblast cluster for further analysis based on canonical marker gene expression, including PDGFRA , DCN and LUM ) (Extended Data Fig. 1a ).
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We did not include a distinct stress response cluster ( MT2A , MT1M , MT1X , HSP90AA1 , JUNB , GADD45B and IER3 ) as this population was not evident on Xenium analysis and thus likely related to cell dissociation.
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For a sensitivity analysis with additional cell types, we also selected Schwann and pericyte clusters.
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We repeated integration using scVI for healthy and phenotypically normal (nonlesional) skin fibroblasts only.
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We used the same workflow as above.
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Visualization in two dimensions was performed using UMAP with positions initialized from PAGA.
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We show gene expression values post-normalization.
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Normalization is a two-step procedure involving depth normalization and variance stabilization.
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We used the shifted logarithm with a scaling factor of 10,000 based on strong performance in a recent benchmarking paper .
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For each cluster, we calculated differentially expressed genes (DEGs) using the t -test with the scanpy rank_genes_groups function.
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The top DEGs for each population are shown in Supplementary Fig. 1a and Supplementary Table 2 .
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Single_Cell
For selecting marker genes to present for each population in Fig. 1c , we selected genes with the highest specificity of expression for that cluster from visualization.
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Single_Cell
We selected our nomenclature for fibroblasts based on our previous report of F1–F3 fibroblasts .
[ { "end": 44, "label": "CellType", "start": 33, "text": "fibroblasts" }, { "end": 94, "label": "CellType", "start": 83, "text": "fibroblasts" } ]
Single_Cell
We refined the names based on spatial location and our in-depth characterization of cell states, including across tissues.
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Single_Cell