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Results for “single cell RNA sequencing”

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At least 253 records · Page 14Linked to original sources

bioRxiv2022

Determination of expression profiles for Drosophila ovarian Follicle Stem Cells (FSCs) using single-cell RNA sequencing

Drosophila ovarian Follicle Stem Cells (FSC) present an excellent paradigm for understanding how a community of active stem cells maintained by population asymmetry is regulated. Here we describe single-cell RNA sequencing studies of a pre-sorted population of cells that include FSCs and the neighboring cell types, Escort Cells (ECs) and Follicle Cells (FCs), which they support. Cell-type assignment relies on anterior-posterior (AP) location within the germarium. We clarify the previously determined location of FSCs and use spatially targeted lineage studies as further confirmation. The scRNA profiles among four clusters are consistent with an AP progression from anterior ECs through posterior ECs and then FSCs, to early FCs. Several genes with graded profiles from ECs to FCs are highlighted as candidate effectors of the inverse gradients of the two principal signaling pathways, Wnt and JAK-STAT, that guide FSC differentiation and division.

developmental biology↗

bioRxiv2026

MIMIQ: Fast mutual information calculation and significance testing for single-cell RNA sequencing analysis

Mutual information is a fundamental quantity in information theory that describes the non-linear dependency between two variables, and has numerous applications within bioinformatics and beyond. However, its exploitation is hampered by a trade-off between computational intensity and accuracy. Here we present an adaptive binning approach to computing the pairwise mutual information, optimized for small integer counts such as those observed in single-cell RNA sequencing. By assuming a sampling distribution such as the negative binomial, a {chi}2 test statistic for hypothesis testing can be computed simultaneously via a copula transformation. Using these quantities, we show how gene rewiring of CD4+ naive T-cells during SARS-CoV-2 infection can be studied using a single-cell sequencing dataset of healthy and COVID-19 donors.

bioinformatics↗

bioRxiv2022

Dynamic single-cell RNA sequencing reveals BCG vaccination curtails SARS-CoV-2 induced disease severity and lung inflammation

COVID-19 continues to exact a toll on human health despite the availability of several vaccines. Bacillus Calmette Guerin (BCG) has been shown to confer heterologous immune protection against viral infections including COVID-19 and has been proposed as vaccine against SARS-CoV-2 (SCV2). Here we tested intravenous BCG vaccination against COVID-19 using the golden Syrian hamster model together with immune profiling and single cell RNA sequencing (scRNAseq). We observed that BCG reduced both lung SCV2 viral load and bronchopneumonia. This was accompanied by an increase in lung alveolar macrophages, a reversal of SCV2-mediated T cell lymphopenia, and reduced lung granulocytes. Single cell transcriptome profiling showed that BCG uniquely recruits immunoglobulin-producing plasma cells to the lung suggesting accelerated antibody production. BCG vaccination also recruited elevated levels of Th1, Th17, Treg, CTLs, and Tmem cells, and differentially expressed gene (DEG) analysis showed a transcriptional shift away from exhaustion markers and towards antigen presentation and repair. Similarly, BCG enhanced lung recruitment of alveolar macrophages and reduced key interstitial macrophage subsets, with both cell-types also showing reduced IFN-associated gene expression. Our observations indicate that BCG vaccination protects against SCV2 immunopathology by promoting early lung immunoglobulin production and immunotolerizing transcriptional patterns among key myeloid and lymphoid populations.

immunology↗

bioRxiv2021

Length Biases in Single-Cell RNA Sequencing of pre-mRNA

Single-molecule pre-mRNA and mRNA sequencing data can be modeled and analyzed using the Markov chain formalism to yield genome-wide insights into transcription. However, quantitative inference with such data requires careful assessment and understanding of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data, and explore the mechanistic implications. A biological explanation for this phenomenon within our modeling framework requires unrealistic transcriptional parameters, leading us to posit a length-based model of capture bias. We provide solutions for this model, and use them to find concordant and mechanistically plausible parameter trends across data from multiple single-cell RNA-seq experiments in several species.

biophysics↗

bioRxiv2022

Porcine intestinal innate lymphoid cells and lymphocyte spatial context revealed through single-cell RNA sequencing

Intestinal lymphocytes are crucial members of the mucosal immune system with impact over outcomes of intestinal health versus dysbiosis. Resolving intestinal lymphocyte complexity and function is a challenge, as the intestine provides cellular snapshots of a diverse spectrum of immune states. In pigs, intestinal lymphocytes are poorly described relative to humans or traditional model species. Enhanced understanding of porcine intestinal lymphocytes will promote food security and improve utility of pigs as a biomedical model for intestinal research. Single-cell RNA sequencing (scRNA-seq) was performed to provide transcriptomic profiles of lymphocytes in porcine ileum, with 31,983 cells annotated into 26 cell types. Deeper interrogation revealed previously undescribed cells in porcine intestine, including SELLhi{gamma}{delta} T cells, group 1 and group 3 innate lymphoid cells (ILCs), and four subsets of B cells. Single-cell transcriptomes in ileum were compared to those in porcine blood, and subsets of activated lymphocytes were detected in ileum but not periphery. Comparison to scRNA-seq human and murine ileum data revealed a general consensus of ileal lymphocytes across species. Lymphocyte spatial context in porcine ileum was conferred through differential tissue dissection prior to scRNA-seq. Antibody-secreting cells, B cells, follicular CD4 {beta} T cells, and cycling T/ILCs were enriched in ileum with Peyers patches, while non-cycling{gamma}{delta} T, CD8 {beta} T, and group 1 ILCs were enriched in ileum without Peyers patches. scRNA-seq findings were leveraged to develop advanced toolsets for further identification of ILCs in porcine ileum via flow cytometry and in situ staining. Porcine ileal ILCs identified via scRNA-seq did not transcriptionally mirror peripheral porcine ILCs (corresponding to natural killer cells) but instead had gene signatures indicative of tissue- and activation-specific functions, indicating potentially similar roles to intestinal ILCs identified in humans. Overall, the data serve as a highly-resolved transcriptomic atlas of the porcine intestinal immune landscape and will be useful in further understanding intestinal immune cell function.

immunology↗

bioRxiv2025

Long-read single-cell RNA sequencing uncovers cell-type specific transcript regulation in COVID-19

SARS-CoV-2 infection leads to extensive host transcriptomic changes, but the role of alternative splicing in shaping the immune response remains underexplored. Here, we present the first application of long-read single-cell RNA sequencing on nasopharyngeal swabs from COVID-19 patients and healthy controls to resolve transcript-level changes across cell types. Our analysis identified major epithelial cell types and pronounced immune infiltration, with cell-type annotations concordant with those from short-read data. By enabling isoform-level resolution, our nanopore sequencing approach revealed cell-type specific alternative splicing, undetectable with short-read sequencing. For example, although gene-level expression of the key immune and apoptosis regulators, IFNAR2 and FAIM, did not differ between COVID-19 patients and healthy controls we identified marked shifts in isoform usage. Between moderate and critical cases, we observed cell-type specific differential transcript usage in the T cell signaling kinase FYN and the immune-regulatory transcription factor IRF2. As some of these splicing alterations yield functionally distinct isoforms, we hypothesize that alternative splicing modulates immune signaling and apoptosis, fine-tuning the host response to SARS-CoV-2 infection. Our study demonstrates the unique power of long-read single-cell transcriptomics to uncover isoform-resolved regulatory changes, offering novel insights into the role of alternative splicing in shaping immune responses to viral infections.

genomics↗

bioRxiv2017

Single Cell RNA Sequencing of stem cell-derived retinal ganglion cells.

We used human embryonic stem cell-derived retinal ganglion cells (RGCs) to characterize the transcriptome of 1,174 cells at the single cell level. The human embryonic stem cell line BRN3B-mCherry A81-H7 was differentiated to RGCs using a guided differentiation approach. Cells were harvested at day 36 and subsequently prepared for single cell RNA sequencing. Our data indicates the presence of three distinct subpopulations of cells, with various degrees of maturity. One cluster of 288 cells upregulated genes involved in axon guidance together with semaphorin interactions, cell-extracellular matrix interactions and ECM proteoglycans, suggestive of a more mature phenotype.

genomics↗

bioRxiv2018

Single-cell RNA sequencing reveals a dynamic stromal niche within the evolving tumour microenvironment

Non-cancerous stromal cells represent a highly diverse compartment of the tumour, yet their role across tumour evolution remains unclear. We employed single-cell RNA sequencing to determine stromal adaptations in murine melanoma at different points of tumour development. Naive lymphocytes recruited from lymph nodes underwent activation and clonal expansion within the tumour, prior to PD1 and Lag3 expression, while tumour-associated myeloid cells promoted the formation of a suppressive niche through cytokine secretion and inhibitory T cell interactions. We identified three temporally distinct cancer-associated fibroblast (CAF) populations displaying unique signatures, and verified these in human datasets. In early tumours, immune CXCL12/CSF1 and complement-expressing CAFs supported recruitment of macrophages, whereas contractile CAFs became more prevalent in later tumours. This study highlights the complex interplay and increasing diversity among cells that co-evolve with the tumour, indicating that from early stages of development, stromal cells acquire the capacity to modulate the immune landscape towards suppression.

cancer biology↗

bioRxiv2025

Single-cell RNA sequencing and large-scale bulk combination with machine learning reveal gastric cancer-related macrophage heterogeneity

BackgroundThe tumor microenvironment (TME) significantly impacts cancer progression and overall patient survival. However, the complexity of tumor cell-TME interactions in gastric cancer (GC) and their underlying molecular basis remain to be systematically elucidated. MethodsWe performed single-cell RNA sequencing (scRNA-seq) on paired tumor and adjacent tissues from 3 treatment-naive GC patients. We conducted an in-depth characterization of the cellular composition and molecular features of the GC TME, with a particular focus on tumor-associated macrophages (TAMs) subsets and their mediated intercellular communication. Investigating the effect of C1q on the polarisation of THP-1 induced macrophages through migration and invasion experiments ResultsTranscriptomic analysis of 56,151 single cells identified a key TAM subset - C1QA{square} TAMs. This subset is characterized by high expression of genes including C1QA, C1QB, C1QC and FN1, exhibiting a distinct M2-like macrophage phenotype. We developed a LASSO-based predictive model, C1Q-LASSO, to accurately stratify patients based on survival outcomes and chemotherapy responses, independently of established prognostic parameters. In vitro functional experiments further confirmed that C1q directly promotes malignant phenotypes in GC cells. Additionally, C1q significantly enhances the malignant progression of gastric cancer induced by M2 macrophages. ConclusionsUtilizing scRNA-seq technology, this study systematically delineated the heterogeneous landscape of the GC immune microenvironment and its complex cellular interaction network at single-cell resolution. We first identified and functionally validated the C1QA{square} TAM subset, demonstrating its association with poor prognosis and pro-tumorigenic functions.

cancer biology↗

bioRxiv2020

Model-based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a novel statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAEs efficacy in integrative analyses of multi-omic datasets with continuous cell population structures.

genetics↗

bioRxiv2024

Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing

Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.

genomics↗

bioRxiv2026

Beyond the gene: contextualizing novel isoforms through pathway analysis in long-read single-cell RNA sequencing

Differential isoform usage has been reportedly associated with distinct contexts associated with cell-type and disease; however, the mechanistic role that individual isoforms play in these contexts is poorly understood. Long-read single-cell RNA sequencing enables greater resolution into genes and isoforms previously difficult to detect via short-read approaches, including pseudogenes and those with repetitive motifs. This technique, combined with isoform-discovery tools such as Bambu, has yielded thousands of newly discovered isoforms and genes for consideration. The functional relevance of these new isoforms, however, remains enigmatic. Tools to prioritize which of these novel isoforms should be interrogated and to predict their functionality remain elusive. To address this, we developed a computational framework, Centroid-based Isoform Pathway Inference (CIPI), which contextualizes novel genes and isoforms relative to reference genes with similar cell-type expression patterns. Rather than relying solely on host-gene annotation or similarity to known protein domains, CIPI constructs expression-based neighborhoods that, combined with pathway analyses such as GO, KEGG, or Reactome, enable inference of pathway-level contextualization of individual isoforms according to their biologically meaningful neighbors. Combining CIPI with pathway analyses provides a biological context to novel isoforms, enabling a hypothesis-generating approach for prioritizing novel isoforms across the isonome.

genomics↗

bioRxiv2021

Single cell RNA sequencing of the Strongylocentrotus purpuratus larva reveals the blueprint of major cell types and nervous system of a non-chordate deuterostome

Identifying the molecular fingerprint of organismal cell types is key for understanding their function and evolution. Here, we use single cell RNA sequencing (scRNA-seq) to survey the cell types of the sea urchin early pluteus larva, representing an important developmental transition from non-feeding to feeding larva. We identify 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these reveal a highly detailed portrait of cell diversity across the larva, including the identification of neuronal cell types. We then validate important gene regulatory networks driving sea urchin development and reveal new domains of activity within the larval body. Focusing on neurons that co-express Pdx-1 and Brn1/2/4, we identify an unprecedented number of genes shared by this population of neurons in sea urchin and vertebrate endocrine pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we show that Pdx1 is necessary for the acquisition of the neuronal identity of these cells. We hypothesize that a network similar to the one orchestrated by Pdx1 in the sea urchin neurons was active in an ancestral cell type and then inherited by neuronal and pancreatic developmental lineages in sea urchins and vertebrates.

developmental biology↗

bioRxiv2023

Single-cell RNA sequencing identifies response of renal lymphatic endothelial cells to acute kidney injury

The inflammatory response to acute kidney injury (AKI) likely dictates future renal health. Lymphatic vessels are responsible for maintaining tissue homeostasis through transport and immunomodulatory roles. Due to the relative sparsity of lymphatic endothelial cells (LECs) in the kidney, past sequencing efforts have not characterized these cells and their response to AKI. Here we characterized murine renal LEC subpopulations by single-cell RNA sequencing and investigated their changes in cisplatin AKI. We validated our findings by qPCR in LECs isolated from both cisplatin-injured and ischemia reperfusion injury, by immunofluorescence, and confirmation in in vitro human LECs. We have identified renal LECs and their lymphatic vascular roles that have yet to be characterized in previous studies. We report unique gene changes mapped across control and cisplatin injured conditions. Following AKI, renal LECs alter genes involved endothelial cell apoptosis and vasculogenic processes as well as immunoregulatory signaling and metabolism. Differences between injury models are also identified with renal LECs further demonstrating changed gene expression between cisplatin and ischemia reperfusion injury models, indicating the renal LEC response is both specific to where they lie in the lymphatic vasculature and the renal injury type. How LECs respond to AKI may therefore be key in regulating future kidney disease progression.

pathology↗

bioRxiv2023

Single-cell RNA sequencing reveals cellular and molecular heterogeneity in fibrocartilaginous enthesis formation

The attachment site of the rotator cuff (RC) is a classic fibrocartilaginous enthesis, which is the junction between bone and tendon with typical characteristics of a fibrocartilage transition zone. Enthesis development has historically been studied with lineage tracing of individual genes selected a priori, which does not allow for the determination of single-cell landscapes yielding mature cell types and tissues. Here, we applied Single-cell RNA sequencing (scRNA-seq) to delineate the comprehensive postnatal RC enthesis growth and the temporal atlas from as early as embryonic day 15 up to postnatal week 4. In summary, we compared the development pattern between enthesis and tendon or articular cartilage, then deciphered the cellular heterogeneity and the molecular dynamics during fibrocartilage differentiation. This data provides a transcriptional resource that will support future investigations of enthesis development at the mechanistic level and may shed light on the strategies for enhanced RC healing outcomes.

developmental biology↗

bioRxiv2024

Characterization of Cancer Stem Cells in Laryngeal Squamous Cell Carcinoma by Single-Cell RNA Sequencing

Cancer stem cells (CSCs) constitute a pivotal element within the tumor microenvironment (TME), driving the initiation and progression of cancer. However, the identification of CSCs and their underlying molecular mechanisms in laryngeal squamous cell carcinoma (LSCC) remains a formidable challenge. We employed single-cell RNA sequencing of matched primary tumor tissues, paracancerous tissues, and local lymph nodes from three LSCC patients. Two distinct clusters of stem cells originating from epithelial populations were delineated and verified as CSCs and normal stem cells (NSCs) respectively. CSCs were abundant in the paracancerous tissues compared to the tumor tissues. CSCs showed high expression of stem cell marker genes such as PROM1, ALDH1A1, and SOX4, and increased activity of tumor-related hypoxia, Wnt/{beta}-catenin, and notch signaling pathways. We then explored the intricate crosstalk between CSCs and the TME cells and identified targets within the TME that related with CSCs. We also find eight marker genes of CSCs that correlated significantly with the prognosis of LSCC patients. Furthermore, bioinformatics analyses showed that drugs such as erlotinib, OSI-027, and ibrutinib selectively targeted the CSC-specifically expressed genes. In conclusion, our results represent the first comprehensive characterization of CSCs properties in LSCC at the single-cell level.

bioinformatics↗

bioRxiv2020

Single-Cell RNA Sequencing of Batch Chlamydomonas Cultures Reveals Heterogeneity in their Diurnal Cycle Phase

The photosynthetic unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) is a versatile reference for algal biology because of the facility with which it can be cultured in the laboratory. Genomic and systems biology approaches have previously been used to describe how the transcriptome responds to environmental changes, but this analysis has been limited to bulk data, representing the average behavior from pools of cells. Here, we apply single-cell RNA sequencing (scRNA-seq) to probe the heterogeneity of Chlamydomonas cell populations under three environments and in two genotypes differing in the presence of a cell wall. First, we determined that RNA can be extracted from single algal cells with or without a cell wall, offering the possibility to sample algae communities in the wild. Second, scRNA-seq successfully separated single cells into non-overlapping cell clusters according to their growth conditions. Cells exposed to iron or nitrogen deficiency were easily distinguished despite a shared tendency to arrest cell division to economize resources. Notably, these groups of cells recapitulated known patterns observed with bulk RNA-seq, but also revealed their inherent heterogeneity. A substantial source of variation between cells originated from their endogenous diurnal phase, although cultures were grown in constant light. We exploited this result to show that circadian iron responses may be conserved from algae to land plants. We propose that bulk RNA-seq data represent an average of varied cell states that hides underappreciated heterogeneity. One-sentence summaryWe show that single-cell RNA-seq (scRNA-seq) can be applied to Chlamydomonas cultures to reveal the that heterogenity in bulk cultures is largely driven by diurnal cycle phases The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Matteo Pellegrini (matteop@mcdb.ucla.edu)

genomics↗

bioRxiv2025

The immune landscape of pediatric Brain and Solid Tumors: A systematic review and integration of immunohistochemistry and single-cell RNA sequencing data

BackgroundImmunotherapies achieved remarkable success in adult cancers, yet their efficacy in pediatric brain and solid tumors remains limited. Insights into the unique immune landscape of pediatric tumors are crucial to improve immunotherapies for pediatric patients. MethodsWe performed a systematic search for studies reporting immunohistochemistry (IHC), single-cell- or single-nucleus RNA-sequencing (sc/snSeq) data on the immune landscape of pediatric tumors. For IHC studies, data on macrophages, T cells, T helper cells, regulatory T cells, NK cells and B cells were extracted. For sc/snSeq studies, cell cluster counts were extracted. 47 IHC studies and 26 sc/snSeq studies were included in the analysis. ResultsOur integrative in-depth analysis of 73 studies covered 35 unique pediatric tumor types with 17 tumor types analyzed by IHC, 4 by sc/snSeq, and 14 by both techniques. Regardless of variability in analysis methods, both IHC and sc/snSeq showed that peripheral nerve tumors and soft tissue sarcomas had relatively immune-infiltrated, T cell-rich tumor microenvironments (TME). Brain tumors exhibited a macrophage/microglia-rich, NK cell- infiltrated and T cell-depleted TME. Sc/snSeq data confirmed these observations, showing a macrophage/microglia-rich brain TME. Compared to adult tumors, (CD8+) T cell infiltration and macrophage infiltration was low for all pediatric tumor types. Integrated IHC and sc/snSeq data were visualized in interactive heatmaps, publicly available on R2 as a comprehensive atlas [https://hgserver1.amc.nl/cgi-bin/r2/main.cgi?option=imi2_targetmap_v1; map Immune_landscape_mm2(by patient)_v4]. ConclusionWe provide a comprehensive, integrated overview of the immune landscape of pediatric brain and solid tumors. These insights can aid the development and selection of immunotherapeutic strategies for specific pediatric cancers, tailored to their unique immune characteristics.

immunology↗