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Integrated analysis of molecular atlases unveils modules driving developmental cell subtype specification in the human cortex.
Human brain development requires generating diverse cell types, a process explored by single-cell transcriptomics. Through parallel meta-analyses of the human cortex in development (seven datasets) and adulthood (16 datasets), we generated over 500 gene co-expression networks that can describe mechanisms of cortical development, centering on peak stages of neurogenesis. These meta-modules show dynamic cell subtype specificities throughout cortical development, with several developmental meta-modules displaying spatiotemporal expression patterns that allude to potential roles in cell fate specification. We validated the expression of these modules in primary human cortical tissues. These include meta-module 20, a module elevated in FEZF2+ deep layer neurons that includes TSHZ3, a transcription factor associated with neurodevelopmental disorders. Human cortical chimeroid experiments validated that both FEZF2 and TSHZ3 are required to drive module 20 activity and deep layer neuron specification but through distinct modalities. These studies demonstrate how meta-atlases can engender further mechanistic analyses of cortical fate specification.
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Identification of a novel immune-related gene signature by single-cell and bulk sequencing for the prediction of the immune landscape and prognosis of breast cancer.
BACKGROUND: As a common cause of cancer-related deaths in women, BRCA (breast cancer) shows complexity and requires precise biomarkers and treatment methods. This study delves into the molecular makeup of BRCA, focusing on immune profiles, molecular subtypes, gene expression and single-cell analysis. METHODS: XCell was used to assess immune infiltration based on TCGA (the Cancer Genome Atlas) data and the clustering analysis was made. Differentially expressed genes were examined in distinct clusters, and the WGCNA (weighted correlation network analysis) was made to establish co-expression networks. The prognostic models were developed by Cox and LASSO-Cox regression. The clustering analysis, GSEA (Gene set enrichment analysis), GSVA (gene set variation analysis) and communication analysis of the single-cell dataset GSE161529 were performed to investigate the functional relevance. Real-time polymerase chain reaction (RT-PCR) was employed for evaluating gene expression. RESULTS: The results revealed significant differences in immune cell infiltration between two clusters (C1 and C2). C2 had poorer survival outcomes, which was associated with higher expression of immune checkpoints PD1 and PD-L1. The gene modules identified via WGCNA were correlated with the immune-based subtypes. Then, a prognostic model comprising seven genes (ACSL1, ABCB5, XG, ADH4, OPN4, NPR3, NLGN1) was used to divide patients into high- and low-risk subgroups. The high-risk group had worse prognosis and higher scores of TIDE (Tumor Immune Dysfunction and Exclusion). The single-cell analysis depicted the immune landscape. Macrophages and endothelial cells exhibited higher AUCell scores. In cellular communication analysis, notably significant ligand-receptor interactions of HLA-DRA-> CD4 and TNFSF13B-> HLA-DPB1 were observed. The proportion of endothelial cells was correlated with risk scores. Finally, RT-PCR results illustrated the expression of seven genes in BRCA specimens. CONCLUSION: The integrative analysis provides new insights into molecular complexities of BRCA. Immune profiles and gene signatures hold potential for improving stratification of BRCA patients and guiding the development of personalized immunotherapy strategies.
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Transcriptomic Profiling of Human Placenta in Gestational Diabetes Mellitus at the Single-Cell Level.
Gestational diabetes mellitus (GDM) is associated with an increased risk of adverse pregnancy outcomes. Increasing evidence shows that placentation defects may play important roles in GDM. However, our understanding of the human placenta remains limited. In this study, we generated a comprehensive transcriptomic profile of cellular signatures and transcriptomes in the human placenta in GDM using single-cell RNA sequencing (scRNA-seq), constructed a comprehensive cell atlas, and identified cell subtypes and subtype-specific marker genes. In addition, we investigated the placental cellular function and intercellular interactions in GDM. These findings help to elucidate the molecular mechanisms of GDM, and may facilitate the development of new approaches to GDM treatment and prevention.
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A molecular atlas of adult C. elegans motor neurons reveals ancient diversity delineated by conserved transcription factor codes.
Motor neurons (MNs) constitute an ancient cell type targeted by multiple adult-onset diseases. It is therefore important to define the molecular makeup of adult MNs in animal models and extract organizing principles. Here, we generated a comprehensive molecular atlas of adult Caenorhabditis elegans MNs and a searchable database (http://celegans.spinalcordatlas.org). Single-cell RNA-sequencing of 13,200 cells revealed that ventral nerve cord MNs cluster into 29 molecularly distinct subclasses. All subclasses are delineated by unique expression codes of either neuropeptide or transcription factor gene families. Strikingly, we found that combinatorial codes of homeodomain transcription factor genes define adult MN diversity both in C. elegans and mice. Further, molecularly defined MN subclasses in C. elegans display distinct patterns of connectivity. Hence, our study couples the connectivity map of the C. elegans motor circuit with a molecular atlas of its constituent MNs, and uncovers organizing principles and conserved molecular codes of adult MN diversity.
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AllenDigger, a Tool for Spatial Expression Data Visualization, Spatial Heterogeneity Delineation, and Single-Cell Registration Based on the Allen Brain Atlas.
Spatial transcriptomics can be used to capture cellular spatial organization and has facilitated new insights into different biological contexts, including developmental biology, cancer, and neuroscience. However, its wide application is still hindered by its technical challenges and immature data analysis methods. Allen Brain Atlas (ABA) provides a great source for spatial gene expression throughout the mouse brain at various developmental stages with in situ hybridization image data. To the best of our knowledge, the portal developed to access spatial expression data is not very useful to biologists. Here, we developed a toolkit to collect and preprocess expression data from the ABA and allow a friendlier query to visualize the spatial distribution of genes of interest, characterize the spatial heterogeneity of the brain, and register cells from single-cell transcriptomics data to fine anatomical brain regions via machine learning methods with high accuracy. AllenDigger will be very helpful to the community in precise spatial gene expression queries and add extra spatial information to further interpret the scRNA-seq data in a cost-effective manner.
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A single-cell atlas of ulcerative colitis reveals neutrophil-stromal circuits linked to biologic therapy resistance.
Ulcerative colitis (UC) is a chronic inflammatory bowel disease marked by immune cell infiltration, crypt erosion, and severe ulceration. In phase 3 studies with etrolizumab, the results of a transcriptional analysis of colonic biopsies revealed that etrolizumab-mediated integrin β7 blockade, but not adalimumab (a TNF-blocking antibody), reduced genes associated with integrin αEβ7+ intraepithelial lymphocytes (IELs). Both treatments significantly reduced stromal and myeloid cell-related genes linked to Mayo Clinic Score (MCS) remission status. A single-cell atlas from UC biopsies identified 36 distinct cell populations, including myeloid cells. This atlas enabled cell-specific signatures and cellular deconvolution of the phase 3 data, showing reductions in neutrophils, monocyte-derived macrophages, and inflammatory fibroblasts, along with increases in epithelial cells common to both treatments. Pseudo-time analyses identified four neutrophil subsets, transitioning from PADI4hi, OSMhi, and MX1hi to CXCR4hi populations. PADI4hi and OSMhi neutrophils exhibited high protease, cytokine (CXCL1, IL1B, OSM), and chemokine receptor (CXCR1, CXCR2) levels, while MX1hi expressed markers of IFN exposure. CXCR4hi neutrophils showed elevated CXCL2, TNF, and VEGFA levels. Notably, interactions between PADI4hi and OSMhi neutrophils and inflammatory fibroblasts, such as OSM and IL1B, were associated with MCS remission with both drugs. CXCR4hi neutrophils showed only minor changes unrelated to clinical outcomes. These findings suggest that neutrophils are highly heterogeneous, with abundant interactions in inflamed colonic tissue, potentially perpetuating chronic disease. Disrupting neutrophil interactions with myeloid and resident cells like inflammatory fibroblasts could reduce inflammation, possibly enhancing clinical remission rates.
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Single-cell RNA profiling reveals an immunosuppressive microenvironment in EGFR double-mutant non-small cell lung cancer.
BACKGROUND: Non-small cell lung cancer (NSCLC) patients with double-driver gene mutations are reported with poorer survival outcomes and reduced therapy responses compared to single-mutant (SM) patients. While substantial progress has been made in treating cancers with single-driver gene alterations, the therapeutic implications and tumor microenvironment of double-mutant (DM) NSCLC remain largely unexplored because of its rarity and poor prognosis. This study aims to delineate the landscape of the immune microenvironment within DM NSCLC. METHODS: We employed single-cell RNA sequencing (scRNA-seq) to generate a comprehensive transcriptomic atlas from 25 EGFR mutant NSCLC samples. To assess immune microenvironment changes over time, time-series scRNA-seq and multiplex immunofluorescence analyses were performed in two DM patients. In vitro, EGFR-mutant cell lines were constructed to assess potential therapeutic responses for future clinical applications. RESULTS: The scRNA-seq platform scFocuSCOPE accurately identified and characterized rare, mutation-bearing cancer cells at the single-cell level. DM cancer cells exhibited a strong tendency toward angiogenesis, suggesting an invasive phenotype. DM patients had a more suppressed immune microenvironment, with fewer dysfunctional T lymphocytes. The observation of fewer immune cells and high programmed death ligand-1 (PD-L1) expression in DM cases, probably related to immune evasion and poorer prognosis. In one DM patient, PD-L1 expression remained unchanged after targeted therapy but decreased after immunotherapy. In vitro, EGFR/ERBB2 DM cells showed greater sensitivity to dual-targeted therapies than to single-agent treatments. CONCLUSIONS: scFocuSCOPE precisely delineated tumor heterogeneity and immune suppression in EGFR-DM NSCLC. The complex immune landscape of EGFR-DM tumors offers valuable insights for future mechanistic studies and personalized therapies.
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Single-cell profiling reveals immune aberrations in progressive idiopathic pulmonary fibrosis.
RATIONALE: Changes in peripheral blood cell populations have been observed but not detailed at single-cell resolution in idiopathic pulmonary fibrosis (IPF). OBJECTIVES: To provide an atlas of the changes in the peripheral immune system in stable and progressive IPF. METHODS: Peripheral blood mononuclear cells (PBMCs) from IPF patients and controls were profiled using 10x Chromium 5' single-cell RNA sequencing (scRNA-seq). Flow cytometry was used for validation. Protein concentrations of Regulatory T-cells (Tregs) and Monocytes chemoattractants were measured in plasma and lung homogenates from patients and controls. MEASUREMENTS AND MAIN RESULTS: Thirty-eight PBMC samples from 25 patients with IPF and 13 matched controls yielded 149,564 cells that segregated into 23 subpopulations, corresponding to all expected peripheral blood cell populations. Classical monocytes were increased in progressive and stable IPF compared to controls (32.1%, 25.2%, 17.9%, respectively, p<0.05). Total lymphocytes were decreased in IPF vs controls, and in progressive vs stable IPF (52.6% vs 62.6%, p=0.035). Tregs were increased in progressive IPF (1.8% vs 1.1%, p=0.007), and were associated with decreased survival (P=0.009 in Kaplan-Meier analysis). Flow cytometry analysis confirmed this finding in an independent cohort of IPF patients. Tregs were also increased in two cohorts of lung scRNA-seq. CCL22 and CCL18, ligands for CCR4 and CCR8 Treg chemotaxis receptors, were increased in IPF. CONCLUSIONS: The single-cell atlas of the peripheral immune system in IPF, reveals an outcome-predictive increase in classical monocytes and Tregs, as well as evidence for a lung-blood immune recruitment axis involving CCL7 (for classical monocytes) and CCL18/CCL22 (for Tregs).
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A single-cell mass cytometry-based atlas of the developing mouse brain.
Development of the mammalian brain requires precise molecular changes across diverse cell lineages. While single-cell RNA abundances in the developing brain have been characterized by single-cell RNA sequencing (scRNA-seq), single-cell protein abundances have not been characterized. To address this gap, we performed mass cytometry on the whole brain at embryonic day (E)11.5-E12.5 and the telencephalon, the diencephalon, the mesencephalon and the rhombencephalon at E13.5-postnatal day (P)4 from C57/BL6 mice. Using a 40-antibody panel to analyze 24,290,787 cells from two to four biological replicates per sample, we identify 85 molecularly distinct cell clusters from distinct lineages. Our analyses confirm canonical molecular pathways of neurogenesis and gliogenesis, and predict two distinct trajectories for cortical oligodendrogenesis. Differences in protein versus RNA expression from mass cytometry and scRNA-seq, validated by immunohistochemistry and RNAscope in situ hybridization (ISH), demonstrate the value of protein-level measurements for identifying functional cell states. Our findings show the utility of mass cytometry as a scalable platform for single-cell profiling of brain tissues.
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Systematic mapping of organism-scale gene-regulatory networks in aging using population asynchrony.
In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans "pan-transcriptome"-a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging.