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Transcriptomic profiling of shed cells enables spatial mapping of cellular turnover in human organs.
Single-cell atlases provide valuable insights into gene expression states but lack information on cellular dynamics. Understanding cell turnover rates-the time between a cell's birth and death-can shed light on stemness potential and susceptibility to damage. However, measuring turnover rates in human organs has been a significant challenge. In this study, we integrate transcriptomic data from both tissue and shed cells to assign turnover scores to individual cells, leveraging their expression profiles in spatially resolved expression atlases. By performing RNA sequencing on shed cells from the upper gastrointestinal tract, collected via nasogastric tubes, we infer turnover rates in the human esophagus, stomach, and small intestine. In addition, we analyze colonic fecal washes to map turnover patterns in the human large intestine. Our findings reveal a subset of short-lived, interferon-stimulated colonocytes within a distinct pro-inflammatory microenvironment. Our approach introduces a dynamic dimension to single-cell atlases, offering broad applicability across different organs and diseases.
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The Human and Mouse Enteric Nervous System at Single-Cell Resolution.
The enteric nervous system (ENS) coordinates diverse functions in the intestine but has eluded comprehensive molecular characterization because of the rarity and diversity of cells. Here we develop two methods to profile the ENS of adult mice and humans at single-cell resolution: RAISIN RNA-seq for profiling intact nuclei with ribosome-bound mRNA and MIRACL-seq for label-free enrichment of rare cell types by droplet-based profiling. The 1,187,535 nuclei in our mouse atlas include 5,068 neurons from the ileum and colon, revealing extraordinary neuron diversity. We highlight circadian expression changes in enteric neurons, show that disease-related genes are dysregulated with aging, and identify differences between the ileum and proximal/distal colon. In humans, we profile 436,202 nuclei, recovering 1,445 neurons, and identify conserved and species-specific transcriptional programs and putative neuro-epithelial, neuro-stromal, and neuro-immune interactions. The human ENS expresses risk genes for neuropathic, inflammatory, and extra-intestinal diseases, suggesting neuronal contributions to disease.
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A spatial vascular transcriptomic, proteomic, and phosphoproteomic atlas unveils an angiocrine Tie-Wnt signaling axis in the liver.
Single-cell transcriptomics (scRNA-seq) has revolutionized the understanding of the spatial architecture of tissue structure and function. Advancing the "transcript-centric" view of scRNA-seq analyses is presently restricted by the limited resolution of proteomics and genome-wide techniques to analyze post-translational modifications. Here, by combining spatial cell sorting with transcriptomics and quantitative proteomics/phosphoproteomics, we established the spatially resolved proteome landscape of the liver endothelium, yielding deep mechanistic insight into zonated vascular signaling mechanisms. Phosphorylation of receptor tyrosine kinases was detected preferentially in the central vein area, resulting in an atypical enrichment of tyrosine phosphorylation. Prototypic biological validation identified Tie receptor signaling as a selective and specific regulator of vascular Wnt activity orchestrating angiocrine signaling, thereby controlling hepatocyte function during liver regeneration. Taken together, the study has yielded fundamental insight into the spatial organization of liver endothelial cell signaling. Spatial sorting may be employed as a universally adaptable strategy for multiomic analyses of scRNA-seq-defined cellular (sub)-populations.
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Integrative single-cell and bulk RNA sequencing unravels the role of ACTN1 in promoting lung cancer with brain metastasis and epidermal growth factor receptor-tyrosine kinase inhibitor resistance.
BACKGROUND: Brain metastasis (BM) remains a severe and fatal complication in patients with lung cancer (LC), presenting a major therapeutic challenge. Although epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) have emerged as a cornerstone of targeted therapy, their clinical efficacy is often limited by the inevitable development of drug resistance. METHODS: We initially constructed a general atlas of the tumor microenvironment (TME) in LCBM lesions by integrating single-cell RNA sequencing (scRNA-seq) data. The sensitivity of each cell cluster to EGFR-TKIs was assessed by the "Beyondcell" method. By performing high-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA), we identified hub genes within an EGFR-TKI resistance-associated cell cluster. Finally, the functional role of the most promising candidate, ACTN1, was further investigated in a constructed osimertinib-resistant LC cell line. RESULTS: We identified a malignant and therapy-resistant ACTN1+ epithelial cell subcluster. Both signaling and functional enrichment analyses demonstrated marked activation of PI3K-Akt and IL-17 signaling pathways in ACTN1-high patient subgroups. Finally, we applied machine learning methods to the ACTN1-related genes to select prognostic factors. In vitro experiments confirmed the pro-resistance and pro-metastatic functions of ACTN1 in osimertinib-resistant LC cells. CONCLUSION: ACTN1 was discovered to induce malignant progression and formation of EGFR-TKI resistance. Targeting ACTN1-related pathways may provide novel insights to treat LCBM and overcome intracranial EGFR-TKI resistance.
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Comprehensive analysis of single-cell and bulk RNA-sequencing data identifies B cell marker genes signature that predicts prognosis and analysis of immune checkpoints expression in head and neck squamous cell carcinoma.
Recent studies have shown that B cells and the associated tertiary lymphoid structures (TLS) correlate with the response of patients to immune checkpoint inhibitors (ICIs) and predict overall survival (OS) in cancer patients. We screened 145 B cell marker genes (BCMG) by a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of head and neck squamous cell carcinoma (HNSC) from the Gene Expression Omnibus (GEO) database. The BCMG signature (BCMGS) was established using The Cancer Genome Atlas (TCGA) dataset of HNSC and verified in four independent datasets. The multivariate Cox regression analysis identified the signature as an independent prognostic factor. A prognostic nomogram was constructed with independent prognostic factors using the TCGA dataset. GO and KEGG analysis revealed the underlying signaling pathways related to this signature. Study of immune profiles showed that patients in the low-risk group presented discriminative immune-cell infiltrations. Furthermore, the low-risk group was featured by higher TCR and BCR diversity, which suggested that low-risk patients may be more sensitive to ICIs. Immunohistochemistry was performed, and we found that high expression of FTH1 was significantly correlated with poor OS (P = 0.025). The expression of TIM-3, LAG-3 and PD-1 was positively correlated and associated with better OS in HNSC. However, there was no statistically significant difference between PD-L1, PD-L2, CTLA-4, TIGIT and prognosis. The BCMGS was a promising prognostic biomarker in HNSC, which may help to interpret the responses to immunotherapy and provide a new perspective for future research on the treatment in HNSC.
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Benchmarking computational decontamination of ambient RNA.
Gene expression profiling of single cells using single-cell and single-nucleus RNA sequencing (sxRNA-seq) enables researchers to characterize cellular heterogeneity and unraveling complex biological processes at unprecedented resolution. However, sxRNA-seq faces challenges due to the presence of ambient RNA, extraneous RNA molecules not originating from the cells of interest. Sample preparation is a major source of ambient RNA, where harsh conditions can lead to cell lysis and the release of intracellular RNA. This inescapable inclusion of ambient RNA can cause erroneous results and hinder downstream analyses. To address this issue, various methodologies have been developed to identify, quantify, and remove ambient RNA. Here, we rigorously evaluate 7 state-of-the-art methodologies for ambient RNA removal using simulated datasets, species-mixing experiments of varying complexities, and genotype-mixing experiments. We find that no single method performs the best across all datasets and metrics, but CellBender, DecontX and SoupX generally perform well.
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A single cell transcriptional atlas of early synovial joint development.
Synovial joint development begins with the formation of the interzone, a region of condensed mesenchymal cells at the site of the prospective joint. Recently, lineage-tracing strategies have revealed that Gdf5-lineage cells native to and from outside the interzone contribute to most, if not all, of the major joint components. However, there is limited knowledge of the specific transcriptional and signaling programs that regulate interzone formation and fate diversification of synovial joint constituents. To address this, we have performed single cell RNA-Seq analysis of 7329 synovial joint progenitor cells from the developing murine knee joint from E12.5 to E15.5. By using a combination of computational analytics, in situ hybridization and in vitro characterization of prospectively isolated populations, we have identified the transcriptional profiles of the major developmental paths for joint progenitors. Our freely available single cell transcriptional atlas will serve as a resource for the community to uncover transcriptional programs and cell interactions that regulate synovial joint development.
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Exploring the molecular mechanisms of Weifuchun in the treatment of chronic atrophic gastritis based on single-cell sequencing and machine learning.
ETHNOPHARMACOLOGICAL RELEVANCE: Chronic atrophic gastritis (CAG) is a common precancerous lesion of gastric cancer, for which there is currently a lack of effective drugs to reverse its pathological progression. Weifuchun (WFC), as a classical traditional Chinese medicine (TCM) compound, has demonstrated definitive clinical efficacy in treating CAG, although its molecular mechanisms remain unclear. AIM OF THE STUDY: To explore the potential molecular mechanisms of WFC in the treatment of CAG. MATERIALS AND METHODS: This study conducted single-cell RNA sequencing (scRNA-seq) on gastric mucosal tissues from patients with chronic superficial gastritis (CSG) and CAG, mapping gastric mucosal cell populations and screening differentially expressed genes. The CAG transcriptome data from six integrated databases from Gene Expression Omnibus (GEO) were combined with the databases from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and relevant literature to identify active components and potential targets of WFC, constructing a "active components-cell populations-intersected target genes" regulatory network. Eight machine learning models (including random forest) were employed to predict the efficacy of key target genes. Finally, a cell model was established using MNNG-induced GES-1 cells, with potential target protein expression validated by Western blot. RESULTS: Single-cell sequencing identified 17 cell types, with epithelial cells constituting the highest proportion (86.96%). In the CAG group, T/NK cells, plasma cells, and fibroblasts were increased as abserved, while goblet cells and enterocytes appeared exclusively in CAG samples. Pseudotime analysis revealed a preferential polarization of mucosal-associated invariant T (MAIT) cells toward a Th17- like phenotype in CAG, accompanied by a decline in IFNG+ effector CD8+ T cells. Cellular interaction analysis highlighted fibroblasts, macrophages, and dendritic cells as central hubs, with key ligand-receptor pairs (e.g., CCL5/CCR5, ADM/RAMP3) implicated in pro-inflammatory crosstalk. Thirteen active components of WFC were identified, and their target genes overlapped with CAG differentially expressed genes from single-cell sequencing, yielding five common targets (PDE4D, JUN, KCNH2, NFKBIA, MMP12). Further intersection with GEO- derived CAG DEGs identified JUN and MMP12 as potential targets. Machine learning demonstrated that the random forest model exhibited optimal classification performance (AUC = 1.000), with JUN being the candidate feature (single-gene discriminant accuracy approaching 95%). JUN, NFKBIA, and MMP12 formed the potential feature set. In vitro experiments showed that WFC concentration-dependently inhibited the proliferation of MNNG-induced GES-1 cells and significantly downregulated MMP12 protein expression (P < 0.05), while no significant effects were observed on JUN, NFKBIA, PDE4D, or KCNH2 protein levels. CONCLUSION: This study revealed the cellular atlas, immune microenvironment shifts, and potential target genes of WFC in treating CAG, demonstrating that WFC may delay CAG progression by downregulating MMP12 expression. Machine learning identified JUN as the candidate predictive gene. This research provides a preliminary approach for precision mechanism studies of traditional Chinese medicine formulations, encompassing "single-cell atlas - multi-source data integration - machine learning prediction - experimental validation".
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Identification and validation of biomarkers for epithelial-mesenchymal transition-related cells to estimate the prognosis and immune microenvironment in primary gastric cancer by the integrated analysis of single-cell and bulk RNA sequencing data.
BACKGROUND: The epithelial-mesenchymal transition (EMT) is associated with gastric cancer (GC) progression and immune microenvironment. To better understand the heterogeneity underlying EMT, we integrated single-cell RNA-sequencing (scRNA-seq) data and bulk sequencing data from GC patients to evaluate the prognostic utility of biomarkers for EMT-related cells (ERCs), namely, cancer-associated fibroblasts (CAFs) and epithelial cells (ECs). METHODS: scRNA-seq data from primary GC tumor samples were obtained from the Gene Expression Omnibus (GEO) database to identify ERC marker genes. Bulk GC datasets from the Cancer Genome Atlas (TCGA) and GEO were used as training and validation sets, respectively. Differentially expressed markers were identified from the TCGA database. Univariate Cox, least-absolute shrinkage, and selection operator regression analyses were performed to identify EMT-related cell-prognostic genes (ERCPGs). Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses were adopted to evaluate the prognostic utility of the ERCPG signature. An ERCPG-based nomogram was constructed by integrating independent prognostic factors. Finally, we evaluated the correlations between the ERCPG signature and immune-cell infiltration and verified the expression of ERCPG prognostic signature genes by in vitro cellular assays. RESULTS: The ERCPG signature was comprised of seven genes (COL4A1, F2R, MMP11, CAV1, VCAN, FKBP10, and APOD). Patients were divided into high- and low-risk groups based on the ERCPG risk scores. Patients in the high-risk group showed a poor prognosis. ROC and calibration curves suggested that the ERCPG signature and nomogram had a good prognostic utility. An immune cell-infiltration analysis suggested that the abnormal expression of ERCPGs induced the formation of an unfavorable tumor immune microenvironment. In vitro cellular assays showed that ERCPGs were more abundantly expressed in GC cell lines compared to normal gastric tissue cell lines. CONCLUSIONS: We constructed and validated an ERCPG signature using scRNA-seq and bulk sequencing data from ERCs of GC patients. Our findings support the estimation of patient prognosis and tumor treatment in future clinical practice.
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Phenotypic variation of transcriptomic cell types in mouse motor cortex.
Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties1,2. Most existing neural taxonomies are based on either transcriptomic3,4 or morpho-electric5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7. Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.