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Results for “single cell RNA sequencing”

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bioRxiv2026

Single cell RNA sequencing of D. pseudoobscura testes reveals transcriptional signatures of heteromorphic spermatogenesis

Sperm heteromorphy, the production of multiple sperm morphs with distinct functions, has evolved repeatedly in animals but its developmental and molecular basis remains poorly understood. In the Drosophila obscura group, males produce fertilising eusperm and non-fertilising parasperm, yet when and how these lineages diverge is unclear. Here, we applied single-cell RNA sequencing to whole testes of D. pseudoobscura to resolve cell-type composition, developmental trajectories, and transcriptional differences between sperm morphs. Analysis of [~]6,500 cells across two replicates identified germline and somatic cell populations and reconstructed spermatogenesis from stem cells to elongated spermatids. We identified a bifurcation in the germline trajectory at the early spermatocyte stage, corresponding to eusperm and parasperm lineages, which was maintained through spermiogenesis. Euspermatocytes, destined to generate longer eusperm, exhibited higher transcriptional activity than paraspermatocytes. Differentially expressed genes included structural sperm tail components, chromatin condensation proteins, post-meiotically transcribed genes and gene duplications resulting in paralogues with reciprocal expression patterns. We also identified markers of somatic cyst cells, including head and tail cyst cells, but found no evidence of morph-specific specialisation within the cyst cell lineage. The transcriptional activities of the distinct germline trajectories corresponded with differential expression of orthologues of critical transcriptional regulators known in D. melanogaster. The transcriptional activator TGIF was enriched in euspermatocytes, while the transcriptional repressor kmg was upregulated in paraspermatocytes. GFP-tagged Kmg showed higher abundance and greater chromatin localisation in paraspermatocytes. These findings demonstrate that sperm morph identity is first apparent in early primary spermatocytes and is largely germline intrinsic. We propose that differential transcriptional provisioning and developmental timing underpin sperm length, providing a framework for understanding the evolution and regulation of sperm heteromorphy.

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↗

bioRxiv2025

Single-Cell RNA Sequencing Defines Developmental Progression and Reproductive Transitions of Pneumocystis carinii

Pneumocystis species are host-obligate fungal pathogens that cause severe pneumonia in immunocompromised individuals. Despite their clinical importance, their life cycle remains poorly understood, in part because Pneumocystis depends on the host environment for most nutrients and requires sexual reproduction for survival, which occurs exclusively in vivo. This study presents the first single-cell RNA sequencing (scRNA-seq) atlas of Pneumocystis carinii, generated from isolated organisms recovered from the bronchoalveolar lavage fluid of infected rats to map the life cycle of P. carinii. Transcriptomes from 87,716 cells were analyzed using the 10x Genomics platform, revealing 13 transcriptionally distinct clusters representing key developmental stages, including biosynthetically active trophic forms, mating-competent intermediates, and asci undergoing sporulation. These states were characterized by expression of MAPK signaling components, {beta}-glucan-modifying enzymes, and spore-associated genes, respectively. The scRNA-seq data supports previous evidence that these host-obligate fungi undergo sexual reproduction and provide new insights into the gene expression patterns associated with different life cycle phases. Biomarkers associated with ascus formation identified by scRNA-seq were validated by RT-qPCR, showing decreased expression levels in ascus-depleted populations treated with anidulafungin, a drug that halts ascus formation. More broadly, this approach provides a strategy for studying the full life cycles of fungal pathogens that cannot be continuously cultured. ImportancePneumocystis species (spp.) are clinically significant fungal pathogens that cannot be sustainably cultured in vitro due to their host obligate nature. This longstanding limitation has impeded progress in understanding their life cycle and identifying therapeutic vulnerabilities. Here, we apply scRNA-seq to P. carinii isolated directly from infected rat lungs, generating the first transcriptional map of its developmental progression. Our results define discrete gene expression states associated with trophic growth, mating activation, and ascus formation, and provide transcriptional evidence for a structured life cycle clarifying key developmental transitions and identifying potential regulatory targets for therapeutic intervention. Importantly, this study demonstrates that scRNA-seq can resolve the developmental biology of host-restricted fungal pathogens that cannot be cultured in vitro. This approach offers a generalizable framework for investigating other unculturable or obligate microbial pathogens directly within their native host environments, where traditional experimental tools are limited.

microbiology↗

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

Single-cell RNA sequencing reveals recruitment of the M2-like CCL8high macrophages in Lewis lung carcinoma-bearing mice following hypofractionated radiotherapy

Tumor-associated macrophages (TAMs) are a crucial factor in reprogramming the tumor microenvironment following radiotherapy. The mechanisms underlying this process remain to be elucidated. Here, we seek to investigate the effects of hypofractionated radiotherapy on macrophages dynamics in a subcutaneous Lewis lung carcinoma murine model. Utilizing single-cell RNA sequencing, we identified a distinct M2-like population of macrophages with high Ccl8 expression level post-hypofractionated radiotherapy. Remarkbly, hypofractionated radiotherapy promoted CCL8high macrophages infiltration and reprogrammed them by upregulating immunosuppressive genes and downregulating antigen-presenting genes, leading to an immunosuppressive tumor microenvironment. Further, we demonstrated that hypofractionated radiotherapy amplified the CCL signaling pathway, enhancing the pro-tumorigenic functions of CCL8high macrophages and promoting macrophages recruitment. The combination therapy of hypofractionated radiotherapy with the CCL signal inhibitor Bindarit was effective in reducing M2 macrophages infiltration and extending the duration of local tumor control. This research highlights the potential of targeting TAMs and introduces a novel combination to improve the efficacy of hypofractionated radiotherapy.

immunology↗

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↗

bioRxiv2024

Discovery of cell type classification marker genes from single cell RNA sequencing data using NS-Forest

The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.

bioinformatics↗

bioRxiv2023

Analysis of the heterogeneity and complexity of murine extraorbital lacrimal gland via single-cell RNA sequencing

PurposeThe lacrimal gland is essential for maintaining ocular surface health and avoiding external damage by secreting the aqueous layer of the tear film. However, a healthy lacrimal glands inventory of cell types and heterogeneity remains understudied. MethodsHere, 10X genome-based single-cell RNA sequencing was used to generate an unbiased classification of cellular diversity in the extraorbital lacrimal gland (ELG) of C57BL/6J mice. From 48,077 high-quality cells, an atlas of cell heterogeneity was produced, and cell types were defined by classic marker genes. The possible functions of these cells and the pseudotime trajectories for certain cell populations were analyzed through bioinformatics. In addition, a preliminary analysis of the cell-cell communication network in ELG was performed. ResultsOver 41 subclasses of cells were identified, including 9 kinds of glandular epithelial cells, 7 kinds of fibroblasts, 10 kinds of myeloid-derived immune cells, at least 10 kinds of lymphoid-derived immune cells, 3 kinds of vascular-associated cell subsets, and 2 kinds of Schwann cells. Analysis of cell-cell communication networks revealed that innate lymphoid cells were closely associated with other cells. ConclusionsThis study provides the first comprehensive transcriptome atlas and related database of mouse ELG. This resource can aid in a deeper understanding of lacrimal gland biology and pathophysiology of its related diseases.

bioinformatics↗

bioRxiv2020

Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma

Recent developments in immuno-oncology demonstrate that not only cancer cells, but also features of the tumor microenvironment guide precision medicine. Still, the relationship between tumor and microenvironment remains poorly understood. To overcome this limitation and identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to lung adenocarcinomas. While the highly heterogeneous carcinoma cell transcriptomes reflected histological grade and activity of relevant oncogenic pathways, our analysis revealed two distinct microenvironmental patterns. We identified a prognostically unfavorable group of tumors with a microenvironment composed of cancer-associated myofibroblasts, exhausted CD8+ T cells, proinflammatory monocyte-derived macrophages and plasmacytoid dendritic cells (CEP2 pattern) and a prognostically favorable group characterized by myeloid dendritic cells, anti-inflammatory monocyte-derived macrophages, normal-like myofibroblasts, NK cells and conventional T cells (MAN2C pattern). Our results show that single-cell gene expression profiling allows to identify patient subgroups based on the tumor microenvironment beyond cancer cell-centric profiling.

cancer biology↗

bioRxiv2019

Single-cell RNA sequencing of a European and an African lymphoblastoid cell line

In biomedical research, lymphoblastoid cell lines (LCLs), often established by in vitro infection of resting B cells with Epstein Barr Virus, are commonly used as surrogates for peripheral blood lymphocytes. Genomic and transcriptomic information on LCLs has been used to study the impact of genetic variation on gene expression in humans. Here we present single-cell RNA sequencing (scRNA-seq) data on GM12878 and GM18502--two LCLs derived from the blood of female donors of European and African ancestry, respectively. Cells from three samples (the two LCLs and a 1:1 mixture of the two) were prepared separately using a 10X Genomics Chromium Controller and deeply sequenced. The final dataset contained 7,045 cells from GM12878, 5,189 from GM18502, and 5,820 from the mixture, offering valuable information on single-cell gene expression in highly homogenous cell populations. This dataset is a suitable reference of population differentiation in gene expression at the single-cell level. Data from the mixture provides additional valuable information facilitating the development of statistical methods for data normalization and batch effect correction.

genetics↗

bioRxiv2022

Single-cell RNA sequencing reveals dysregulation of spinal cord cell types in a several spinal muscular atrophy mouse model

Although spinal muscular atrophy (SMA) is a motor neuron disease caused by the loss of survival of motor neuron (SMN) proteins, there is growing evidence that non-neuronal cells play important roles in SMA pathogenesis. However, transcriptome alterations occurring at the single-cell level in SMA spinal cord remain unknown, preventing us from fully comprehending the role of specific cells. Here, we performed single-cell RNA sequencing of the spinal cord of a severe SMA mouse model, and identified ten cell types as well as their differentially expressed genes. Using CellChat, we found that cellular communication between different cell types in the spinal cord of SMA mice was significantly reduced. A dimensionality reduction analysis revealed 29 cell subtypes and their differentially expressed gene. A subpopulation of vascular fibroblasts showed the most significant change in the SMA spinal cord at the single-cell level. This subpopulation was drastically reduced, possibly causing vascular defects and resulting in widespread protein synthesis and energy metabolism reductions in SMA mice. This study reveals for the first time a single-cell atlas of the spinal cord of mice with severe SMA, and sheds new light on the pathogenesis of SMA.

neuroscience↗

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↗

bioRxiv2023

Single-cell RNA sequencing reveals microenvironment context-specific routes for epithelial-mesenchymal transition in pancreas cancer cells

In the PDAC tumor microenvironment, multiple factors initiate the epithelial-mesenchymal transition (EMT) that occurs heterogeneously among transformed ductal cells, but it is unclear if different drivers promote EMT through common or distinct signaling pathways. Here, we use single-cell RNA sequencing (scRNA-seq) to identify the transcriptional basis for EMT in pancreas cancer cells in response to hypoxia or EMT-inducing growth factors. Using clustering and gene set enrichment analysis, we find EMT gene expression patterns that are unique to the hypoxia or growth factor conditions or that are common between them. Among the inferences from the analysis, we find that the FAT1 cell adhesion protein is enriched in epithelial cells and suppresses EMT. Further, the receptor tyrosine kinase AXL is preferentially expressed in hypoxic mesenchymal cells in a manner correlating with YAP nuclear localization, which is suppressed by FAT1 expression. AXL inhibition prevents EMT in response to hypoxia but not growth factors. Relationships between FAT1 or AXL expression with EMT were confirmed through analysis of patient tumor scRNA-seq data. Further exploration of inferences from this unique dataset will reveal additional microenvironment context-specific signaling pathways for EMT that may represent novel drug targets for PDAC combination therapies.

cancer biology↗

bioRxiv2026

Benchmarking Imputation Methods for Single-Cell RNA Sequencing Data Using Peripheral Blood Mononuclear Cells from Acute Myocardial Infarction Patients

Acute myocardial infarction (AMI) remains one of the leading causes of mortality worldwide, and the following post-effects, such as post-AMI inflammation and tissue repair, involve peripheral blood mononuclear cells playing a critical role. The influence of imputation methods in biological data is assessed with respect to high-resolution single-cell RNA sequencing (scRNAseq) data relevant to these cells. Still scRNAseq data often encounter a lot of dropout events, leading to sparse and noisy datasets, hampering downstream results. To assess the influence of the missingness in the data, we artificially impose different levels of dropout in available scRNAseq data by leveraging various imputation techniques. Specifically, we introduce artificial missingness at 10%, 20%, and 30% levels under a missing completely at random (MCAR) framework, repeated across 10 independent runs. We benchmarked six imputation strategies - MAGIC, IterativeImputer, KNNImputer, Mean Imputation, SoftImpute, and a Generative adversarial network (GAN) - based approaches using multiple evaluation metrics: marker gene preservation, clustering consistency (Adjusted Rand Index - ARI), gene-wise correlation with ground truth, and structural separation (silhouette scores). The results clearly underline that no single imputation method dominated across all metrics. Overall, Mean and KNN imputers showed limited recovery across all benchmarks. GAN excelled in global transcriptional recovery and SoftImpute in preserving biologically meaningful cell-type signals. Our results highlight the importance of selecting the imputation methods as part of the pre-processing step towards the downstream biological questions related to transcriptome recovery, detection of marker genes, or maintaining cell-type-specific resolution.

bioinformatics↗

bioRxiv2026

Single-cell RNA sequencing of CTLA-4 and PD-1 blockade in pulmonary paracoccidioidomycosis highlights a protective transcriptional program mediated by activated Th17 cells, neutrophils and macrophages

Pulmonary paracoccidioidomycosis (PCM) relies on a finely balanced lung-immune network in which Th17, Treg, neutrophils, and macrophages orchestrate fungal control and tissue integrity. Furthermore, within the context of single-cell sequencing, little is known about how immune checkpoint inhibition modulates this balance during systemic mycosis. In a previous study we verified that the blockade of checkpoint molecules (CTLA-4 and PD-1) restores protective immunity that reduces fungal loads, tissue pathology and mortality of infected mice. Here, we have further studied this model by single-cell RNA sequencing on lung leukocytes from mice infected with Paracoccidioides brasiliensis and treated with anti-CTLA-4 or anti-PD-1 antibodies to define the cellular and molecular consequences of checkpoint blockade in vivo. We generated a high-resolution atlas covering T cells, neutrophils, macrophages/monocytes, B cells, NK cells, and epithelial subsets. Checkpoint inhibition consistently remodeled the CD4 T cell compartment toward a Th17-enriched program, with reduced interleukin-10 expressing Treg frequencies and a prominent interleukin-17A (IL-17), C-C chemokine receptor type 2, CXC chemokine (CXC) receptor type 6, CD44 (Il17aCcr2Cxcr6Cd44) signature. This shift was accompanied by the expansion and activation of neutrophils and macrophages expressing tumor necrosis factor and chemokines (including CXC motif chemokine ligand (Cxcl1, Cxcl2 and Ccl4), microbicidal-associated genes (S100 calcium-binding protein A8 and A9), and regulatory mediators such as secretory leukocyte protease inhibitor and interleukin-15. Ligand-receptor and trajectory analyses revealed a reinforced Th17-myeloid communication axis, particularly under CTLA-4 blockade, converging on IL17-centered inflammatory circuits while attenuating canonical Treg checkpoints (cytotoxic T-lymphocyte-associated protein 4, programmed cell death protein 1, interleukin-10). Together, these data demonstrate that anti-CTLA-4 and anti-PD-1 immunotherapies profoundly rewire the lung-immune microenvironment during PCM, amplifying effector Th17-neutrophil-macrophage networks that benefits protective antifungal activity Our work provides a mechanistic framework for evaluating immune checkpoint inhibitors in chronic fungal infections.

immunology↗

bioRxiv2021

Reference transcriptomes of porcine peripheral immune cells created through bulk and single-cell RNA sequencing

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B cell, conventional CD4 and CD8 {beta} T cells, NK cells, and {gamma}{delta} T cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

immunology↗

bioRxiv2020

The type 1 diabetes at-risk allele at rs3842753 increases beta cell INS mRNA expression and variance in a meta-analysis of single cell RNA sequencing data

Type 1 diabetes is characterized by the autoimmune destruction of insulin secreting {beta} cells. Genetic variations upstream at the insulin (INS) locus contribute to ~10% of type 1 diabetes heritable risk. Multiple studies showed an association between rs3842753 C/C genotype and type 1 diabetes susceptibility, but the molecular mechanisms remain unclear. To date, no large-scale studies have looked at the effect of genetic variation at rs3842753 on INS mRNA at the single cell level. We aligned all human islet single cell RNA sequencing datasets available to us in 2020 to the reference genome GRCh38.98 and genotyped rs3842753, integrating 2315 {beta} cells and 1223 {beta}-like cells from 13 A/A protected donors, 23 A/C heterozygous donors, and 35 C/C at-risk donors, including adults without diabetes and with type 2 diabetes. INS expression mean and variance were significantly higher in single {beta} cells from females compared with males. Comparing across {beta} cells and {beta}-like cells, we found that rs3842753 C containing cells (either homozygous or heterozygous) had the highest INS expression. We also found that {beta} cells with the rs3842753 C allele had significantly higher ER stress marker gene expression compared to the A/A homozygous genotype. These findings support the emerging concept that inherited risk of type 1 diabetes may be associated with inborn, persistent elevated insulin production which may lead to {beta} cell ER stress and fragility.

cell biology↗