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bioRxiv2023

scfetch: an R package to access and format single-cell RNA sequencing datasets from public repositories

BackgroundDownloading and reanalyzing the existing single-cell RNA sequencing (scRNA-seq) data provides an efficient choice to gain clues and new insights. However, no tool can fetch the diverse scRNA-seq data types (raw data, count matrix, and processed object) distributed in various repositories, process and load the downloaded data to R, convert formats between scRNA-seq objects, and benchmark the format conversion tools. FindingsHere, we present GEfetch2R, an R package with Docker image to (i) download diverse scRNA-seq data types, including raw data (SRA and ENA), count matrices (GEO, UCSC Cell Browser, and PanglaoDB), and processed objects (Zenodo, CELLxGENE, and HCA); (ii) process the downloaded data, load output/downloaded count matrices and annotations to R (SeuratObject/DESeqDataSet), filter the SeuratObject based on cell metadata and genes, and merge multiple SeuratObjects if applicable; (iii) convert formats between the widely used scRNA-seq objects, including SeuratObject, AnnData, SingleCellExperiment, CellDataSet/cell_data_set, and loom, and benchmark format conversion tools in terms of information kept, usability, running time, and scalability to guide the tool selection. Furthermore, GEfetch2R can also download, process, and load bulk RNA-seq raw data (SRA and ENA) and count matrices (GEO) to R (DESeqDataSet). ConclusionsGEfetch2R is an R package dedicated to facilitating researchers to access and explore the existing gene expression data from various public repositories. It can function as a data downloader (supports all three scRNA-seq and two bulk RNA-seq data types), a data processor (processes and loads the output/downloaded count matrices and annotations to R), and an object format converter (between the widely used scRNA-seq objects).

bioinformatics↗

bioRxiv2025

Comparative Analysis of Feature Selection Methods for Single-Cell RNA Sequencing Data

Feature selection is a critical preprocessing step in single-cell RNA sequencing (scRNA-seq) analysis, directly impacting downstream clustering and biological interpretation. We systematically compared 16 feature selection methods across three diverse datasets: PBMC3K (immune cells), Visium Heart, and Visium Brain (spatial transcriptomics). Methods included established approaches (Seurat HVG variants, CellRanger HVG), statistical methods (Pearson residuals, variance-based, coefficient of variation), supervised methods (ANOVA F-test, mutual information, random forest), and deep learning techniques (IntegratedGradients, DeepLIFT, GradientShap). We evaluated methods based on execution time, feature overlap, marker recovery, and pathway enrichment. Our analysis revealed substantial variability across methods, with mean pairwise overlap of only 23.7%. However, a core set of 1,150 genes was consistently selected by[≥] 50% of methods. Supervised methods demonstrated superior recovery of known cell type markers, while unsupervised approaches captured broader biological processes. Deep learning methods identified unique gene sets with strong immune pathway enrichment but higher computational cost. Pathway analysis showed all methods successfully identified relevant biological processes, though with varying emphasis. These findings provide practical guidance for method selection based on analytical goals and highlight the value of ensemble approaches in scRNA-seq feature selection.

bioinformatics↗

bioRxiv2017

Assessing The Reliability Of Spike-In Normalization For Analyses Of Single-Cell RNA Sequencing Data

By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell, and to scale the observed expression values so that the coverage of spike-in RNA is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample, and on similarities in behaviour (e.g., capture efficiency) between the spike-in and endogenous transcripts. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behaviour between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses such as detection of highly variable genes and clustering. Our results suggest that spike-in normalization is reliable enough for routine use in single-cell RNA sequencing data analyses.

bioinformatics↗

bioRxiv2020

Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases. Author SummaryDetection of allelic expression imbalance (AEI), a phenomenon where the two alleles of a gene differ in their expression magnitude, is a key step towards the understanding of phenotypic variations among individuals. Existing methods detect AEI use bulk RNA sequencing (RNA-seq) data and ignore AEI variations among different cell types. Although single-cell RNA sequencing (scRNA-seq) has enabled the characterization of cell-to-cell heterogeneity in gene expression, the high costs have limited its application in AEI analysis. To overcome this limitation, we developed BSCET to characterize cell-type-specific AEI using the widely available bulk RNA-seq data by integrating cell-type composition information inferred from scRNA-seq samples. Since the degree of AEI may vary with disease phenotypes, we further extended BSCET to detect genes whose cell-type-specific AEIs are associated with clinical factors. Through extensive benchmark evaluations and analyses of two pancreatic islet bulk RNA-seq datasets, we demonstrated BSCETs ability to refine bulk-level AEI to cell-type resolution, and to identify genes whose cell-type-specific AEIs are associated with the progression of type 2 diabetes. With the vast amount of easily accessible bulk RNA-seq data, we believe BSCET will be a valuable tool for elucidating cell type contributions in human diseases.

bioinformatics↗

bioRxiv2024

The comparison of single-cell RNA sequencing platforms based droplets

Single-cell sequencing enables to reveal cellular heterogeneity and discover new cellular subpopulations. In terms of the strategy of single-cell sequencing, the main methods are based with combinatorial index, microwell and microfluidic. Due to the simplicity, methods based droplets are widely used for single-cell sequencing for multi-omics. Therefore, in order to facilitate researchers to choose a suitable platform to meet their application scenarios, we compared several commercial platforms: the Chromium X platform of 10x Genomics, the MobiNova-100 platform of MobiDrop, the SeekOne platform of SeekGene, and the C4 platform of BGI. Based the comprehensive assessment of the data analysis, the Chromium X platform shows a excellent performance, closely followed by MobiNova-100 platform. One-Sentence SummaryAs droplet-based single-cell sequencing platforms, Chromium X and MobiNova-100 have comparable data performance.

cell biology↗

bioRxiv2020

Spatiotemporal single-cell RNA sequencing of developing hearts reveals interplay between cellular differentiation and morphogenesis

Single-cell RNA sequencing is a powerful tool to study developmental biology but does not preserve spatial information about cellular interactions and tissue morphology. Here, we combined single-cell and spatial transcriptomics with new algorithms for data integration to study the early development of the chicken heart. We collected data from four key ventricular development stages, ranging from the early chamber formation stage to the late four-chambered stage. We created an atlas of the diverse cellular lineages in developing hearts, their spatial organization, and their interactions during development. Spatial mapping of differentiation transitions revealed the intricate interplay between cellular differentiation and morphogenesis in cardiac cellular lineages. Using spatially resolved expression analysis, we identified anatomically restricted gene expression programs. Last, we discovered a stage-dependent role for the small secreted peptide, thymosin beta-4, in the coordination of multi-lineage cellular populations. Overall, our study identifies key stage-specific regulatory programs that govern cardiac development.

genomics↗

bioRxiv2022

Benchmarking strategies for cross-species integration of single-cell RNA sequencing data

The growing number of available single cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. We benchmarked 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examined the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also developed a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieved a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs was beneficial. SAMap outperformed when integrating whole-body atlases between species with challenging gene homology annotation. We provided our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.

bioinformatics↗

bioRxiv2026

Ultra-fast and scalable high-resolution full-length single-cell RNA sequencing using CHART-seq

Plate-based full-length single-cell RNA sequencing resolves transcript structure details but remains difficult to scale because each cell usually requires a separate library. Here we developed CHART-seq (Combinatorial Heteroduplex Assay via Recombinant Tn5), which uses orthogonally indexed Tn5 complexes to tagment RNA/cDNA heteroduplexes and permits early sample pooling. The workflow processed up to 96 cells per library, was compatible with 384-well expansion, and completed library preparation within 3 h at a reagent cost below US$1 per cell. At matched sequencing depth, CHART-seq detected more genes and annotated isoforms than Smart-seq2, Smart-seq3, Flash-seq and SHERRY2, while retaining broad genebody coverage and reproducible expression estimates. In the CHART-seq results of vascular smooth muscle cells, TGF-{beta}1 pretreatment before PDGF-BB exposure partly restored contractile features, suppressed a PDGF-associated inflammatory programme, and induced a distinct metabolic-matrix response with coordinated transcript-usage changes. These biological findings remain exploratory because independent biological replicates were unavailable. CHART-seq provides a rapid, scalable route to full-length single-cell transcript profiling with gene-programme and candidate isoform resolution.

genomics↗

bioRxiv2021

Chord: Identifying Doublets in Single-Cell RNA Sequencing Data by an Ensemble Machine Learning Algorithm

High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of these methods have good performance in some datasets but lack stability in others; thus, it is difficult to regard a single method as the gold standard for each scenario, and it is a difficult and time-consuming task for researcher to choose the most appropriate software. To address these issues, we propose Chord which implements a machine learning algorithm that integrates multiple doublet detection methods. Chord had a higher accuracy and stability than the individual approaches on different datasets containing real and synthetic data. Moreover, Chord was designed with a modular architecture port, which has high flexibility and adaptability to the incorporation of any new tools. Chord is a general solution to the doublet detection problem.

bioinformatics↗

bioRxiv2019

CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing

Cell type identification is essential for single-cell RNA sequencing (scRNA-seq) studies that are currently transforming the life sciences. CHETAH (CHaracterization of cEll Types Aided by Hierarchical clustering) is an accurate cell type identification algorithm that is rapid and selective, including the possibility of intermediate or unassigned categories. Evidence for assignment is based on a classification tree of previously available scRNA-seq reference data and includes a confidence score based on the variance in gene expression per cell type. For cell types represented in the reference data, CHETAHs accuracy is as good as existing methods. Its specificity is superior when cells of an unknown type are encountered, such as malignant cells in tumor samples which it pinpoints as intermediate or unassigned. Although designed for tumor samples in particular, the use of unassigned and intermediate types is also valuable in other exploratory studies. This is exemplified in pancreas datasets where CHETAH highlights cell populations not well represented in the reference dataset, including cells with profiles that lie on a continuum between that of acinar and ductal cell types. Having the possibility of unassigned and intermediate cell types is pivotal for preventing misclassification and can yield important biological information for previously unexplored tissues.

bioinformatics↗

bioRxiv2021

Simultaneous Identification of Brain Cell Type and Lineage via Single Cell RNA Sequencing

Acquired mutations are sufficiently frequent such that the genome of a single cell offers a record of its history of cell divisions. Among more common somatic genomic alterations are loss of heterozygosity (LOH). Large LOH events are potentially detectable in single cell RNA sequencing (scRNA-seq) datasets as tracts of monoallelic expression for constitutionally heterozygous single nucleotide variants (SNVs) located among contiguous genes. We identified runs of monoallelic expression, consistent with LOH, uniquely distributed throughout the genome in single cell brain cortex transcriptomes of F1 hybrids involving different inbred mouse strains. We then phylogenetically reconstructed single cell lineages and simultaneously identified cell types by corresponding gene expression patterns. Our results are consistent with progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis. Compared to engineered recording systems, LOH events accumulate throughout the genome and across the lifetime of an organism, affording tremendous capacity for encoding lineage information and increasing resolution for later cell divisions. This approach can conceivably be computationally incorporated into scRNA-seq analysis and may be useful for organisms where genetic engineering is prohibitive, such as humans.

genomics↗

bioRxiv2024

Long-read single-cell RNA sequencing enables the study of cancer subclone-specific genotype and phenotype in chronic lymphocytic leukemia

Brutons tyrosine kinase (BTK) inhibitors are effective for the treatment of chronic lymphocytic leukemia (CLL) due to BTKs role in B cell survival and proliferation. Treatment resistance is most commonly caused by the emergence of the hallmark BTKC481S mutation that inhibits drug binding. In this study, we aimed to investigate whether the presence of additional CLL driver mutations in cancer subclones harboring a BTKC481S mutation accelerates subclone expansion. In addition, we sought to determine whether BTK-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we employ our recently published method (Qiao et al. 2024) that combines bulk DNA sequencing and single-cell RNA sequencing (scRNA-seq) data to genotype individual cells for the presence or absence of subclone-defining mutations. While the most common approach for scRNA-seq includes short-read sequencing, transcript coverage is limited due to the vast majority of the reads being concentrated at the priming end of the transcript. Here, we utilized MAS-seq, a long-read scRNAseq technology, to substantially increase transcript coverage across the entire length of the transcripts and expand the set of informative mutations to link cells to cancer subclones in six CLL patients who acquired BTKC481S mutations during BTK inhibitor treatment. We found that BTK-mutated subclones often acquire additional mutations in CLL driver genes, leading to faster subclone proliferation. When examining subclone-specific gene expression, we found that in one patient, BTK-mutated subclones are transcriptionally distinct from the rest of the malignant B cell population with an overexpression of CLL-relevant genes.

genomics↗

bioRxiv2021

A robust method of nuclei isolation for single-cell RNA sequencing of solid tissues from the plant genus Populus

Single-cell transcriptome analysis has been extensively applied in humans and animal models to uncover gene expression heterogeneity between the different cell types of a tissue or an organ. It demonstrated its capability to discover the key regulatory elements that determine cell fate during developmental programs such as brain or heart development. Single-cell analysis requires the isolation and labeling of the messenger RNA (mRNA) derived from each cell. These challenges were primarily addressed in mammals by developing microfluidic-based approaches. For plant species whose cells contain cell walls, these approaches have generally required the generation of isolated protoplasts. Many plant tissues secondary cell wall hinders enzymatic digestion required for individual protoplast isolation, resulting in an unequal representation of cell types in a protoplast population. This limitation is especially critical for cell types located in the inner layers of a tissue or the inner tissues of an organ. Consequently, single-cell RNA sequencing (scRNA-seq) studies using microfluidic approaches in plants have mainly been restricted to Arabidopsis roots, for which well-established procedures of protoplast isolation are available. Here we present a simple alternative approach to generating high-quality protoplasts from plant tissue by characterizing the mRNA extracted from individual nuclei instead of whole cells. We developed the protocol using two different plant materials with varying cellular complexity levels and cell-wall structure, Populus shoot apices, and more lignified stems. Using the 10x Genomics Chromium technology, we show that this procedure results in intact mRNA isolation and limited leakage, with a broad representation of individual cell transcriptomes.

plant biology↗

bioRxiv2025

Comprehensive Bulk and Single-Cell RNA Sequencing Uncovers Senescence-Associated Biomarkers in Therapeutic Mesenchymal Stem Cells

BackgroundMesenchymal stem cells (MSCs) hold great promise in cell therapy, but their effectiveness declines with repeated cell divisions due to senescence. Canines, sharing aging characteristics with humans, serve as a valuable model to study this process in a translational context. MethodsIn the present study, we performed an in-depth characterization of senescence in canine MSCs using a combination of morphological, molecular, and transcriptomic analyses. Early (P2) and late-passage (P6) canine MSCs were characterized using a combination of senescence-associated {beta}-galactosidase staining, cell cycle profiling, and both bulk and single-cell RNA sequencing to capture global transcriptional changes. ResultsBy employing a passage-based in vitro approach, the present study demonstrates that late-passage cells (P6) compared to early-passage cells (P2) exhibit hallmark features of senescence, including morphological alterations, elevated SA-{beta}-galactosidase activity, and considerable transcriptional changes. These changes were represented by significant upregulation of established senescence marker genes, alongside potential novel candidates and downregulation of genes associated with cell cycle progression and proliferation. Moreover, single-cell RNA sequencing uncovered heterogeneous distribution of senescent subpopulations, upregulation of SASP-related genes and reduced proliferation markers. ConclusionsOur findings demonstrate that combining classical markers with bulk and single-cell RNA sequencing facilitates senescent cell identification while improving quality control for clinical MSC samples.

cell biology↗

bioRxiv2026

scGSCA: gene-set differential co-expression analysis for single-cell RNA sequencing data

Understanding disease-associated pathway coordination requires distinguishing phenotype-related co-expression changes from differences across cellular contexts while accounting for cells sampled from the same subject. Bulk gene-set co-expression methods do not directly address the sparse counts, single-cell distributional noise, subject-level dependence, and cell-type heterogeneity of single-cell RNA sequencing (scRNA-seq) data. We developed single-cell gene-set co-expression analysis (scGSCA), a framework for pathway-level differential co-expression analysis in scRNA-seq data. scGSCA fits gene-wise zero-inflated negative binomial mixed models with subject-level random effects and computes gene-set co-expression statistics from quantile residual correlations. It provides an overall differential gene-set co-expression analysis and an interaction test to assess whether the co-expression pattern between two cell types varies by sample phenotype. We applied scGSCA to tumor-normal gastric, breast-subtype, and lung-skin comparisons with matched bulk and scRNA-seq data. The overall test identified signals not captured in bulk analysis, including Axon guidance in gastric cancer and estrogen-response, IL6-JAK-STAT3, and TNF/NF-{kappa}B pathways in breast cancer. The interaction test further identified co-expression contrasts across cell types. scGSCA extends gene-set co-expression analysis to single-cell data while retaining subject-level structure and cell-type-specific context. scGSCA is freely available as an open-source R package.

bioinformatics↗

bioRxiv2019

Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

Cell atlas projects and single-cell CRISPR screens hit the limits of current technology, as they require cost-effective profiling for millions of individual cells. To satisfy these enormous throughput requirements, we developed "single-cell combinatorial fluidic indexing" (scifi) and applied it to single-cell RNA sequencing. The resulting scifi-RNA-seq assay combines one-step combinatorial pre-indexing of single-cell transcriptomes with subsequent single-cell RNA-seq using widely available droplet microfluidics. Pre-indexing allows us to load multiple cells per droplet, which increases the throughput of droplet-based single-cell RNA-seq up to 15-fold, and it provides a straightforward way of multiplexing hundreds of samples in a single scifi-RNA-seq experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow, thereby enabling massive-scale scRNA-seq experiments for a broad range of applications ranging from population genomics to drug screens with scRNA-seq readout. We benchmarked scifi-RNA-seq on various human and mouse cell lines, and we demonstrated its feasibility for human primary material by profiling TCR activation in T cells.

genomics↗

bioRxiv2018

Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments

In single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.

genomics↗

bioRxiv2024

Single-cell RNA sequencing on formalin-fixed and paraffin-embedded (FFPE) tissue identified multi-ciliary cells in breast cancer

The purpose of this study was to evaluate the suitability of paraffin-embedded (FFPE) samples and fixed fresh (FF) samples for single-cell RNA sequencing (scRNAseq). To this end, we compared whether single-cell profiles from FFPE and matched FF tissue samples from one invasive carcinoma of no especial type carcinoma (invasive ductal carcinoma -IDC) and one invasive lobular carcinoma (ILC) were similar when captured using the common immunohistochemical (IHC) and immune features of the tumors. FF and FFPE tissue derived libraries showed high-quality parameters. The cell heterogeneity obtained was similar, although, there were some differences in the number of cells captured, but there were no specific cell populations captured exclusively by one of the two different approaches. The combined analysis of the four samples identified six types of epithelial cells (normal basal cells, four subpopulations of neoplastic cells in the IDC and one in the ILC), and different tumoral microenvironment populations. scRNAseq results from epithelial neoplastic cells were concordant with common IHC markers. In addition, after quantitative assessment of the whole slide images using QuPath, the proportion of immune cells identified by IHQ in FFPE sections were similar to those obtained by scRNAseq. Confirming the suitability of scRNAseq using FFPE tissue, this technique identified one subpopulation of neoplastic cells present only in the IDC, characterized by the expression of genes related with multi-ciliated cell (MCC) differentiation (FOXJ1, ROPN1L, TPPP3, CFAP45). IHC analysis of FOXJ1 expression confirmed the presence of positive cells only in the IDC in a similar proportion to that obtained by scRNAseq. Analysis of FOXJ1 in a cohort of 214 ER-positive invasive carcinomas demonstrated protein expression in at least 1% of cells in 33% of tumors, suggesting frequent focal MCC differentiation. Our results support the suitability of scRNAseq analysis using FFPE tissue and identified a subpopulation of neoplastic MCC in breast cancer.

cancer biology↗