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bioRxiv2024

Comparative Analysis of Commercial Single-Cell RNA Sequencing Technologies

This study evaluates ten commercially available single-cell RNA sequencing (scRNA-seq) approaches across four technology groups: Emulsion-based kits from 10x Genomics and Fluent Biosciences; Microwell-based kits from Becton Dickinson, Honeycomb Technologies and Singerlon Technologies; Combinatorial-indexing kits from Parse Biosciences and Scale Biosciences; and a Matrigel-based kit from Scipio Biosciences. Peripheral blood mononuclear cells (PBMCs) from a single donor were used to assess analytical performance. Key features such as sample compatibility, cost, and experimental duration were also compared. Notably, superior analytical performance was demonstrated by the Chromium Fixed RNA Profiling kit from 10x Genomics, which uniquely features probe hybridization for transcript detection. Additionally, the Rhapsody WTA kit from Becton Dickinson provided a cost-effective balance of performance and expense per cell. With a rich dataset of 218,154 cells, this work provides a basis for differentiating commercial scRNA-seq technologies, which is intended to facilitate the effective application and further methodological development of single cell transcriptomics.

molecular biology↗

bioRxiv2021

SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics

Spatial transcriptomics is revolutionising the study of single-cell RNA and tissue-wide cell heterogeneity, but few robust methods connecting spatially resolved cells to so-called marker genes from single-cell RNA sequencing, which generate significant insight gleaned from spatial methods, exist. Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA sequencing data for spatial transcriptomics approaches. SMaSH extracts robust and biologically well-motivated marker genes, which characterise the given data-set better than existing and limited computational approaches for global marker gene calculation.

bioinformatics↗

bioRxiv2019

Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks

Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present O_SCPLOWSCC_SCPLOWPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks (PPINs) that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted PPINs, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. O_SCPLOWSCC_SCPLOWPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, O_SCPLOWSCC_SCPLOWPPIN also identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.

bioinformatics↗

bioRxiv2023

demuxmix: Demultiplexing oligonucleotide-barcoded single-cell RNA sequencing data with regression mixture models

MotivationDroplet-based single-cell RNA sequencing (scRNA-seq) is widely used in biomedical research to interrogate the transcriptomes of single cells on a large scale. Pooling and processing cells from different samples together can reduce costs and batch effects. In order to pool cells, cells are often first labeled with hashtag oligonucleotides (HTOs). These HTOs are sequenced along with the cells RNA in the droplets and are subsequently used to computationally assign each droplet to its sample of origin, which is referred to as demultiplexing. Accurate demultiplexing is crucial and can be challenging due to background HTOs, low-quality cells/cell debris, and multiplets. ResultsA new demultiplexing method, demuxmix, based on negative binomial regression mixture models is introduced. The method implements two significant improvements. First, demuxmixs probabilistic classification framework provides error probabilities for droplet assignments that can be used to discard uncertain droplets and inform about the quality of the HTO data and the demultiplexing success. Second, demuxmix utilizes the positive association between detected genes in the RNA library and HTO counts to explain parts of the variance in the HTO data resulting in improved droplet assignments. The improved performance of demuxmix compared to existing demultiplexing methods is assessed on real and simulated data. Finally, the feasibility of accurately demultiplexing experimental designs where non-labeled cells are pooled with labeled cells is demonstrated. AvailabilityR/Bioconductor package demuxmix (https://doi.org/doi:10.18129/B9.bioc.demuxmix)

bioinformatics↗

bioRxiv2021

Detecting expressed cancer somatic mutations from single-cell RNA sequencing data

Identifying expressed somatic mutations directly from single-cell RNA sequencing (scRNA-seq) data is challenging but highly valuable. Computational methods have been attempted but no reliable methods have been reported to identify somatic mutations with high fidelity. We present RESA -- Recurrently Expressed SNV Analysis, a computational framework that identifies expressed somatic mutations from scRNA-seq data with high precision. We test RESA in multiple cancer cell line datasets, where RESA demonstrates average area under the curve (AUC) of 0.9 on independently held out test sets, and achieves average precision of 0.71 when evaluated by bulk whole exome, which is substantially higher than previous approaches. In addition, RESA detects a median of 201 mutations per cell, 50 times more than what was reported in experimental technologies with simultaneous expression and mutation profiling. Furthermore, applying RESA to scRNA-seq from a melanoma patient, we demonstrate that RESA recovers the known BRAF driver mutation of the sample and melanoma dominating mutational signatures, identifies mutation associated expression signatures, reveals nondriver perturbed and stage specific cancer hallmarks, and unveils the complex relationship between genomic and transcriptomic intratumor heterogeneity. Therefore, RESA could provide novel views in the study of intratumor heterogeneity and relate genetic alterations to transcriptional changes at single cell level.

bioinformatics↗

bioRxiv2023

Comparative Analysis of Association Networks Using Single-Cell RNA Sequencing Data Reveals Perturbation-Relevant Gene Signatures

Single-cell RNA sequencing (scRNA-seq) data has elevated our understanding of systemic perturbations to organismal physiology at the individual cell level. However, despite the rich information content of scRNA-seq data, the relevance of genes to a perturbation is still commonly assessed through differential expression analysis. This approach provides a one-dimensional perspective of the transcriptomic landscape, risking the oversight of tightly controlled genes characterized by modest changes in expression but with profound downstream effects. We present GENIX (Gene Expression Network Importance eXamination), a novel platform for constructing gene association networks, equipped with an innovative network-based comparative model to uncover condition-relevant genes. To demonstrate the effectiveness of GENIX, we analyze influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) collected from recovered COVID-19 patients, shedding light on the mechanistic underpinnings of gender differences. Our methodology offers a promising avenue to identify genes relevant to perturbation responses in biological systems, expanding the scope of response signature discovery beyond differential gene expression analysis. HIGHLIGHTSO_LIConventional methods used to identify perturbation-relevant genes in scRNA-seq data rely on differential expression analysis, susceptible to overlooking essential genes. C_LIO_LIGENIX leverages cell-type-specific inferred gene association networks to identify condition-relevant genes and gene programs, irrespective of their specific expression alterations. C_LIO_LIGENIX provides insight into the gene-regulatory response to the influenza vaccine in naive and recovered COVID-19 patients, expanding on previously observed gender-specific differences. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/556872v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1837d3eorg.highwire.dtl.DTLVardef@1937860org.highwire.dtl.DTLVardef@c40114org.highwire.dtl.DTLVardef@22d3b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Lipid Nanoparticle Barcoding for Multiplexed Single-Cell RNA Sequencing

Sample multiplexing is an emerging method in single-cell RNA sequencing (scRNA-seq) that addresses high costs and batch effects. Current multiplexing schemes use DNA labels to barcode cell samples but are limited in their stability and extent of labeling across heterogeneous cell populations. Here, we introduce Nanocoding using lipid nanoparticles (LNPs) for high barcode labeling density in multiplexed scRNA-seq. LNPs reduce dependencies on cell surface labeling mechanisms due to multiple controllable means of cell uptake, amplifying barcode loading 10-100-fold and allowing both protection and efficient release by dissolution. In cultured cell lines and heterogeneous cells from tissue digests, Nanocoding occurs in 40 minutes with stability after sample mixing and requires only commercially available reagents without novel chemical modifications. In spleen digests, 6-plex barcoded samples show minimal unlabeled cells, with all barcodes giving bimodal count distributions. Challenging samples containing lipid-rich debris and heterogeneous cells from adipose tissue of obese rodents show more than 95% labeling with all known subtypes identified. Using Nanocoding, we investigate gene expression changes related to aging in adipose tissue, profiling cells that could not be readily identified with current direct conjugate methods using lipid or antibody conjugates. This ease of generating and tuning these constructs may afford efficient and robust whole-sample multiplexing with minimal sample crosstalk.

bioengineering↗

bioRxiv2026

StabCell: Stability selection for clustering and marker detection in single-cell RNA sequencing

MotivationConventional pipelines for differential expression analysis in single-cell RNA sequencing (scRNA-seq) data first cluster individual cells and then test for differentially expressed genes between the resulting clusters. Using the same data for clustering and testing, however, poses a selective inference problem and can result in overconfidence in differences that may not reflect true biological variation. ResultsWe introduce StabCell, a stability selection framework which integrates clustering and detection of differentially expressed marker genes. By repeatedly performing clustering and differential expression analysis on complementary random subsamples, StabCell assesses clustering and marker stability, yielding a stable clustering with sets of stable marker genes. In simulations, we demonstrate that StabCell provides approximate empirical per-family error rate (PFER) control, selecting fewer false positive marker genes compared with conventional approaches, especially in cases with low signal-to-noise ratio and low sequencing depth. Applying the method to a cell differentiation dataset from induced pluripotent stem cells (IPSCs) to cardiomyocytes reveals that meaningful marker genes are consistently among the top-ranked genes. These results indicate that StabCell can improve the interpretability and robustness of scRNA-seq analyses. Availability and implementationAn implementation of StabCell in the statistical programming language R is available at https://github.com/LuckyLueck/StabCell. Code to reproduce the results is available at https://github.com/LuckyLueck/StabCell_paper.

bioinformatics↗

bioRxiv2019

Microbial single-cell RNA sequencing by split-pool barcoding

Single-cell RNA-sequencing (scRNA-seq) has become an essential tool for characterizing multi-celled eukaryotic systems but current methods are not compatible with bacteria. Here, we introduce microSPLiT, a low cost and high-throughput scRNA-seq method that works for gram-negative and gram-positive bacteria and can resolve transcriptional states that remain hidden at a population level. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled from different growth stages, creating a detailed atlas of changes in metabolism and lifestyle. We not only retrieve detailed gene expression profiles associated with known but rare states such as competence and PBSX prophage induction, but also identify novel and unexpected gene expression states including heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. microSPLiT empowers high-throughput analysis of gene expression in complex bacterial communities.

bioengineering↗

bioRxiv2020

Normalizing single-cell RNA sequencing data with internal spike-in-like genes

Normalization with respect to sequencing depth is a crucial step in single-cell RNA sequencing preprocessing. Most methods normalize data using the whole transcriptome based on the assumption that the majority of transcriptome remains constant and are unable to detect drastic changes of the transcriptome. Here, we develop an algorithm based on a small fraction of constantly expressed genes as internal spike-ins to normalize single cell RNA sequencing data. We demonstrate that the transcriptome of single cells may undergo drastic changes in several case study datasets and accounting for such heterogeneity by ISnorm improves the performance of downstream analyzes.

bioinformatics↗

bioRxiv2026

A framework for designing splice-junction experiments in deep 3' single-cell RNA sequencing

Alternative splicing is cell-type-specific and disease-relevant, but single-cell RNA sequencing is optimized for gene-level quantification, and how sequencing depth governs splice-junction recovery in the dominant 3'-biased chemistries has not been quantified, so experiments cannot be designed to a depth target and datasets cannot be interpreted against expected recovery. We generated deep single-cell RNA sequencing of human airway epithelial cells (12 samples, 102 to 803 million uniquely mapped reads per sample) and benchmarked junction recovery in five annotated samples against matched bulk RNA sequencing from a 190-donor cohort. Junction detection did not saturate with depth, and validity was governed by read support rather than annotation status. Recovering 90% of the observed full-depth junction complement required approximately 52,250 reads per cell, several-fold more than gene-level analysis. Quantifying splicing was more depth-limited than detecting it, and rare cell types were limited by cell number rather than depth. Against matched bulk, single-cell recall plateaued at a practical ceiling set by sample size and sparse sampling, and was independent of distance from the 3' end. These results establish a framework for designing splicing experiments in deep 3'-biased single-cell RNA sequencing, and provide an airway-epithelial dataset with matched bulk for benchmarking splicing methods.

bioinformatics↗

bioRxiv2021

A comparison of data integration methods for single-cell RNA sequencing of cancer samples

Tumours are routinely profiled with single-cell RNA sequencing (scRNA-seq) to characterize their diverse cellular ecosystems of malignant, immune, and stromal cell types. When combining data from multiple samples or studies, batch-specific technical variation can confound biological signals. However, scRNA-seq batch integration methods are often not designed for, or benchmarked, on datasets containing cancer cells. Here, we compare 5 data integration tools applied to 171,206 cells from 5 tumour scRNA-seq datasets. Based on our results, STACAS and fastMNN are the most suitable methods for integrating tumour datasets, demonstrating robust batch effect correction while preserving relevant biological variability in the malignant compartment. This comparison provides a framework for evaluating how well single-cell integration methods correct for technical variability while preserving biological heterogeneity of malignant and non-malignant cell populations.

bioinformatics↗

bioRxiv2023

A mannitol-based buffer improves single-cell RNA sequencing of high-salt marine cells

Single-cell RNA sequencing (scRNA-seq) enables discovery of novel cell states by transcriptomic profiling with minimal prior knowledge, making it useful for studying non-model organisms. For most marine organisms, however, cells are viable at a higher salinity than is compatible with scRNA-seq, impacting data quality and cell representation. We show that a low-salinity phosphate buffer supplemented with D-mannitol (PBS-M) enables higher-quality scRNA-seq of blood cells from the tunicate Ciona robusta. Using PBS-M reduces cell death and ambient mRNA, revealing cell states not otherwise detected. This simple protocol modification could enable or improve scRNA-seq for the majority of marine organisms.

genomics↗

bioRxiv2025

EnrichSci: Transcript-guided Targeted Cell Enrichment for Scalable Single-Cell RNA Sequencing

Large-scale single-cell atlas efforts have revealed many aging-or disease-associated cell types, yet these populations are often underrepresented in heterogeneous tissues, limiting detailed molecular and dynamic analyses. To address this, we developed EnrichSci--a highly scalable, microfluidics-free platform that combines Hybridization Chain Reaction RNA FISH with combinatorial indexing to profile single -nucleus transcriptomes of targeted cell types with full gene-body coverage. When applied to profile oligodendrocytes in the aging mouse brain, EnrichSci uncovered aging-associated molecular dynamics across distinct oligodendrocyte subtypes, revealing both shared and subtype-specific gene expression changes. Additionally, we identified aging-associated exon-level signatures that are missed by conventional gene-level analyses, highlighting post-transcriptional regulation as a critical dimension of cell-state dynamics in aging. By coupling transcript-guided enrichment with a scalable sequencing workflow, EnrichSci provides a versatile approach to decode dynamic regulatory landscapes in diverse cell types from complex tissues.

genomics↗

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↗

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↗