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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 19 recordsLinked to original sources

bioRxiv2021

Unambiguous detection of SARS-CoV-2 subgenomic mRNAs with single cell RNA sequencing

Single cell RNA sequencing (scRNA-Seq) studies have provided critical insight into the pathogenesis of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), the causative agent of COronaVIrus Disease 2019 (COVID-19). scRNA-Seq workflows are generally designed for the detection and quantification of eukaryotic host mRNAs and not viral RNAs. Here, we compare different scRNA-Seq methods for their ability to quantify and detect SARS-CoV-2 RNAs with a focus on subgenomic mRNAs (sgmRNAs). We present a data processing strategy, single cell CoronaVirus sequencing (scCoVseq), which quantifies reads unambiguously assigned to sgmRNAs or genomic RNA (gRNA). Compared to standard 10X Genomics Chromium Next GEM Single Cell 3' (10X 3') and Chromium Next GEM Single Cell V(D)J (10X 5') sequencing, we find that 10X 5' with an extended read 1 (R1) sequencing strategy maximizes the detection of sgmRNAs by increasing the number of unambiguous reads spanning leader-sgmRNA junction sites. Using this method, we show that viral gene expression is highly correlated across cells suggesting a relatively consistent proportion of viral sgmRNA production throughout infection. Our method allows for quantification of coronavirus sgmRNA expression at single-cell resolution, and thereby supports high resolution studies of the dynamics of coronavirus RNA synthesis.

microbiology↗

bioRxiv2025

Multi-site Assessment of Methods for Cell Preservation Upstream of Single Cell RNA Sequencing

Single cell RNA sequencing (scRNA-seq) is a revolutionary technique to identify cell types and their molecular phenotype in heterogeneous biological specimens. ScRNA-seq typically requires fresh, high quality single cell suspensions that are processed immediately to preserve their molecular profiles. This presents a challenge for samples with long preparation times and prevents collection at remote sites lacking the required instrumentation for sample processing. Recently, several commercial assays have been released that enable sample preservation at the time of collection either via fixation or cryopreservation, allowing for sample processing to occur months after the initial collection. The Association of Biomolecular Research Facilities (ABRF) DNA Sequencing (DSRG) and Genomics Bioinformatics (GBiRG) Research Groups have undertaken a cross-platform, multi-site study to assess the performance and reproducibility of three platforms: a) 10x Genomics FLEX, b) Parse Bioscience Evercode WT v2 and c) Honeycomb Bio HIVE. Total leukocytes were isolated from a single healthy individual using the EasySep RBC depletion reagent. Cells were then characterized by collecting a 21-color flow cytometry dataset for reference and the remaining material was used for scRNA-seq procedures where different sites then processed either the fixed or cryopreserved cells for each method. We evaluated performance of each method across traditional scRNA-seq quality control metrics and analysis applications, including gene/transcript detection sensitivity, cell type discovery and annotation, and differential expression. We demonstrate that data from the methods tested can be effectively integrated and produce concordant results with regard to cell type annotation and relative abundance, though we observe platform-specific differences in the expression of a subset of genes. Preservation-based methods also show better retention of fragile granulocyte populations compared with fresh samples processed using the 10x 3 workflow. The improvements to preservation methods are changing the way research is conducted and our thorough investigation into the performance of each method provides a valuable resource to help scientists determine the most appropriate single cell preservation workflow given their sample collection logistics and laboratory infrastructure constraints.

genomics↗

bioRxiv2021

Glyoxal as alternative fixative for single cell RNA sequencing

Single cell RNA sequencing (scRNA-seq) has become an important method to identify cell types, delineate the trajectories of cell differentiation in whole organisms and understand the heterogeneity in cellular responses. Nevertheless, sample collection and processing remain a severe bottleneck for scRNA-seq experiments. Cell isolation protocols often lead to significant changes in the transcriptomes of cells, requiring novel methods to preserve cell states. Here, we developed and benchmarked protocols using glyoxal as a fixative for scRNA-seq application. Using Drop-seq methodology, we detected high numbers of transcripts and genes from glyoxal-fixed Drosophila cells after scRNA-seq. The effective glyoxal fixation of transcriptomes in Drosophila and human cells was further supported by a high correlation of gene expression data between glyoxal-fixed and unfixed samples. Accordingly, we also found highly expressed genes overlapping to a large extent between experimental conditions. These results indicated that our fixation protocol did not induce considerable changes in gene expression and conserved the transcriptome for subsequent single cell isolation procedures. In conclusion, we present glyoxal as a suitable fixative for Drosophila cells and potentially cells of other species that allows high-quality scRNA-seq applications.

genomics↗

bioRxiv2021

Kidney pathological changes alter cell clustering features in single-cell RNA sequencing

Single cell RNA sequencing (scRNA-Seq) is useful to classify cell-specific gene expression profiles in kidney tissue. As viable cells are required, we report an optimized cell dissociation methodology and the necessity of screening tissue histology prior to scRNA-Seq. We demonstrate that glomerular injury can selectively reduce the appearance of groups of cells during analysis of cell clustering and we confirmed reductions in cell-specific markers among injured cells on kidney sections with fluorescence microscopy. Interpretation of scRNA-Seq results may be refined based upon these considerations.

cell biology↗

bioRxiv2020

NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing

Single cell genomics is rapidly advancing our knowledge of cell phenotypic types and states. Driven by single cell/nucleus RNA sequencing (scRNA-seq) data, comprehensive atlas projects covering a wide range of organisms and tissues are currently underway. As a result, it is critical that the cell transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell-types by surface protein expression to defining diseases by molecular drivers. Here we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the non-linear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that precisely captures the cell type identity represented in the complete scRNA-seq transcriptional profiles. The marker genes selected provide a barcode of the necessary and sufficient characteristics for semantic cell type definition and serve as useful tools for downstream biological investigation. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and non-coding RNAs in neuronal cell type identity.

bioinformatics↗

bioRxiv2025

Genome-wide expression gradient estimation based on local pseudotime in single cell RNA sequencing

Single cell genomics measures the internal state of individual cells and makes it possible to describe biological phenomena such as cell type heterogeneity, developmental progression, and dynamic differentiation using computational methods. A prominent approach to facilitate downstream analysis, for large collection of cells, is to connect individual cells by a k-Nearest Neighbor Graph (kNN-G). The kNN-G is among other things used by methods that derive pseudotime, a temporal ordering of cells. However, pseudotime methods require knowledge of initial states of the trajectories created. Alternatives have been invented that estimate gene rate of transcription e.g. by RNA velocity but these methods dont generalize to all individual genes for estimating transcriptional rate of change along pseudotime. While these methods can derive pseudo progression de novo by extrapolating transcription velocity vectors they are limited to subsets of genes where intronic reads are captured with sufficient magnitude. Here we present pseudovelocity, an alternative method for calculating a local rate of change in transcription based on the kNN-G and diffusion-based pseudotime, to derive RNA velocity estimates for individual genes, and demonstrate how it improves the gene-based estimation of RNA velocity for downstream analysis. This package can be accessed here: https://gitlab.com/Xparx/pseudovelocity.

bioinformatics↗

bioRxiv2018

SMARTer single cell total RNA sequencing

Single cell RNA sequencing methods have been increasingly used to understand cellular heterogeneity. Nevertheless, most of these methods suffer from one or more limitations, such as focusing only on polyadenylated RNA, sequencing of only the 3 end of the transcript, an exuberant fraction of reads mapping to ribosomal RNA, and the unstranded nature of the sequencing data. Here, we developed a novel single cell strand-specific total RNA library preparation method addressing all the aforementioned shortcomings. Our method was validated on a microfluidics system using three different cancer cell lines undergoing a chemical or genetic perturbation. We demonstrate that our total RNA-seq method detects an equal or higher number of genes compared to classic polyA[+] RNA-seq, including novel and non-polyadenylated genes. The obtained RNA expression patterns also recapitulate the expected biological signal. Inherent to total RNA-seq, our method is also able to detect circular RNAs. Taken together, SMARTer single cell total RNA sequencing is very well suited for any single cell sequencing experiment in which transcript level information is needed beyond polyadenylated genes.

genomics↗

bioRxiv2020

PRIME: a probabilistic imputation method to reduce dropouteffects in single cell RNA sequencing

Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data therefore need to be carefully processed before in-depth analysis. Here we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local community of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single cell sequencing), on six datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.

bioinformatics↗

bioRxiv2019

A molecular cell atlas of the human lung from single cell RNA sequencing

Although single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet- and plate-based single cell RNA sequencing applied to [~]75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution.

genomics↗

bioRxiv2017

mcSCRB-seq: sensitive and powerful single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has emerged as the central genome-wide method to characterize cellular identities and processes. While performance of scRNA-seq methods is improving, an optimum in terms of sensitivity, cost-efficiency and flexibility has not yet been reached. Among the flexible plate-based methods \"Single-Cell RNA-Barcoding and Sequencing\" (SCRB-seq) is one of the most sensitive and efficient ones. Based on this protocol, we systematically evaluated experimental conditions such as reverse transcriptases, reaction enhancers and PCR polymerases. We find that adding polyethylene glycol considerably increases sensitivity by enhancing cDNA synthesis. Furthermore, using Terra polymerase increases efficiency due to a more even cDNA amplification that requires less sequencing of libraries. We combined these and other improvements to a new scRNA-seq library protocol we call \"molecular crowding SCRB-seq\" (mcSCRB-seq), which we show to be the most sensitive and one of the most efficient and flexible scRNA-seq methods to date.

genomics↗

bioRxiv2017

Gene Length And Detection Bias In Single Cell RNA Sequencing Protocols

Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include Unique Molecular Identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene and PCR duplicates resulting in copies of the UMI are not included in expression estimates. Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets differing in terms of capture technology, library preparation, cell types and species. We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, and have a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. We briefly discuss the role of these genes in the context of differential expression testing and GO analysis. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal underlying biology influencing expression of mESCs.

bioinformatics↗

bioRxiv2017

A Comparison Between Single Cell RNA Sequencing And Single Molecule RNA FISH For Rare Cell Analysis

The development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what \"transcriptome coverage\" (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of [~]2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.

genomics↗

bioRxiv2025

LN's t-Test: A Principled Approach to t-Testing in Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, yet differential gene expression (DGE) analysis remains hindered by inconsistencies in log fold change (LFC) estimation. Existing methods, such as those implemented in Scanpy and Seurat, rely on log-transformed count data with a pseudocount, introducing a bias that compromises the reliability of the statistical inference. In this work, we propose LNs t-test, a novel approach to DGE testing that circumvents these biases by employing a log-Normal (LN) distribution-based LFC estimator. Our method jointly estimates the probability of non-zero expression and the mean of positive expression values, enabling an asymptotically unbiased and normally distributed LFC estimator with corresponding confidence intervals. Through extensive simulation studies, we demonstrate that LNs t-test outperforms competing methods by reducing false discovery rates and providing more accurate effect size estimates. Notably, we leverage stochastic ordering theory to explain why conventional t-tests systematically mis-classify non-differentially expressed genes under realistic variance conditions. Our approach offers a theoretically principled and computationally efficient alternative for DGE analysis in scRNA-seq, with implications for improving the reliability and interpretability of single-cell transcriptomics studies. Code that implements the results is available on GitHub: https://github.com/okviman/DE-ZILN.

bioinformatics↗

bioRxiv2021

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) aims to characterize how variation in gene expression is distributed across cells in tissues and organisms. Yet, effective comprehension of these extremely high-dimensional datasets remains a critical barrier to progress in biological research. In standard analyses of scRNA-seq data, feature selection steps aim to reduce the dimensionality of the data by focusing on a subset of genes that are the most biologically variable across a set of cells. Ideally, these features provide the genes that are the most informative for partitioning groups of transcriptionally distinct cells, each representing a different cell type or identity. In this work, we propose a simple feature selection model where a binomial sampling process for each mRNA species produces a null model of technical variation. To compare our model to existing methods, we use scRNA-seq data where cell identities have been established a priori for each cell, and characterize whether different feature sets retain biologically varying genes, distort neighborhood structures, and allow popular clustering algorithms to partition groups of cells into their established classes. We find that our model of biological variation, which we term "Differentially Distributed Genes" or DDGs, outperforms existing methods, and enables dimensionality reduction without loss of critical structure within the data set.

bioinformatics↗

bioRxiv2017

Gene Expression Distribution Deconvolution in Single Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each genes expression distribution across cells, thus allowing the assessment of the dispersion, burstiness, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data is noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a re-examination of 9 public data sets, we propose a simple technical noise model for scRNA-seq data with Unique Molecular Identifiers (UMI). We develop DESCEND, a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and burstiness. DESCEND can adjust for cell-level covariates such as cell size, cell cycle and batch effects. DESCENDs noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations, and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially bursty genes, identifying cell types, and selecting differentiation markers.

genetics↗

bioRxiv2019

DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data

Single cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic data sets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps. Here, we present DrivAER, a machine learning approach that scores annotated gene sets based on their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. We demonstrate that DrivAER extracts the key driving pathways and transcription factors that regulate complex biological processes from scRNA-seq data.

bioinformatics↗

bioRxiv2026

Characterizing and Mitigating Protocol-Dependent Gene Expression Bias in 3' and 5' Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) has enabled large-scale characterization of cellular heterogeneity; yet, integrating datasets generated through different library preparation protocols remains challenging. For instance, comparisons between 10X Genomics 3' and 5' chemistries are complicated by protocol-dependent technical biases imposed by differences in transcript end capture and amplification. While normalization, and often batch correction, is an integral step in preprocessing scRNA-seq datasets, it remains unclear which correction is most appropriate, or even necessary, for reliable cross-protocol comparisons. Here, we systematically characterize protocol-related expression differences using 35 matched donors across six tissues profiled with both 3' and 5' scRNA-seq approaches. We find that gene expression discrepancies are not pervasive across the whole transcriptome, but driven instead by a relatively small, reproducible subset of protocol-biased genes. Excluding these genes improves cross-protocol concordance, indicating that most genes are directly comparable without aggressive correction. We then benchmark commonly employed normalization approaches and show that while several methods, such as fastMNN, improve statistical alignment when cell populations are well matched, they can distort gene-level signals and inflate differential expression in biologically realistic settings with incomplete cell-type overlap. Taken together, our results demonstrate that protocol bias between 3' and 5' scRNA-seq is limited in scope and that targeted handling of a small set of biased genes presents an alternative approach to normalization or batch correction strategies. This work provides a practical guideline for integrating 3' and 5' scRNA-seq data and highlights the importance of matching normalization strategies to the structure of technical variation and the intended downstream analyses.

bioinformatics↗

bioRxiv2026

De novo transformer modeling improves recovery of genetic cell types from sparse single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) simultaneously provides gene-expression profiles and genetic variants from individual cells, creating an opportunity to relate cellular phenotypes to their somatic evolutionary histories. However, delineation of genetic type (GTs) from scRNA-seq remains difficult because most variant positions are unobserved in individual cells and the observed base calls contain substantial false-positive and false-negative errors. We evaluated some existing phylogenetic and imputation methods using one simulated dataset and two empirical tumor datasets. We found that extreme sparsity prevented reliable recovery of known or independently inferred GTs when multiple GTs were present. This led us to adapt the STICI transformer architecture to train a separate model de novo on each sparse cell-variant (CV) matrix. These data-specific models predicted millions of missing bases, greatly reducing matrix sparsity. Phylogenetic analyses of the imputed CV matrices showed substantially improved recovery of GTs in both simulated and empirical datasets. In the empirical dataset, transformer-based analysis also suggested finer-scale genetic structure within some previously reported GTs that was not apparent with the existing methods. These results demonstrate that highly sparse scRNA-seq datasets contain substantially more recoverable lineage information than previously appreciated and that de novo transformer modeling provides an effective approach for recovering much of this hidden information. Nevertheless, sequencing errors persisted, limiting reconstruction of cellular lineage structure and leaving significant room for methodological improvement before expression phenotypes can be examined reliably in the context of their cellular evolutionary relationships.

bioinformatics↗