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Single-cell RNA sequencing of freshly isolated bovine milk cells and cultured primary mammary epithelial cells.
Bovine mammary function at molecular level is often studied using mammary tissue or primary bovine mammary epithelial cells (pbMECs). However, bulk tissue and primary cells are heterogeneous with respect to cell populations, adding further transcriptional variation in addition to genetic background. Thus, understanding of the variation in gene expression profiles of cell populations and their effect on function are limited. To investigate the mononuclear cell composition in bovine milk, we analyzed a single-cell suspension from a milk sample. Additionally, we harvested cultured pbMECs to characterize gene expression in a homogeneous cell population. Using the Drop-seq technology, we generated single-cell RNA datasets of somatic milk cells and pbMECs. The final datasets after quality control filtering contained 7,119 and 10,549 cells, respectively. The pbMECs formed 14 indefinite clusters displaying intrapopulation heterogeneity, whereas the milk cells formed 14 more distinct clusters. Our datasets constitute a molecular cell atlas that provides a basis for future studies of milk cell composition and gene expression, and could serve as reference datasets for milk cell analysis.
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Identification of Prognostic Biomarkers Originating From the Tumor Stroma of Betel Quid-Associated Oral Cancer Tissues.
BACKGROUND: Partial epithelial-mesenchymal transition (p-EMT) is a distinct clinicopathological feature prevalent in oral cavity tumors of The Cancer Genome Atlas. Located at the invasion front, p-EMT cells require additional support from the tumor stroma for collective cell migration, including track clearing, extracellular matrix remodeling and immune evasion. The pathological roles of otherwise nonmalignant cancer-associated fibroblasts (CAFs) in cancer progression are emerging. METHODS: Gene set enrichment analysis was used to reveal differentially enriched genes and molecular pathways in OC3 and TW2.6 xenograft tissues, representing mesenchymal and p-EMT tumors, respectively. R packages of genomic data science were executed for statistical evaluations and data visualization. Immunohistochemistry and Alcian blue staining were conducted to validate the bioinformatic results. Univariate and multivariate Cox proportional hazards models were performed to identify covariates significantly associated with overall survival in clinical datasets. Kaplan-Meier curves of estimated overall survival were compared for statistical difference using the log-rank test. RESULTS: Compared to mesenchymal OC3 cells, tumor stroma derived from p-EMT TW2.6 cells was significantly enriched in microvessel density, tumor-excluded macrophages, inflammatory CAFs, and extracellular hyaluronan deposition. By translating these results to clinical transcriptomic datasets of oral cancer specimens, including the Puram single-cell RNA-seq cohort comprising ~6000 cells, we identified the expression of stromal TGFBI and HYAL1 as independent poor and protective biomarkers, respectively, for 40 Taiwanese oral cancer tissues that were all derived from betel quid users. In The Cancer Genome Atlas, TGFBI was a poor marker not only for head and neck cancer but also for additional six cancer types and HYAL1 was a good indicator for four tumor cohorts, suggesting common stromal effects existing in different cancer types. CONCLUSIONS: As the tumor stroma coevolves with cancer progression, the cellular origins of molecular markers identified from conventional whole tissue mRNA-based analyses should be cautiously interpreted. By incorporating disease-matched xenograft tissue and single-cell RNA-seq results, we suggested that TGFBI and HYAL1, primarily expressed by stromal CAFs and endothelial cells, respectively, could serve as robust prognostic biomarkers for oral cancer control.
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Single-nucleus RNA sequencing revealed ribosomal genes expression defines the cellular state of oligodendrocytes and precursor cells in post-mortem ischemia mouse brains.
Accurate cell atlas analysis using single-nucleus RNA-seq (snRNA-seq) is highly influenced by RNA quality, with post-mortem interval (PMI) being a major factor. Thus, deciphering the non-cell-autonomous effects induced by PMI is critical for understanding the cellular and molecular disruptions it triggers. Here, we investigated the impact of PMI on cellular composition and gene expression using snRNA-seq of post-mortem mouse brain tissues. Samples were allowed to decay at 25 °C for varying durations prior to sequencing, covering a comprehensive range of RNA integrity number (RIN) values. Our results revealed that mRNA is more stable than rRNA during decay. Notably, we observed a preferential and transient depletion of oligodendrocytes and OPCs with increasing PMI. In parallel, a rapid and widespread upregulation of ribosomal protein (RP) genes occurred across various cell types, reaching a plateau at approximately 36 h PMI. Although overt loss of neuronal cell states was not detected, we observed significant upregulation of PMI-dependent RP genes, accompanied by a reduction in subpopulation cells. RP genes exhibited the most prominent differential expression in subpopulations experiencing greater perturbations, suggesting that their dysregulation may be linked to cell death. Through this work, we systematically characterized PMI-induced transcriptomic alterations in brain tissue at single-cell resolution and identified key factors contributing to these changes. Our findings offer new insights into the molecular consequences of postmortem hypoxia-ischemia and provide a valuable reference for transcriptomic analyses of RNA-degraded samples.
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A gene expression atlas of early craniofacial development.
We present a gene expression atlas of early mouse craniofacial development. Laser capture microdissection (LCM) was used to isolate cells from the principal critical microregions, whose development, differentiation and signaling interactions are responsible for the construction of the mammalian face. At E8.5, as migrating neural crest cells begin to exit the neural fold/epidermal ectoderm boundary, we examined the cranial mesenchyme, composed of mixed neural crest and paraxial mesoderm cells, as well as cells from adjacent neuroepithelium. At E9.5 cells from the cranial mesenchyme, overlying olfactory placode/epidermal ectoderm, and underlying neuroepithelium, as well as the emerging mandibular and maxillary arches were sampled. At E10.5, as the facial prominences form, cells from the medial and lateral prominences, the olfactory pit, multiple discrete regions of underlying neuroepithelium, the mandibular and maxillary arches, including both their mesenchymal and ectodermal components, as well as Rathke's pouch, were similarly sampled and profiled using both microarray and RNA-seq technologies. Further, we performed single cell studies to better define the gene expression states of the early E8.5 pioneer neural crest cells and paraxial mesoderm. Taken together, and analyzable by a variety of biological network approaches, these data provide a complementing and cross validating resource capable of fueling discovery of novel compartment specific markers and signatures whose combinatorial interactions of transcription factors and growth factors/receptors are responsible for providing the master genetic blueprint for craniofacial development.
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Integrated analysis and systematic characterization of the regulatory network for human germline development.
Primordial germ cells (PGCs) are the precursors of germline that are specified at the embryonic stage. Recent studies reveal that humans employ different mechanisms for PGC specification compared with model organisms such as mice. Moreover, the specific regulatory machinery remains largely unexplored, mainly due to the inaccessible nature of this complex biological process in humans. Here, we curate and integrate multi-omics data, including 581 RNA-seq, 54 ATAC-seq, 45 ChIP-seq, and 69 single-cell RNA-seq samples from different stages of human PGC development to recapitulate the precisely controlled and stepwise process, presenting an atlas in the human PGC database (hPGCdb). With these uniformly processed data and integrated analyses, we characterize the potential key transcription factors and regulatory networks governing human germ cell fate. We validate the important roles of some of the key factors in germ cell development by CRISPRi knockdown. We also identify the soma-germline interaction network and discover the involvement of SDC2 and LAMA4 for PGC development, as well as soma-derived NOTCH2 signaling for germ cell differentiation. Taken together, we have built a database for human PGCs (http://43.131.248.15:6882) and demonstrate that hPGCdb enables the identification of the missing pieces of mechanisms governing germline development, including both intrinsic and extrinsic regulatory programs.
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Microbial single-cell RNA sequencing by split-pool barcoding.
Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.
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Prognostic value of autophagy-related genes based on single-cell RNA-sequencing in colorectal cancer.
Background: Colorectal cancer (CRC) is the second most common cancer in China. Autophagy plays an important role in the initiation and development of CRC. Here, we assessed the prognostic value and potential functions of autophagy-related genes (ARGs) using integrated analysis using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA). Methods: We analyzed GEO-scRNA-seq data from GEO using various single-cell technologies, including cell clustering, and identification of differentially expressed genes (DEGs) in different cell types. Additionally, we performed gene set variation analysis (GSVA). The differentially expressed ARGs among different cell types and those between CRC and normal tissues were identified using TCGA-RNA-seq data, and the hub ARGs were screened. Finally, a prognostic model based on the hub ARGs was constructed and validated, and patients with CRC in TCGA datasets were divided into high- and low-risk groups based on their risk-score, and immune cells infiltration and drug sensitivity analyses between the two groups were performed. Results: We obtained single-cell expression profiles of 16,270 cells, and clustered them into seven types of cells. GSVA revealed that the DEGs among the seven types of cells were enriched in many signaling pathways associated with cancer development. We screened 55 differentially expressed ARGs, and identified 11 hub ARGs. Our prognostic model revealed that the 11 hub ARGs including CTSB, ITGA6, and S100A8, had a good predictive ability. Moreover, the immune cell infiltrations in CRC tissues were different between the two groups, and the hub ARGs were significantly correlated with the enrichment of immune cell infiltration. The drug sensitivity analysis revealed that the patients in the two risk groups had difference in their response to anti-cancer drugs. Conclusion: We developed a novel prognostic 11-hub ARG risk model, and these hubs may act as potential therapeutic targets for CRC.
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Single-cell and spatial transcriptomic analyses reveal transcriptional cell lineage heterogeneity in extracranial arteriovenous malformation.
BACKGROUND: Extracranial arteriovenous malformations (eAVMs) are rare congenital vascular anomalies consisting of abnormal artery-vein bypass with no intervening capillary network, and can lead to disability and death. The critical genetic determination factors and key transcriptional pathways of the eAVMs genesis process are still unclear. OBJECTIVE: To generate an overview of the molecular information within eAVMs at the single-cell level. METHODS: We performed single-cell RNA sequencing (scRNA-seq) on nine samples of eAVMs receiving a confirmatory histopathologic evaluation from a board-certified dermatopathologist and two nonlesional tissue sample controls. 10x Visium spatial transcriptomics (ST) was performed on one eAVM to spatially localize heterogeneous cells and profile the gene expression dynamics of the cells in their morphological context. The scRNA-seq and ST data were integrated and analyzed to further query for spatially restricted mapping of intrapopulation heterogeneous cells. RESULTS: We identified different cell states of endothelial cells (ECs), perivascular cells and immune cells in eAVMs, uncovered the presence of MAFB+ nidus ECs, characterized mesenchymal activation in ECs, and identified transcriptional variation within perivascular cells and the presence of smooth muscle-like pericytes in eAVMs. Dysregulated cell to cell interactions among ECs, perivascular cells and immune cells that are associated with eAVMs, including those involving MDK, VEGF, ANGPT, SEMA3 and GALECTIN-9 were cataloged. Together, our results depicted the heterogeneity underlying cell function and interaction of eAVMs at a single-cell resolution. CONCLUSION: We present a comprehensive picture of the cell-resolution atlas that describes the transcriptomic heterogeneity underlying cell function and interaction in eAVMs.
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ITGA5 promotes tumor angiogenesis in cervical cancer.
PURPOSE: Integrins are critical to cancer progression. Integrin alpha 5 (ITGA5) is correlated with the prognosis of cervical cancer patients. However, whether ITGA5 plays an active role in cervical cancer progression or not remains unknown. METHODS: ITGA5 protein expression was detected in 155 human cervical cancer tissues by immunohistochemistry. Data from The Cancer Genome Atlas were utilized to identify risk factors for the overall survival of cervical cancer patients and ITGA5-associated differentially expressed genes. Analyses of single-cell RNA-seq based on Gene Expression Omnibus datasets were performed to show the coexpression of ITGA5 and angiogenesis factors. Tube formation assay, 3D spheroid sprout assay, qRT-PCR, Western Blotting, ELISA, and immunofluorescence were conducted to explore the angiogenic function of ITGA5 in vitro and underlying mechanisms. RESULTS: High ITGA5 level was significantly correlated with increased risk in terms of overall survival and advanced disease stage in cervical cancer patients. ITGA5-associated differentially expressed genes linked ITGA5 to angiogenesis, and immunohistochemistry showed a positive correlation between ITGA5 and microvascular density in cervical cancer tissues. Moreover, tumor cells transfected with ITGA5-targeting siRNA decreased ability to promote endothelial tube formation in vitro. ITGA5/VEGFA coexpression was observed in a tumor cell subpopulation and the decreased endothelial angiogenesis by downregulating ITGA5 could be reversed by VEGFA. Bioinformatics analysis highlighted the PI3K-Akt signaling pathway as downstream of ITGA5. Downregulation of ITGA5 in tumor cells significantly decreased p-AKT and VEGFA levels. Fibronectin (FN1) coated cells or transfected with FN1-targeting siRNA showed fibronectin may play a critical role on ITGA5-mediated angiogenesis. CONCLUSION: ITGA5 promotes angiogenesis and possibly be a potential predictive biomarker for poor survival of patients in cervical cancer.
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Harnessing Single-Cell RNA Sequencing to Better Understand How Diseased Cells Behave the Way They Do in Cardiovascular Disease.
The transition of healthy arteries and cardiac valves into dense, cell-rich, calcified, and fibrotic tissues is driven by a complex interplay of both cellular and molecular mechanisms. Specific cell types in these cardiovascular tissues become activated following the exposure to systemic stimuli including circulating lipoproteins or inflammatory mediators. This activation induces multiple cascades of events where changes in cell phenotypes and activation of certain receptors may trigger multiple pathways and specific alterations to the transcriptome. Modifications to the transcriptome and proteome can give rise to pathological cell phenotypes and trigger mechanisms that exacerbate inflammation, proliferation, calcification, and recruitment of resident or distant cells. Accumulating evidence suggests that each cell type involved in vascular and valvular diseases is heterogeneous. Single-cell RNA sequencing is a transforming medical research tool that enables the profiling of the unique fingerprints at single-cell levels. Its applications have allowed the construction of cell atlases including the mammalian heart and tissue vasculature and the discovery of new cell types implicated in cardiovascular disease. Recent advances in single-cell RNA sequencing have facilitated the identification of novel resident cell populations that become activated during disease and has allowed tracing the transition of healthy cells into pathological phenotypes. Furthermore, single-cell RNA sequencing has permitted the characterization of heterogeneous cell subpopulations with unique genetic profiles in healthy and pathological cardiovascular tissues. In this review, we highlight the latest groundbreaking research that has improved our understanding of the pathological mechanisms of atherosclerosis and future directions for calcific aortic valve disease.