Baoguo Li’s research while affiliated with Weizmann Institute of Science and other places

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Publications (18)


Egr1 regulates regenerative senescence and cardiac repair
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June 2024

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355 Reads

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12 Citations

Nature Cardiovascular Research

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Detailed atlas of the immune microenvironment in breast cancer lung metastasis. A, Experimental design of the spontaneous metastasis model conducted in photoactivatable-GFP mice. B, Two-dimensional projection of the transcriptomic profiles of cells from three immune subsets: T and NK; monocyte, macrophage, and DC; and neutrophils. Dots represent single cells and are color-coded according to subpopulation annotation, larger circles represent metacells. See Methods. C, Bubble heat map showing marker gene expression across T and NK cell types from B. Size indicates the fraction of expressing cells. Color indicates the mean log-normalized expression levels. D, As in C but for myeloid populations.
Lung metastases and primary tumors exhibit divergent immune landscapes. A, Scheme illustrating the comparison of primary breast tumors and lung metastases. B, The cumulative cell fraction of the different main immune lineages (left) and subtypes (right) in primary tumors (n = 4 samples) and metastasis core (n = 13). C, Fractions of cells belonging to different immune lineages (from total) or subtypes (from their respective lineage), averaged over primary tumor (x-axis) or metastasis core (y-axis) samples. Size indicates the average of x and y. Color depicts P value of the two-sided T test between x and y, accounting for sample variation. D, PCA of immune compartment makeup, based on cell type and subpopulation fractions. E, Fractions of indicated cell types out of total CD45⁺ cells. F, Fractions of indicated T-cell subtypes from total T cells. G, The log2 ratio of activated T cells (Cd8 Gzma and Cd8 Gzmk) and naïve T cells (Cd8 Dapl1 and Cd4 Lef1). H, Fractions of the indicated monocyte subtypes from total monocytes. I, Comparison of monocyte subtypes gene expression (log2 normalized). J, Fractions of the indicated macrophage subtypes from the total macrophage. K, Comparison of macrophage subtypes gene expression (log2 normalized). L, Enriched gene ontology terms in macrophage subtypes. Two-tailed Student t test was used. In box plots, the center line represents the median, the box limits denote the 25th to the 75th percentiles, and the whiskers represent the minimum and maximum values. Differentially expressed genes (DEG) are colored in red and leading DEGs are labeled. Normalized gene ontology term enrichment score (NES) is shown on the x-axis. For all terms, Padj < 0.05.
The premetastatic lung microenvironment is characterized by the activation of monocytes and neutrophils. A, Scheme illustrating the comparison of control and premetastatic lung tissues. B, The cumulative fraction of the different main immune lineages (left) and subtypes (right) in control (n = 3 samples) and premetastatic (Pre-MET, n = 3) samples. C, Fractions of indicated cell types out of total CD45⁺ cells. D, Fractions of indicated T-cell subtypes from total T cells. E, The log2 ratio of activated T cells (Cd8 Gzma and Cd8 Gzmk) and naïve T cells (Cd8 Dapl1 and Cd4 Lef1). F, Fractions of the indicated monocyte subtypes from total monocytes. G, Comparison of monocyte subtypes gene expression (log2 normalized). H, Enriched gene ontology terms in monocyte subtypes. I, Fractions of the indicated neutrophil subtypes from total monocytes (ns. = non significant). J, Comparison of neutrophil subtypes gene expression (log2 normalized). K, Enriched gene ontology terms in neutrophil subtypes. L, CellChat analysis (see Methods) of differential interaction strength between cell types in Pre-MET and control lungs based on ligand–receptor gene expression (top), and of upregulated ligand–receptor interactions in Pre-MET cells (bottom). Up depicts higher in Pre-MET. M, Scheme illustrating ex vivo cell migration assay. Cells were purified from the bone marrow of normal mice. The lung noncellular fraction was produced from the lungs of control or Pre-MET mice, and the migration of monocytes and granulocytes toward lung-secreted factors was analyzed. N, Quantification of migrated Ly6c⁺ monocytes and Ly6g⁺ granulocytes toward supernatant from normal or pre-MET lung-secreted factors, with or without anti-CCL6 antibody (presented as fold change from the normal mean; error bars, SE). Two-tailed Student t test was used. In box plots, the center line represents the median, the box limits denote the 25th to the 75th percentiles, and the whiskers represent the minimum and maximum values. Differentially expressed genes (DEG) are colored in red and leading DEGs are labeled. Normalized gene ontology term enrichment score (NES) is shown on the x-axis. For all terms, Padj < 0.05.
Progression to lung metastasis is associated with infiltration by unconventional immune cell subtypes. A, Scheme illustrating the comparison of control, relapse-free, distal normal, and metastasis lung tissues. B, The cumulative fraction of the different main immune cell lineages (left) and subtypes (right) per tissue type (control, n = 3 samples; relapse free, n = 6; distal normal, n = 14; metastasis, n = 13). C, PCA of immune compartment makeup, based on cell type and subtype fractions. D, Cellular module analysis. Pairwise Spearman correlation of cell type and subpopulation fraction across samples of distal normal and metastasis (left; color gradient represents Spearman correlation). Consensus hierarchical clustering into four cell modules. Enrichment of each cell type between distal normal and metastasis tissues (right). Size indicates the mean percentage of cells in all samples; color gradient represents the P value of Student t test between metastasis and distal normal per cell population. E, Quantification of cell fractions from total cells per sample in the metastasis cell module analysis in D. CTL, control; PM, premetastasis; RF, relapse free; DN, distal normal; MET, metastasis. F, Fractions of the indicated monocyte subtypes from total monocytes. G, Fractions of the indicated macrophage subtypes from total macrophages. H, Scheme illustrating ex vivo cell migration assay. Cells were purified from the bone marrow of normal mice. The lung noncellular fraction was produced from the distal normal area or metastatic area of metastases-bearing lungs. Migration of monocytes and granulocytes toward lung-secreted factors was analyzed. I, Quantification of migrated Ly6c⁺ monocytes and Ly6g⁺ granulocytes toward supernatant from the distal normal area or metastatic area of metastases-bearing lungs (presented as log2 fold change from the distal normal mean; error bars, SE. Two-tailed paired Student t test was used). J, Scheme and quantification of macrophage-induced T-cell suppression assay. Splenic T cells from normal mice were stimulated and stained with cell proliferation dye and then cocultured for 48 hours with macrophages isolated from the lungs of control, premetastatic (Pre-MET), or metastasis-bearing mice. Error bars, SE. Two-tailed Student t test was used. In box plots, the center line represents the median, the box limits denote the 25th to the 75th percentiles, and the whiskers represent the minimum and maximum values.
The metastatic invasive margin is populated by suppressive TREM2 macrophages. A, Scheme illustrating labeling of cells in metastasis invasive margins and metastatic cores, using PA-GFP. B, Representative fluorescent imaging of lung tissue samples pre- and postphotoactivation of a random region in a control sample, and the metastasis invasive margin or core in metastasis-bearing mice. C, The cumulative fraction of the different main immune cell lineages (left) and subtypes (right). D, Fractions of immune cell lineages (from total) or subtypes (from their respective lineage), averaged over invasive margin (x-axis) or metastasis core (y-axis) samples. Size indicates the average of x and y. Color gradient depicts P value of the two-sided t test between x and y, accounting for sample variation. E, Fractions of Tregs and PMN-MDSCs from their respective lineages. F, Fractions of the indicated monocyte subtypes from total monocytes. G, Fractions of the indicated macrophage subtypes from total macrophages. H, Comparison of macrophage subtypes gene expression (log2 normalized). Differentially expressed genes (DEG) are colored in red and leading DEGs are labeled. I, Enriched gene ontology terms in macrophage subtypes. Normalized gene ontology term enrichment score (NES) is shown on the x-axis. For all terms, Padj < 0.05. J, Cell subtype enrichment in metastasis over distal normal tissues (x-axis), compared with metastasis core over invasive margin (y-axis). K, CD9 and IL7R flow cytometry protein expression values (index sorting) of cells that were annotated as Mreg or Mac Isg20 by following scRNA-seq. L, Activated T cells (or not activated control) were cocultured with the lung-derived macrophage populations (CD45⁺ CD11b⁺ Ly6g⁻, and the indicated gate). Quantification of activated CD8 T cells (CD25⁺ CD69⁺, left), and IFNγ secreted in supernatant (right). M, Representative immuno-fluorescence imaging of EO771 breast cancer lung metastasis, stained for GPNMB (Cyan). Tumor cells shown in red (tdTomato), nuclei shown in blue (DAPI). N, Quantification of the percentage of GPNMB⁺ cells at the metastasis core and invasive margin, at different distances from metastases’ boundary. Two-tailed paired t test was used. In boxplots, the center line represents the median, the box limits denote the 25th to the 75th percentiles, and the whiskers represent the minimum and maximum values.

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Spatial and Temporal Mapping of Breast Cancer Lung Metastases Identify TREM2 Macrophages as Regulators of the Metastatic Boundary

September 2023

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245 Reads

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55 Citations

Cancer mortality primarily stems from metastatic recurrence, emphasizing the urgent need for developing effective metastasis-targeted immunotherapies. To better understand the cellular and molecular events shaping metastatic niches, we used a spontaneous breast cancer lung metastasis model to create a single-cell atlas spanning different metastatic stages and regions. We found that premetastatic lungs are infiltrated by inflammatory neutrophils and monocytes, followed by the accumulation of suppressive macrophages with the emergence of metastases. Spatial profiling revealed that metastasis-associated immune cells were present in the metastasis core, with the exception of TREM2⁺ regulatory macrophages uniquely enriched at the metastatic invasive margin, consistent across both murine models and human patient samples. These regulatory macrophages (Mreg) contribute to the formation of an immune-suppressive niche, cloaking tumor cells from immune surveillance. Our study provides a compendium of immune cell dynamics across metastatic stages and niches, informing the development of metastasis-targeting immunotherapies. Significance Temporal and spatial single-cell analysis of metastasis stages revealed new players in modulating immune surveillance and suppression. Our study highlights distinct populations of TREM2 macrophages as modulators of the microenvironment in metastasis, and as the key immune determinant defining metastatic niches, pointing to myeloid checkpoints to improve therapeutic strategies. This article is featured in Selected Articles from This Issue, p. 2489


Integrative spatial analysis reveals a multi-layered organization of glioblastoma

July 2023

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402 Reads

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5 Citations

Glioma contains malignant cells in diverse states. Hypoxic regions are associated with a unique histology of pseudopalisading cells, while other regions appear to have limited histological organization, reflecting the diffuse nature of glioma cells. Here, we combine spatial transcriptomics with spatial proteomics and novel computational approaches to define glioma cellular states at high granularity and uncover their organization. We find three prominent modes of cellular organization. First, cells in any given state tend to be spatially clustered, such that tumors are composed of small local environments that are each typically enriched with one major cellular state. Second, specific pairs of states preferentially reside in proximity across multiple scales. Despite the unique composition of each tumor, this pairing of states remains largely consistent across tumors. Third, the pairwise interactions that we detect collectively define a global architecture composed of five layers. Hypoxia appears to drive this 5-layered organization, as it is both associated with unique states of surrounding cells and with a long-range organization that extends from the hypoxic core to the infiltrative edge of the tumor. Accordingly, tumor regions distant from any hypoxic foci and tumors that lack hypoxia such as IDH-mutant glioma are less organized. In summary, we provide a conceptual framework for the organization of gliomas at the resolution of cellular states and highlight the role of hypoxia as a long-range tissue organizer.


Detection and enrichment of renal Epo-producing cells
a, Epo expression analysis across tissues in hypoxic mice (0.1% CO, 4 h). Epo qPCR normalized to the Actb housekeeping gene. Data are shown as a fold change in hypoxia over normoxia (n = 1 independent experiment using tissues from n = 1 male mouse, n = 4 technical replicates). b, Epo expression during a hypoxia time course (0.1% CO, 4 h). Data were normalized to Actb and are shown as fold change in hypoxia over normoxia (n = 1 independent experiment using kidneys from n = 1 male mouse per time point, n = 4 technical replicates). c, Transgenic mice carry a bacterial artificial chromosome (BAC) insertion of the Epo locus with CreERT2 replacing Epo exons 2 to 4. The Rosa26 locus harbors a tdTomato transgene that is driven by the CAG promoter, followed by a STOP cassette flanked by loxP sites. Tamoxifen and hypoxia treatment (0.1% CO, 4 h) activate tdTomato and permanently label the Epo-positive cells. d, Representative gating strategy to optimize enrichment of tdTomato⁺ cells. Sort gate A contains live, tdTomato⁺ cells. Sort gate B contains live, tdTomato⁺CD31⁻ cells. Sort gate C contains live, tdTomato⁺CD45⁻CD326⁻ cells. Sort gate D contains live, tdTomato⁺CD45⁻CD326⁻CD31⁻ cells. e, Percentage enrichment of true tdTomato⁺ cells in sort gates from d, as determined by single-cell RNA-seq combined with index-sorting analysis. APC-A, APC area; Ctrl, control; DAPI-A, DAPI area; FSC-A, forward scatter area; PE-A, PE area; SSC-A, side scatter area; WPRE, woodchuck hepatitis virus posttranscriptional regulatory element.
Epo-producing Norn cells are a distinct stromal cell type
a, Two-dimensional projection of 35,834 renal cells combined from normoxic and hypoxic (0.1% CO, 4 h) mice. Cells were grouped into 385 metacells, based on the MetaCell algorithm⁵⁰. Dots represent single cells and colors denote cell-type annotation. The stack bar shows the percentage of renal cell types obtained in hypoxic (23,650 cells) and normoxic (12,184 cells) mouse experiments. Cells were pooled from n = 22 independent experiments with kidneys from n = 52 mice (33 males, 19 females). Collecting duct i., collecting duct intercalated; collecting duct t., collecting duct transient; collecting duct p., collecting duct principal. b, Two-dimensional projections of representative expression (log2(normalized UMI count)) and distribution of known marker genes for Norn cells, fibroblasts and pericytes. c, Summary of differential expression between Norn cells and selected cell populations. Differentially expressed genes (DEGs) were determined by log2(fold change) >1 and FDR adjusted P < 0.0001 (two-way chi-squared test). Data are shown as log2(sum of gene counts). d,e, Differential expression between Norn cells and (d) fibroblasts or (e) pericytes (pooled over all pericyte subsets). Data are shown as log2(size-normalized pooled expression) for equal numbers of cells. Blue dots denote significantly differential genes, red dots denote selected genes shown. f, RNA in situ hybridization of hypoxic mouse kidneys (0.1% CO, 4 h), n = 1 biological independent sample, n = 3 independent experiments. Representative images showing Epo and candidate Norn markers, from top: Cxcl12, Cxcl14, Dcn, Cfh and Pdgfra. Scale bar, 20 μm. Nuclei were stained with DAPI. g, Differential expression between mouse Norn cells derived from normoxic or hypoxic conditions (0.1% CO, 4 h). Data are shown as log2(size-normalized pooled expression) for equal numbers of cells.
ATAC-seq profiling of Norn cells identifies genomewide and Epo locus-specific regulatory elements
a, Schematic of the experimental set-up for single nuclear ATAC-seq (snATAC-seq) and RNA-seq. n = 1 experiment, kidneys from n = 5 mice (2 females, 3 males). b, Two-dimensional projection of single nuclear RNA overlaid on the MARS-seq data. Single nuclei are represented as colored circles with black rims. c, Heatmap showing 29,021 ATAC peaks in promoters clustered with k-means (n = 10). Values indicate average read counts within a region. Each column represents a subsample of 50 cells from each population. Endo., endocyte; Fibro., fibroblast; Peri/Ren, pericyte/Ren1; Podo., podocyte. d, Heatmap showing 95,181 ATAC peaks in enhancer regions clustered with k-means (n = 20). Values indicate average read counts within a region. e, Pseudo-bulk of ATAC-seq peaks are shown. Norn-specific enhancer regions are shaded in transparent rose. Displayed peaks are at the locus-specific kb range. Scales of peaks are displayed from 0.5 to 100. The mammalian conservation (Mamm. conserv.) track is from the UCSC browser. TF motifs: TCF21, HRE, C/EBPδ and GATA6 are highlighted with colored boxes. f, Motif enrichment in all Norn-specific ATAC peaks is calculated using cumulative binomial distributions by HOMER package, correcting for multiple hypotheses using the Benjamini–Hochberg method⁵⁹. Enrichment in n = 5,844 total target sequences over n = 360,380 total background sequences. TCF21 (P = 1 × 10⁻¹⁹⁶) is present in 26.1% of Norn peaks. C/EBPδ (P = 1 × 10⁻⁸²) is present in 13.1% of Norn peaks. GATA6 (P = 1 × 10⁻³⁹) is present in 12.4% of Norn peaks. The HIF-2α–HIF-1β HRE motif was not significantly enriched. g, MARS-seq derived expression of Tcf21, Cebpd, Gata6 and Epas1 show enrichment in Norn cells. h, Motif enrichment within specific ATAC peaks in Norn, pericyte and fibroblast cells was derived using the GimmeMotif framework⁶⁰. Heatmap data shown as log10. Left of heatmap: six TF families are shown as basic leucine zipper (bZIP), bHLH, E2F, SMAD, Cys2–His2 (C2H2) zinc fingers and GATA. Grayscale bar indicates FDR adjusted P value (two-way nonparametric Mann–Whitney U-test) for specificity in Norn cells compared with pericyte and fibroblasts. A selected consensus motif is shown on the right, with the relevant Norn-enriched factor on top of the motif.
Norn cell molecular signature is conserved in human Epo-producing cells
a, Summary of Norn-like signature in kidney scRNA-seq datasets, from left: adult mouse C57BL/6J unenriched⁴⁴, fetal C57BL/6J mouse unenriched⁴⁵, fetal mouse Foxd1⁺ enriched⁴⁶, adult human unenriched⁴² and fetal week 18 human unenriched⁴⁵. Values on the y axis indicate the percentage of Norn cells identified. WT, wild-type. b, Expression of genes found in core Norn gene signature, surface receptors and TFs. Data are shown as log2(fold change) in gene expression enrichment over background. c, Representative gating strategy to enrich for human Norn-like cells. d, Two-dimensional projection of 18,055 cells, grouped together into 71 metacells. Dots represent single cells and colors denote cell-type annotation. n = 3 independent experiments, n = 3 independent biological samples (2 males, 1 female). e, A two-dimensional projection of representative expression (log2(normalized UMI count)) and distribution of selected marker genes for human Norn-like cells, fibroblasts and pericytes. f, GSEA pathway analysis enrichment in mouse and human Norn-like cells. Data are shown as −log10(fold change) in enrichment score using a two-way Kolmogorov–Smirnov statistic test and FDR corrected P values with a threshold of 0.25. Rank was generated using fold over mean of cells separately for mouse and human, using in mouse n = 2,703 Norn cells, n = 3,459 fibroblasts, n = 1,227 pericyte Ren1 and n = 4,716 endothelial cells; and in human n = 1,345 Norn cells, n = 548 fibroblasts, n = 2,473 pericyte Ren1 and n = 5,777 endothelial cells. CMV, cytomegalovirus; E2F; EMT, epithelial-mesenchymal transition; HCC, hepatocellular carcinoma; KO, knock out; TGFb, transforming growth factor beta. g, Differential expression between mouse and human Norn cells and fibroblasts using two-way Mann–Whitney U-test with FDR corrected P values. Data are shown as log2(size-normalized pooled expression) for an equal number of cells. Blue dots denote significant genes, red dots denote selected conserved genes present in both mouse and human Norn cells. h, mRNA-FISH of human hypoxic kidney (50% CO-bound hemoglobin). n = 3 independent experiments, kidney from n = 1 human (female). Representative images of EPO together with candidate Norn markers. Scale bar, 20 μm. Nuclei were stained with DAPI.
The transcriptional and regulatory identity of erythropoietin producing cells

April 2023

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1,664 Reads

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39 Citations

Nature Medicine

Erythropoietin (Epo) is the master regulator of erythropoiesis and oxygen homeostasis. Despite its physiological importance, the molecular and genomic contexts of the cells responsible for renal Epo production remain unclear, limiting more-effective therapies for anemia. Here, we performed single-cell RNA and transposase-accessible chromatin (ATAC) sequencing of an Epo reporter mouse to molecularly identify Epo-producing cells under hypoxic conditions. Our data indicate that a distinct population of kidney stroma, which we term Norn cells, is the major source of endocrine Epo production in mice. We use these datasets to identify the markers, signaling pathways and transcriptional circuits characteristic of Norn cells. Using single-cell RNA sequencing and RNA in situ hybridization in human kidney tissues, we further provide evidence that this cell population is conserved in humans. These preliminary findings open new avenues to functionally dissect EPO gene regulation in health and disease and may serve as groundwork to improve erythropoiesis-stimulating therapies.



Schematic representation of the ST analysis pipeline with DestVI
a, A ST analysis workflow relies on two data modalities, producing unpaired transcriptomic measurements, each in the form of count matrices. The ST data measures the gene expression ys in a given spot s and its location λs. However, each spot may contain multiple cells. The scRNA-seq data measure the gene expression xn in a cell n, but the spatial information is lost because of tissue dissociation. After annotation, we may associate each cell with a cell type cn. These matrices are the input to DestVI, composed of two LVMs: the scLVM and the stLVM. DestVI outputs a joint representation of the single-cell data and the spatial data by estimating the proportion of every cell type in every spot and projecting the expression of each spot onto cell-type-specific latent spaces. These inferred values may be used for performing downstream analysis, such as cell-type-specific DE and comparative analyses of conditions. b, Schematic of the scLVM. RNA counts and cell type information from the scRNA-seq data are jointly transformed by an encoder neural network into the parameters of the approximate posterior of γn, a low-dimensional representation of cell-type-specific cell state. Next, a decoder neural network maps samples from the approximate posterior of γn along with the cell type information cn to the parameters of a count distribution for every gene. The superscript notation fg denotes the g-th entry ρng of the vector ρn. c, Schematic of the stLVM. RNA counts from the ST data are transformed by an encoder neural network into the parameters of the cell-type-specific embeddings γsc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma _s^c$$\end{document}. Free parameters βsc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _s^c$$\end{document} encode the abundance of cell type c in spot s and may be normalized into CTP πsc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi _s^c$$\end{document} (Methods). The decoder from the scLVM model maps cell-type-specific embeddings γsc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma _s^c$$\end{document} to estimates of cell-type-specific gene expression. These values are summed across all cell types, weighted by the abundance parameters βsc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _s^c$$\end{document}, to obtain the parameter rsg approximating the gene expression of the spot. After training, the decoder may be used to perform cell-type-specific imputation of gene expression across all spots.
Evaluating the performance on DestVI on simulations
a, Schematic view of the semi-simulation framework. For each cell type of an scRNA-seq dataset, we learned a continuous model of gene expression. We sampled spatially relevant random vectors on a grid to encode the proportion of every cell type in every spot πsc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi _s^c$$\end{document} as well as the cell-type-specific embeddings γsc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma _s^c$$\end{document}. Then, we feed those parameters into the learned continuous model to generate ST data (Methods). b, c, Visualization of the single-cell data and the cell state labels used for comparison to competing methods (UMAP embeddings of the single-cell data; 32,000 cells). b, Cells are colored by cell type. c, Cells are colored by the sub-cell types, obtained via hierarchical clustering (five clusters). d–f, Comparison of DestVI to competing algorithms, possibly applied to different clustering resolutions. Performance is not reported for cases that did not terminate by 3 hours (SPOTLight with eight sub-clusters; Methods). d, Spearman correlation of estimated CTP compared to ground truth for all methods. e, Spearman correlation of estimated cell-type-specific gene expression compared to ground truth, for combinations of spot and cell type for which the proportion is >0.4 for the parent cluster (not applicable to algorithms run at the coarsest level, as they do not provide cell type proportions at any sub-cell-type level). f, Scatter plot of both metrics that shows the tradeoff reached by all methods. Colors in this panel are in concordance with the ones from e and f. g, h, Follow-up stress tests for DestVI. g, Accuracy of imputation, measured by Spearman correlation as a function of the cell type proportion in a given spot. h, Head-to-head comparison of estimated cell type proportion against ground truth across all spots and cell types (8,000 combinations of spot and cell type). i, j, Ablation studies for the amortization scheme used by DestVI. ‘None’ stands for vanilla MAP inference. ‘Latent’ and ‘Proportion’ refer to only the inference of the latent variables and only the cell type abundance being amortized with a neural network, respectively. ‘Both’ refers to fully amortized MAP inference. i, Spearman correlation of estimated CTP compared to ground truth. j, Spearman correlation of estimated cell-type-specific gene expression compared to ground truth. UMAP, uniform manifold approximation and projection.
Application of DestVI to the murine lymph nodes
a, Schematics of the experimental pipeline. We processed murine lymph nodes with ST (10x Visium) and scRNA-seq (10x Chromium) after 48-hour stimulation by MS compared to PBS control (two sections from each condition). b, ST data (1,092 spots; only three sections passed the quality check) (Supplementary Methods). Sample MS-1 and samples PBS/MS-2 were processed on different capture areas of the same Visium gene expression slide. c, UMAP projection of the scRNA-seq data (14,989 cells). d, Spatial autocorrelation of the CTP. e, Spatial distribution of CTP for B cells, CD8 T cells, monocytes and NK cells, as inferred by DestVI. f, Embedding of the monocytes (circles; 128 single cells) alongside the monocyte-abundant spots (crosses; 79 spots). Single cells are colored by expression of IFN-II genes identified by Hotspot (Fcgr1, Cxcl9 and Cxcl10; Supplementary Figs. 12–14). g, Imputation of monocyte-specific expression of the IFN-II marker genes for the monocyte-abundant spots of the spatial data (log-scale). h, Monocyte-specific DE analysis between MS and PBS lymph nodes (2,000 genes, 79 spots, total 10,980 samples from the generative model). Red dots designate genes with statistical significance, according to our DE procedure (two-sided Kolmogorov–Smirnov test, adjusted for multiple testing using the Benjamini–Hochberg procedure; Methods). i, Immunofluorescence imaging from an MS lymph node, staining for CD11b, CD64 and Ly6C in the IFA. Scale bar, 50 μm. j, Embedding of the B cells (circles, 8,359 single cells) alongside the B-cell-abundant spots (crosses, 579 spots). Single cells are colored by expression of the IFN-I genes identified by Hotspot (Ifit3, Ifit3b, Stat1, Ifit1, Usp18 and Isg15; Supplementary Figs. 17 and 18). k, Imputation of B-cell-specific expression of the IFN-I gene module on the spatial data (log-scale), reported on B-cell-abundant spots. l, B-cell-specific DE analysis between MS and PBS lymph nodes (2,000 genes, 579 spots, 6,160 samples). Red dots designate genes with statistical significance, according to our DE procedure (two-sided Kolmogorov–Smirnov test, adjusted for multiple testing using the Benjamini–Hochberg procedure; Methods). m, Immunofluorescence imaging from an MS lymph node, staining for IFIT3, B220 and Ly6C in B cell follicle near the inflammatory IFA. Scale bar, 50 μm. UMAP, uniform manifold approximation and projection. cDC2, type 2 conventional dendritic cell; GD, gamma delta; pDC, plasmacytoid dendritic cell.
Application of DestVI to a MCA205 tumor sample
a, Schematics of the experimental pipeline. We performed ST (10x Visium) and scRNA-seq (single-cell MARS sequencing protocol) on MCA205 tumor that contains heterogeneous immune cell populations 14 days after intracutaneous transplantation into the wild-type mouse (two sections). b, Visualization of the ST data for two MCA205 tumor sections, after quality control (4,027 spots). Scale bar, 1,000 μm. The two sections were processed on the different capture areas of the same Visium gene expression slide. c, UMAP projection of the scRNA-seq data (8,051 cells), embedded by scVI and manually annotated. d, Spatial autocorrelation of the CTP for every cell type, computed using Hotspot. e, Spatial distribution of CTP for DCs, monocytes and macrophages (Mon-Mac), CD8 T cells and NK cells. f, Immunofluorescence imaging from neighboring tumor sections, using antibodies for MHCII⁺ cells showing for DCs (Section-3, +20 μm from Section-2), F4/80⁺MHCII⁻ cells showing for Mon-Mac (Section-3, +20 μm from Section-2), TCRb⁺ cells showing for CD8 T cells (Section-5, +60 μm from Section-2) and NK1.1⁺ cells showing for NK cells (Section-4, +30 μm from Section-2). All scale bars denote 500 μm. Red solid lines indicate the section boundary. Right side is the MCA205 tumor marginal boundary. The cells positive for staining marker are segmented and annotated using QuPath and showing yellow color here with changed brightness and contrast (Supplementary Methods). UMAP, uniform manifold approximation and projection.
DestVI identifies a hypoxic population of macrophages in the tumor core
a, Visualization of the hypoxia gene expression module on the Mon-Mac cells from the scRNA-seq data (4,400 cells), on the embedding from scVI (identified using Hotspot; Supplementary Figs. 28 and 29). b, Imputation of gene expression for this module on the spatial dataset (log-scale), reported only on spots with high abundance of Mon-Mac (3,906 spots across the two sections). Imputation for other modules is shown in Supplementary Fig. 30. c, H&E-stained histology of Section-1 (left), with overlapping Mreg-identified regions from DestVI showing red polygons (as identified in Supplementary Fig. 32). Blue arrows show the location of cells from the necrotic core. H&E-stained histology showing a magnification of the necrotic core of the yellow frame in Section-1 (right). Scale bar, 55 μm. d, Mon-Mac cell-specific DE analysis between the Mreg-enriched areas and the rest of the tumor section (2,886 genes; 379 spots for the Mreg-enriched area and 361 randomly sampled spots from the rest of the tumor; total of 2,220 samples from the generative model). Red dots designate genes with statistical significance, according to our DE procedure (two-sided Kolmogorov–Smirnov test, adjusted for multiple testing using the Benjamini–Hochberg procedure; Methods). e, Representative image of the multiplexed immunofluorescence staining. Left, Hypoxic areas as identified by the Hypoxyprobe (HYPO) in a whole MCA205 tumor section. Two yellow frames show the hypoxic areas with necrotic cores. Scale bar, 500 μm. Middle, Magnification of a necrotic core with F4/80, Arg1, GPNMB, HYPO and DAPI staining. Scale bar, 50 μm. Right, Annotation of different macrophages surrounding the necrotic core. Different colors shown in the legend bar show different staining combinations. Red spindle shows the extent of hypoxia. Blue arrow shows the location of cells from the necrotic core. Scale bar, 50 μm.
DestVI identifies continuums of cell types in spatial transcriptomics data

April 2022

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581 Reads

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151 Citations

Nature Biotechnology

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools (https://scvi-tools.org).


LGR5 expressing skin fibroblasts define a major cellular hub perturbed in scleroderma

April 2022

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242 Reads

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104 Citations

Cell

Systemic sclerosis (scleroderma, SSc) is an incurable autoimmune disease with high morbidity and mortality rates. Here, we conducted a population-scale single-cell genomic analysis of skin and blood samples of 56 healthy controls and 97 SSc patients at different stages of the disease. We found immune compartment dysfunction only in a specific subtype of diffuse SSc patients but global dysregulation of the stromal compartment, particularly in a previously undefined subset of LGR5⁺-scleroderma-associated fibroblasts (ScAFs). ScAFs are perturbed morphologically and molecularly in SSc patients. Single-cell multiome profiling of stromal cells revealed ScAF-specific markers, pathways, regulatory elements, and transcription factors underlining disease development. Systematic analysis of these molecular features with clinical metadata associates specific ScAF targets with disease pathogenesis and SSc clinical traits. Our high-resolution atlas of the sclerodermatous skin spectrum will enable a paradigm shift in the understanding of SSc disease and facilitate the development of biomarkers and therapeutic strategies.


The interaction of CD4+ helper T cells with dendritic cells shapes the tumor microenvironment and immune checkpoint blockade response

March 2022

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1,065 Reads

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147 Citations

Nature Cancer

Despite their key regulatory role and therapeutic potency, the molecular signatures of interactions between T cells and antigen-presenting myeloid cells within the tumor microenvironment remain poorly characterized. Here, we systematically characterize these interactions using RNA sequencing of physically interacting cells (PIC-seq) and find that CD4+PD-1+CXCL13+ T cells are a major interacting hub with antigen-presenting cells in the tumor microenvironment of human non-small cell lung carcinoma. We define this clonally expanded, tumor-specific and conserved T-cell subset as T-helper tumor (Tht) cells. Reconstitution of Tht cells in vitro and in an ovalbumin-specific αβ TCR CD4+ T-cell mouse model, shows that the Tht program is primed in tumor-draining lymph nodes by dendritic cells presenting tumor antigens, and that their function is important for harnessing the antitumor response of anti-PD-1 treatment. Our molecular and functional findings support the modulation of Tht–dendritic cell interaction checkpoints as a major interventional strategy in immunotherapy. Amit and colleagues report that the specific interaction of a CD4+PD-1+CXCL13+ T-cell subset with antigen-presenting cells reprograms the tumor microenvironment and response to immune checkpoint inhibitors in non-small cell lung cancer.


Citations (17)


... 78-80 EGR1, a well-known immediate-early response gene, has been implicated in various immune processes, including macrophage activation and cytokine production. [81][82][83] Its persistent activation in monocyte subsets during PR suggests that it may act as a key driver of the pro-inflammatory state that characterises this disease stage. ...

Reference:

Single‐cell transcriptional profiling reveals cellular senescence and inflammatory persistence as key features of type 1 diabetes partial remission
Egr1 regulates regenerative senescence and cardiac repair

Nature Cardiovascular Research

... Likewise, Jain et al. investigated cancer-associated fibroblasts and glioma stem cell interactions, identifying spatially relevant marker expression and cell type localizations 11 . More recently, multiple studies have examined high-grade glioblastomas by integrating single-cell RNA sequencing, spatial transcriptomics, and other multi-omic methods to better understand the relation between tissue architecture and invasion of the tumor microenvironment [12][13][14][15] . ...

Integrative spatial analysis reveals a multi-layered organization of glioblastoma

Cell

... Close interactions within FOLR2 + macrophages niches appear to be a major factor, as epithelial cells could induce necroptosis in these macrophages during cancer progression via direct contact [67]. Advanced techniques, such as spatial omics, time-resolved single-cell transcriptomics and medical imaging, serve promising strategies to track cancer evolution and depict elaborate cell communications [77,78]. Despite these advances, distinguishing FOLR2 + TAMs from FOLR2 + macrophages in the normal tissues using several markers remains challenging. ...

Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma
  • Citing Article
  • December 2023

Cell

... Technological advances in single-cell RNA sequencing (scRNAseq) and digital spatial profiling have deconvoluted the tumor microenvironment (TME), revealing the complex crosstalk between neoplastic cells and lung stromal components [4][5][6][7][8]. ...

Spatial and Temporal Mapping of Breast Cancer Lung Metastases Identify TREM2 Macrophages as Regulators of the Metastatic Boundary

... High grade brain tumors, both primary and metastatic, pose formidable therapeutic challenges (1). In this regard, the uniqueness of the brain microenvironment and its responses to the neoplastic process have been implicated in many aspects of disease intractability and extensively studied using bulk RNA sequencing (2,3), singlecell RNA sequencing (scRNAseq) (4)(5)(6), spatial transcriptomics (7)(8)(9), mass spectrometry (10)(11)(12), immunostaining (2,3), CyToF (10,11), and other techniques. Paradigmatic in this regard is the cellular landscape of primary astrocytic brain tumors, such as glioblastoma (GBM) (7), the cellular milieu of which comprises a multiplicity of noncancer cells including vascular endothelial cells (ECs), pericytes, astrocytes, oligodendrocytes, microglia, and macrophages (13)(14)(15)(16), with notable limitation in the presence of cytotoxic immune effectors, such as natural killer (NK) and CD8α + T cells (17,18). ...

Integrative spatial analysis reveals a multi-layered organization of glioblastoma

... Another example of the successful application of single-cell multiomics to describe an underrepresented population was the characterization of erythropoietin-producing cells. Kragesteen et al. applied a single-cell multiomics approach to sorted erythropoietin-producing cells, identifying TCF21, CEBPD, and GATA6 as candidate master regulators, beyond the paradigm of HIF-2α-HIF-1β circuit in erythropoietin regulation [57]. ...

The transcriptional and regulatory identity of erythropoietin producing cells

Nature Medicine

... Microglia are innate immune cells of the central nervous system (CNS), which not only have a unique origin but also adapt to the microenvironment in specific tissues over the long term, exhibiting distinctive functional characteristics. Emerging evidence suggests that microglia originate from erythromyeloid progenitors (EMPs) in the yolk sac and migrate into the brain, where they colonize, differentiate, and mature, ultimately maintaining immune homeostasis in the CNS environment 17,18 . ...

Human microglia development in the embryonic brain

Life Medicine

... These models often overlook complex gene expression variation and are sensitive to incomplete or imbalanced cell type representation in the reference. Probabilistic approaches such as DestVI [18], Cell2location [19], and Redeconve [20] incorporate latent variable modeling to capture uncertainty and improve robustness, but their inference procedures can be computationally demanding and may not scale efficiently to large tissue sections. Spatially informed methods like CARD [16] and Tangram [21] introduce spatial priors or alignment strategies, yet they often depend on rigid spatial assumptions that may not generalize well across tissue types. ...

DestVI identifies continuums of cell types in spatial transcriptomics data

Nature Biotechnology

... Moreover, the exacerbation of fibrosis in systemic scleroderma correlates with the loss of CXCL12 + and PI16 + steady-state fibroblasts, which is essential for maintaining tissue balance and vascularization 124 . Interestingly, recent studies have downplayed the role of LRG5 + fibroblasts previously associated with disease progression, suggesting that its complexity remains incompletely understood 123,125 . ...

LGR5 expressing skin fibroblasts define a major cellular hub perturbed in scleroderma
  • Citing Article
  • April 2022

Cell

... These subsets modulate immune responses by secreting cytokines (e.g., IFN-γ, IL-4, and IL-17), indirectly exerting antitumor effects [18]. A reduction in CD4 + T cells diminishes cytokine release and B-cell-mediated antibody production, thereby weakening antitumor immunity [19]. Additionally, some CD4 + T cells differentiate into immunosuppressive Tregs and naïve T cells. ...

The interaction of CD4+ helper T cells with dendritic cells shapes the tumor microenvironment and immune checkpoint blockade response

Nature Cancer