<p>(A) UMAP representation of dimensionally reduced data following batch-corrected graph-based clustering of all tumor epithelial cells annotated by sample. (B) Heatmap depicting expression of five highest significantly expressed genes (adjusted p-value <0.05) per patient. (C) Heatmap representation of inferred single-cell CNV profiles of all tumors and reference cells. (A-C) Data from seven mCRCs.</p>
<p>(A-C) Comparisons with Pearson correlation between (A) proportions of cell lineages in five samples with both scRNA-seq and CODEX data. (B) Average expression of LGALS3 and CD68 in macrophages across all 15 patients. (C) Average expression of COL4A1 and ACTA2 in CAFs across all 15 patients.</p>
Abstract Single cells influence, and are shaped by, their local spatial niche. Technologies for in situ measurement of gene expression at the transcriptome scale have enabled the detailed profiling of the spatial distributions of cell types in tissue as well as the interrogation of local signaling patterns between cell types [1]. Towards these goals, we propose a new statistical procedure called niche-differential expression (niche-DE) analysis. Niche-DE identifies cell-type specific niche-associated genes, defined as genes whose expression within a specific cell type is significantly up- or down-regulated, in the context of specific spatial niches. We develop effective and interpretable measures for global false discovery control and show, through the analysis of data sets generated by myriad protocols, that the method is robust to technical issues such as over-dispersion and spot swapping. Niche-DE can be applied to low-resolution spot- and ROI-based spatial transcriptomics data as well as data that is single-cell or subcellular in resolution. Based on niche-DE, we also develop a procedure to reveal the ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. When applied to 10x Visium data from liver metastases of colorectal cancer, niche-DE identifies marker genes for cancer-associated fibroblasts and macrophages and elucidates ligand-receptor crosstalk patterns between tumor cells, macrophages and fibroblasts. Co-detection by indexing (CODEX) was performed on the same patient samples, to corroborate the niche-DE results.
Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration usually involve a lot of guesswork: Cells across batches should be aligned to be "appropriately" mixed, while preserving "main cell type clusters". We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, to recover biological signal that is lost during single cell data integration. CellANOVA utilizes a "pool-of-controls" design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are shown to be validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where the recovered biological signals are replicated in an independent study.
ABSTRACT Purpose The liver is the most frequent metastatic site for colorectal cancer ( CRC ). Its microenvironment is modified to provide a niche that allows CRC cell growth. This study focused on characterizing the cellular changes in the metastatic CRC ( mCRC ) liver tumor microenvironment ( TME ). Experimental Design We analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue and peripheral blood mononuclear cells using single cell RNA-seq ( scRNA-seq ). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution. Results We identified TME-specific SPP1 -expressing macrophages with altered metabolism features, foam cell characteristics and increased activity for extracellular matrix ( ECM ) organization. SPP1+ macrophages and fibroblasts expressed complementary ligand receptor pairs with the potential to mutually influence their gene expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of SPP1 + macrophages and fibroblasts using gene expression data. An increased proportion of TME fibroblasts was associated with worst prognosis in these patients. Conclusions We demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports CRC growth in the immunosuppressed metastatic niche in the liver. These features can be used to target these immune checkpoint resistant MSS tumors. TRANSLATIONAL RELEVANCE The liver is the commonest site for metastatic colorectal cancer ( mCRC ). Alterations in the tumor microenvironment ( TME ) allow metastatic cells to seed the distant liver site and grow. Leveraging single-cell RNA sequencing, we discovered a distinct SPP1 + macrophage cell state with pro-fibrogenic gene expression and altered metabolism. These SPP1 + macrophages communicated with fibroblasts, mutually influencing each other’s gene expression program. Using spatial imaging, we confirmed proximal colocalization between macrophages and fibroblasts in the mCRC TME, which is required for intercellular communication. These states and intercellular communication promoted immunosuppression in the TME, with a lack of dysfunctional anti-tumor CD8 T cells and prevalence of regulatory T cells. Increased fibroblasts were associated with worst prognosis in an independent patient cohort. Our results identified novel TME features that result in reshaping of the metastatic niche that allows progression of mCRC. These features can be potential targets for mCRC treatment, which is microsatellite stable and resistant to immune checkpoint blockade.
<p>Supplemental Table 1: Antibodies, reporters, imaging order and exposure times used in CODEX imaging. EPCAM, SPP1, CD163 were excluded from downstream analysis due to non-specific staining; Supplemental Table 2: Sequencing metrics for scRNA-seq; Supplemental Table 3: Absolute number of cells detected from each sample per cell lineage; Supplemental Table 4: Differentially expressed genes belonging to respective macrophage clusters compared by site of sample origin. pct.1, pct.2 indicate percentage of cells expressing given gene in the cluster of interest and all other comparison clusters respectively; Supplemental Table 5: Enriched reactome pathways in differentially expressed genes in tumor macrophages; Supplemental Table 6: Gene signatures for Foam cells (Fernandez et.al., 2020, PMID 31591603) and scar-associated macrophages (Ramachandran et. al., 2020, PMID 31597160); Supplemental Table 7: Annotation of differentially expressed genes in CAFs with the components of the matrisome expression program; Supplemental Table 8: Spatial proximity analysis examining whether fibroblasts are more proximal to macrophages than any other cell type.</p>
<p>Supplemental Table 1: Antibodies, reporters, imaging order and exposure times used in CODEX imaging. EPCAM, SPP1, CD163 were excluded from downstream analysis due to non-specific staining; Supplemental Table 2: Sequencing metrics for scRNA-seq; Supplemental Table 3: Absolute number of cells detected from each sample per cell lineage; Supplemental Table 4: Differentially expressed genes belonging to respective macrophage clusters compared by site of sample origin. pct.1, pct.2 indicate percentage of cells expressing given gene in the cluster of interest and all other comparison clusters respectively; Supplemental Table 5: Enriched reactome pathways in differentially expressed genes in tumor macrophages; Supplemental Table 6: Gene signatures for Foam cells (Fernandez et.al., 2020, PMID 31591603) and scar-associated macrophages (Ramachandran et. al., 2020, PMID 31597160); Supplemental Table 7: Annotation of differentially expressed genes in CAFs with the components of the matrisome expression program; Supplemental Table 8: Spatial proximity analysis examining whether fibroblasts are more proximal to macrophages than any other cell type.</p>
<div>AbstractPurpose:<p> The liver is the most frequent metastatic site for colorectal cancer. Its microenvironment is modified to provide a niche that is conducive for colorectal cancer cell growth. This study focused on characterizing the cellular changes in the metastatic colorectal cancer (mCRC) liver tumor microenvironment (TME).</p>Experimental Design:<p>We analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue, and peripheral blood mononuclear cells using single-cell RNA sequencing (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution.</p>Results:<p>We identified TME-specific <i>SPP1</i>-expressing macrophages with altered metabolism features, foam cell characteristics, and increased activity in extracellular matrix (ECM) organization. <i>SPP1<sup>+</sup></i> macrophages and fibroblasts expressed complementary ligand–receptor pairs with the potential to mutually influence their gene-expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, which is a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of <i>SPP1</i><sup>+</sup> macrophages and fibroblasts using gene-expression data. An increased proportion of TME fibroblasts was associated with the worst prognosis in these patients.</p>Conclusions:<p>We demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports colorectal cancer growth in the immunosuppressed metastatic niche in the liver. These features can be used to target immune-checkpoint–resistant MSS tumors.</p></div>
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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