bioRxiv Science⌕ Search

SEARCH · bioRxiv Science

Results for “single cell RNA sequencing”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22Linked to original sources

Comparison of Different Approaches to Single Cell RNA Sequencing of Cancer Associated Fibroblasts

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with a poor prognosis. PDAC has a high propensity for metastasis, particularly to the lungs and liver. Cancer associated fibroblasts (CAFs) represent a major stromal component of PDAC with both tumor-promoting and restraining properties. Of note, CAFs play a significant role in the creation of an immunosuppressive tumor microenvironment (TME) and the metastasis of PDAC. Studies have demonstrated functional heterogeneity among different subpopulations of CAFs, highlighting the need to identify specific subpopulations when targeting CAFs. MethodsThe orthotopic model was used for both KPC-4545 and KPC-3403 cell lines, which were derived from the primary tumors of KPC mice with liver metastases and lung metastases only, respectively. In brief, 2x106 KPC cells were injected subcutaneously into the flanks of synergic female C57BI6 mice. Tumors were harvested and cut into 2-3 mm3 pieces before being implanted into the pancreas of new 6-8-week-old syngeneic female C57Bl/6 mice. Murine orthotopic tumors were dissected, mechanically and enzymatically processed with Miltenyi Tumor Dissociation Kit (Miltenyi Biotec) thirteen days after tumor implantation. Samples were filtered with a 100 {micro}m strainer, washed with T cell media, and centrifuged twice. Two different samples underwent single cell RNA-sequencing (scRNA-seq) for each cell line: an unenriched sample, which represents all cells following dissociation of the tumor, and a CAF-enriched sample. To further obtain the CAF-enriched sample, cells were then stained with CD45-AF657 (BioLegend clone 30-F11, 1:20), CD31-AF647 (BioLegend clone 390, 1:20), EPCAM-AF647 (BioLegend, clone G8.8, 1:20), and TER119-AF647 (BioLegend clone TER-119 1:20) for 30 minutes on ice. After two washes, CD45-, CD31-, EPCAM-, and TER119-negative cells, representing the CAF-enriched fraction, were obtained via cell sorting. scRNA-seq of both the unenriched and CAF-enriched fractions were performed using 10X Chromium microfluidic chips and data was analyzed using CellRanger v6.1.1, mm10 transcriptome reference, and 10X Loupe Browser. ResultsWe found that scRNA-seq of the unenriched whole tumor showed only one cluster of CAFs for both cells lines, making it difficult for studying CAF heterogeneity. Enriching for CAFs prior to scRNA-seq allowed for better capture of CAFs and provided more granularity on CAF heterogeneity for both KPC-4545 and KPC-3403. ConclusionsWhile enrichment provides more information on CAF heterogeneity, the process results in the loss of other cells within the TME. The need to capture CAF heterogeneity while studying cell-cell interaction between CAFs and other cells within the TME and identifying how distinct CAF populations respond differently to treatment warrants the use of other methods such as single-nuclear RNA-seq.

cancer biology↗

Single-cell RNA sequencing reveals cell-type specific cis-eQTLs in peripheral blood mononuclear cells

Most disease-associated genetic risk factors are regulatory. Here, we generated single-cell RNA-seq data of [~]25,000 peripheral blood mononuclear cells from 45 donors to identify how genetic variants affect gene expression. We validated this approach by replicating previously published whole blood RNA-seq cis-expression quantitative trait loci effects (cis-eQTLs), but also identified new cell type-specific cis-eQTLs. These eQTLs give additional insight into the downstream consequences of genetic risk factors for immune-mediated diseases.

genetics↗

Transcriptomic landscape and potential therapeutic targets for human testicular aging revealed by single-cell RNA sequencing

BackgroundTesticular aging is known to cause male age-related fertility decline and hypogonadism, but the underlying molecular mechanisms remain unclear. MethodsWe survey the single-cell transcriptomic landscape of testes from young and old men and examine age-related changes in germline and somatic niche cells. ResultsIn-depth evaluation of the gene expression dynamics of germline cells reveals that disturbance of base-excision repair pathway is a major feature of aging spermatogonial stem cells (SSCs), suggesting that defective DNA repair of SSCs may serve as a potential driver for increased de novo germline mutations with age. Further analysis of aging-associated transcriptional changes shows that stress-related changes and apoptotic signaling pathway accumulate in aged somatic cells. We identify age-related impairment of redox homeostasis in aged Leydig cells and find that pharmacological treatment with antioxidants alleviate this cellular dysfunction of Leydig cells and promote testosterone production. Lastly, our results reveal that decreased pleiotrophin (PTN) signaling is a contributing factor for testicular aging. ConclusionsThese findings provide a comprehensive understanding of the cell-type-specific mechanisms underlying human testicular aging at a single-cell resolution, and suggest potential therapeutic targets that may be leveraged to address age-related male fertility decline and hypogonadism. FundingThis work was supported by the National Key Research and Development Program of China (2018YFA0107200, 2018YFA0801404), the National Natural Science Foundation of China (32130046, 82171564, 82101669, 81871110, 81971759), the Key Research and Development Program of Guangdong Province (2019B020234001), the Natural Science Foundation of Guangdong Province, China (2022A1515010371), the Major Project of Medical Science and Technology Development Research Center of National Health Planning Commission, China (HDSL202001000), the Open Project of NHC Key Laboratory of Male Reproduction and Genetics (Family Planning Research Institute of Guangdong Province) (KF202001), the Guangdong Province Regional Joint Fund-Youth Fund Project (2021A1515110921), the China Postdoctoral Science Foundation (2021M703736).

developmental biology↗

Single cell RNA sequencing reveals cellular diversity of trisomy 21 retina

Retina is a crucial tissue for the capturing and processing of light stimulus. Characterization of the retina at single cell level is essential for the understanding of its biological functions. A variety of abnormalities in terms of morphology and function were reported in T21 retina. To evaluate the effects of chromosome aneuploidy on retina development, we characterized single cell transcriptional profiles of a T21 fetus and performed comprehensive bioinformatic analyses. Our data revealed the diversity and heterogeneity of cellular compositions in T21 retina. In total, we identified seven major cell types, and detected several subtypes within each cell type, followed by the detection of corresponding molecular markers including previously reported ones and a series of novel markers. Our analyses identified extensive communication networks between distinct cellular types, among which a few ligand-receptor interactions were associated with the development of retina and immunoregulatory interactions. Taken together, our data provided the first single cell transcriptome profile for human T21 retina which facilitates our understanding on the dosage effects of chromosome 21 on the development of retina.

cell biology↗

MARVEL: An integrated alternative splicing analysis platform for single-cell RNA sequencing data

Alternative splicing is an important source of heterogeneity in gene expression between individual cells but remains an understudied area due to the paucity of computational tools to analyze splicing dynamics at single-cell resolution. Here, we present MARVEL, a comprehensive R package for single-cell splicing analysis applicable to RNA sequencing generated from the plate- and droplet-based methods. We performed extensive benchmarking of MARVEL against available tools and demonstrated its utility by analyzing iPSC differentiation into endoderm cells and cardiomyocytes. MARVEL enables systematic and integrated splicing and gene expression analysis of single cells to characterize the splicing landscape and reveal biological insights.

bioinformatics↗

Genotyping Copy Number Alterations from single-cell RNA sequencing

MotivationCancers are composed by several heterogeneous subpopulations, each one harbouring different genetic and epigenetic somatic alterations that contribute to disease onset and therapy response. In recent years, copy number alterations leading to tumour aneuploidy have been identified as potential key drivers of such populations, but the definition of the precise makeup of cancer subclones from sequencing assays remains challenging. In the end, little is known about the mapping between complex copy number alterations and their effect on cancer phenotypes. ResultsWe introduce CONGAS, a Bayesian probabilistic method to phase bulk DNA and single-cell RNA measurements from independent assays. CONGAS jointly identifies clusters of single cells with subclonal copy number alterations, and differences in RNA expression. The model builds statistical priors leveraging bulk DNA sequencing data, does not require a normal reference and scales fast thanks to a GPU backend and variational inference. We test CONGAS on both simulated and real data, and find that it can determine the tumour subclonal composition at the single-cell level together with clone-specific RNA phenotypes in tumour data generated from both 10x and Smart-Seq assays. AvailabilityCONGAS is available as 2 packages: CONGAS (https://github.com/caravagnalab/congas), which implements the model in Python, and RCONGAS (https://caravagnalab.github.io/rcongas/), which provides R functions to process inputs, outputs, and run CONGAS fits. The analysis of real data and scripts to generate figures of this paper are available via RCONGAS; code associated to simulations is available at https://github.com/caravagnalab/rcongas_test. Contactgcaravagna@units.it Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Integrating short-read and long-read single-cell RNA sequencing for comprehensive transcriptome profiling in mouse retina

The vast majority of protein-coding genes in the human genome produce multiple mRNA isoforms through alternative splicing, significantly enhancing the complexity of the transcriptome and proteome. To establish an efficient method for characterizing transcript isoforms within tissue samples, we conducted a systematic comparison between single-cell long-read and conventional short-read RNA sequencing techniques. The transcriptome of approximately 30,000 mouse retina cells was profiled using 1.54 billion Illumina short reads and 1.40 billion Oxford Nanopore long reads. Consequently, we identified 44,325 transcript isoforms, with a notable 38% previously uncharacterized and 17% expressed exclusively in distinct cellular subclasses. We observed that long-read sequencing not only matched the gene expression and cell-type annotation performance of short-read sequencing but also excelled in the precise identification of transcript isoforms. While transcript isoforms are often shared across various cell types, their relative abundance shows considerable cell-type-specific variation. The data generated from our study significantly enhance the existing repertoire of transcript isoforms, thereby establishing a foundational resource for future research into the mechanisms and implications of alternative splicing within retinal biology and its links to related diseases.

bioinformatics↗

Vaeda computationally annotates doublets in single-cell RNA sequencing data

MotivationSingle-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technologys utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation computational methods to annotate and remove doublets form scRNA-seq datasets are needed. ResultsWe introduce vaeda, a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to sixteen benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. All together, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows. AvailabilityVaeda is available at https://github.com/kostkalab/vaeda Contactkostka@pitt.edukostka@pitt.edu

bioinformatics↗

Global Dynamic Molecular Profiles of Stomatal Lineage Cell Development by Single-Cell RNA Sequencing

The regulation of stomatal lineage cell development has been extensively investigated. However a comprehensive characterization of this biological process based on single-cell transcriptome analysis has not yet been reported. Here, we performed RNA-seq on over 12,844 individual cells from the cotyledons of five-day-old Arabidopsis seedlings. We identified 11 cell clusters corresponding mostly to cells at specific stomatal developmental stages with a series of new marker genes. Comparative analysis of genes with the highest variable expression in these cell clusters revealed three transcriptional networks that regulate the development of mesophyll and guard cells, as well as the differentiation from protodermal to guard mother cells. We investigated the developmental dynamics of marker genes via pseudo-time analysis which revealed potential interactions between them. The identification of several novel marker genes suggests new regulatory mechanisms during development of stomatal cell lineage.

plant biology↗

Single-cell RNA sequencing reveals influences of rearing environment on cellular immunity in brown trout (Salmo trutta)

Vertebrate immune systems exhibit striking evolutionary diversity, yet our understanding remains biased toward mammalian models. Here, we generate a single-cell atlas of immune cells from the ecologically and economically important salmonid Salmo trutta (brown trout), a lineage characterized by an ancestral whole-genome duplication (WGD). Profiling over 83,000 kidney-derived immune cells, we resolved 34 transcriptionally distinct populations, identified core immune lineages, and uncovered novel markers in neutrophils, macrophages, T- and B-cells. We detected pervasive transcriptional divergence between WGD-derived ohnologue pairs, indicating putative sub- and neofunctionalization in immune gene regulation. We further show that the transcriptional identity of immune cells is shaped by rearing history: fish raised in hatcheries--whether for one or multiple generations--showed shifts in immune gene expression across cell types. These included genes involved in G protein-coupled receptor signalling, a process which has previously been implicated in domestication. Our findings provide insight into the evolution of vertebrate immunity and raise concerns about the immunological fitness of hatchery-reared fish released into the wild.

immunology↗

Single-cell RNA sequencing reveals dysregulated fibroblast subclusters in prurigo nodularis

Prurigo nodularis (PN) is an intensely pruritic, chronic inflammatory skin disease that disproportionately affects black patients. However, the pathogenesis of PN is poorly understood. We performed single-cell transcriptomic profiling, ligand receptor analysis and cell trajectory analysis of 28,695 lesional and non-lesional PN skin cells to uncover disease-identifying cell compositions and genetic characteristics. We uncovered a dysregulated role for fibroblasts (FBs) and myofibroblasts as a key pathogenic element in PN, which were significantly increased in PN lesional skin. We defined seven unique subclusters of FBs in PN skin and observed a shift of PN lesional FBs towards a cancer-associated fibroblast (CAF)-like phenotype, with WNT5A+ CAFs increased in the skin of PN patients and similarly so in squamous cell carcinoma (SCC). A multi-center PN cohort study subsequently revealed an increased risk of SCC as well as additional CAF-associated malignancies in PN patients, including breast and colorectal cancers. Systemic fibroproliferative diseases were also upregulated in PN patients, including renal sclerosis and idiopathic pulmonary fibrosis. Ligand receptor analyses demonstrated increased FB1-derived WNT5A and periostin interactions with neuronal receptors MCAM and ITGAV, suggesting a fibroblast-neuronal axis in PN. Type I IFN responses in immune cells and increased angiogenesis/permeability in endothelial cells were also observed. As compared to atopic dermatitis (AD) and psoriasis (PSO) patients, increased mesenchymal dysregulation is unique to PN with an intermediate Th2/Th17 phenotype between atopic dermatitis and psoriasis. These findings identify a pathogenic role for CAFs in PN, including a novel targetable WNT5A+ fibroblast subpopulation and CAF-associated malignancies in PN patients.

bioinformatics↗

FASTGenomics: An analytical ecosystem for single-cell RNA sequencing data

Recent technological advances enable genomics of individual cells, the building blocks of all living organisms. Single cell data characteristics differ from those of bulk data, which led to a plethora of new analytical strategies. However, solutions are only useful for experts and currently, there are no widely accepted gold standards for single cell data analysis. To meet the requirements of analytical flexibility, ease of use and data security, we developed FASTGenomics (https://fastgenomics.org) as a powerful, efficient, versatile, robust, safe and intuitive analytical ecosystem for single-cell transcriptomics.

bioinformatics↗

STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrate STRIDEs utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell types to spatial locations but also improved the identification of spatial localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions, and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/wanglabtongji/STRIDE.

bioinformatics↗

Single Cell RNA Sequencing of the Adult Drosophila Eye Reveals Distinct Clusters and Novel Marker Genes for All Major Cell Types

The adult Drosophila eye is a powerful model system for phototransduction and neurodegeneration research. However, single cell resolution transcriptomic data are lacking for this tissue. We present single cell RNA-seq data on 1-day male and female, 3-day and 7-day old male adult eyes, covering early to mature adult eyes. All major cell types, including photoreceptors, cone and pigment cells in the adult eye were captured and identified. Our data sets identified novel cell type specific marker genes, some of which were validated in vivo. R7 and R8 photoreceptors form clusters that reflect their specific Rhodopsin expression and the specific Rhodopsin expression by each R7 and R8 cluster is the major determinant to their clustering. The transcriptomic data presented in this report will facilitate a deeper mechanistic understanding of the adult fly eye as a model system.

genomics↗

Single-cell RNA Sequencing of Pediatric Ependymoma Unravels Subclonal Heterogeneity Associated with Patient Survival

Ependymoma (EPN) is a malignant glial tumor occurring throughout central nervous system which commonly presents in children. Although recent studies have characterized EPN samples at both the bulk and single-cell level, intra-tumoral heterogeneity across subclones remains a confounding factor which impedes understanding of EPN biology. In this study, we generated a high-resolution single-cell dataset of pediatric ependymoma with a particular focus on the comparison of subclone differences within tumors, and show upregulation of cilium-associated genes in more highly differentiated subclone populations. As a proxy to traditional pseudotime analysis, we applied a novel trajectory scoring method to reveal cellular compositions associated with poor survival outcomes across primary and relapsed patients. Furthermore, we identified putative cell-cell communication features between relapsed and primary samples and show upregulation of pathways associated with immune cell crosstalk. Our results reveal both inter- and intratumoral gene expression profiles and tumor differentiation and provide a framework for studying transcriptomic signatures of individual subclones in ependymoma at single-cell resolution.

bioinformatics↗

Multiview Graph Learning for single-cell RNA sequencing data

Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

genomics↗

Single-cell RNA sequencing unveils intestinal eosinophil development and specialization

Eosinophils are an integral part of the gastrointestinal (GI) immune system that contribute to homeostatic and inflammatory processes. Here, we investigated the existence of functional subsets that carry out specialized tasks in health and disease. We used single-cell transcriptomics and high-dimensional flow cytometry to delineate murine eosinophil subpopulations and their ontogenetic relationship in the steady state and during infection and inflammation. Profiling of eosinophils from bone marrow, blood, spleen and several GI tissues revealed five distinct subsets representing consecutive developmental and maturation stages across organs, each controlled by a specific set of transcription factors. Furthermore, we discovered a highly adapted PD-L1+CD80+ eosinophil subset in the GI tract, characterized by its immune regulatory properties, bactericidal activity upon challenge infection and tissue-protective function during inflammation. Our data provide a framework for the characterization of eosinophil subsets in GI diseases and highlight their crucial contribution to homeostasis, immune regulation and host defense.

immunology↗

cellSight: Characterizing dynamics of cells using single-cell RNA-sequencing

Single-cell analysis has transformed our understanding of cellular diversity, offering insights into complex biological systems. Yet, manual data processing in single-cell studies poses challenges, including inefficiency, human error, and limited scalability. To address these issues, we propose the automated workflow cellSight, which integrates high-throughput sequencing in a user-friendly platform. By automating tasks like cell type clustering, feature extraction, and data normalization, cellSight reduces researcher workload, promoting focus on data interpretation and hypothesis generation. Its standardized analysis pipelines and quality control metrics enhance reproducibility, enabling collaboration across studies. Moreover, cellSights adaptability supports integration with emerging technologies, keeping pace with advancements in single-cell genomics. cellSight accelerates discoveries in single-cell biology, driving impactful insights and clinical translation. It is available with documentation and tutorials at https://github.com/omicsEye/cellSight.

bioinformatics↗