Single-cell RNA Sequencing Studies Research Articles

Detection and removal of barcode swapping in single-cell RNA-seq data

Barcode swapping results in the mislabelling of sequencing reads between multiplexed samples on patterned flow-cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays; however, the severity and consequences of barcode swapping remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in two plate-based single-cell RNA-sequencing datasets. We found that approximately 2.5% of reads were mislabelled between samples on the HiSeq 4000, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA-sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10x Genomics experiments, allowing the continued use of cutting-edge sequencing machines for these assays.

SAVER: gene expression recovery for single-cell RNA sequencing.

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of lowly and moderately expressed genes which hinders downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for UMI-based scRNA-seq data that borrows information across genes and cells to obtain accurate expression estimates for all genes.

Single cell clustering based on cell-pair differentiability correlation and variance analysis.

The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies. We propose a new cell similarity measure based on cell-pair differentiability correlation, which is derived from gene differential pattern among all cell pairs. Through plugging into the framework of hierarchical clustering with this new measure, we further develop a variance analysis based clustering algorithm 'Corr' that can determine cluster number automatically and identify cell types accurately. The robustness and superiority of the proposed algorithm are compared with representative algorithms: shared nearest neighbor (SNN)-Cliq and several other state-of-the-art clustering methods, on many benchmark or real single cell RNA-sequencing datasets in terms of both internal criteria (clustering number and accuracy) and external criteria (purity, adjusted rand index, F1-measure). Moreover, differentiability vector with our new measure provides a new means in identifying potential biomarkers from cancer related single cell datasets even with strong noise. Prognosis analyses from independent datasets of cancers confirmed the effectiveness of our 'Corr' method. The source code (Matlab) is available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/Corr--SourceCodes.zip. Supplementary data are available at Bioinformatics online.

Medical bioinformatics in melanoma.

Bioinformatic insights from next-generation sequencing has been integral in understanding melanoma biology, resistance to treatment and provided new avenues for melanoma treatment. Whole-genome sequencing, whole-exome sequencing and RNA sequencing has redefined the molecular classification of melanoma, revealed distinct genetic aberrations that define clinical subtypes of melanoma and uncovered the diverse heterogeneity that resides in an individual tumor. In this review, we will summarize the recent whole-genome study that catalogs the genomic landscape across many melanoma subtypes, the single-cell RNA sequencing studies that interrogates tumor heterogeneity and the personalized vaccine approaches to melanoma treatment. Whole-genome sequencing of diverse subtypes of melanoma revealed acral and mucosal subtypes to have a different genomic landscape compared with cutaneous melanoma. Acral and mucosal melanomas are characterized by low mutation burden and high structural variants. Single-cell RNA sequencing revealed high intratumoral heterogeneity and the existence of rare intrinsic drug-resistant populations. Lastly, vaccination against tumor neoantigens could be a potential personalized medicine therapy for melanoma patients. In summary, bioinformatics research is deeply ingrained in all aspects of melanoma research and will continue to blossom together for many years to come.

Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data.

Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single-cell RNA-sequencing (RNA-seq) studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets (MGSs) in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at www.neuroexpresso.org.

Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes

Single cell RNA sequencing (scRNAseq) technique is becoming increasingly popular for unbiased and high-resolutional transcriptome analysis of heterogeneous cell populations. Despite its many advantages, scRNAseq, like any other genomic sequencing technique, is susceptible to the influence of confounding effects. Controlling for confounding effects in scRNAseq data is a crucial step for accurate downstream analysis. Here, we present a novel statistical method, which we refer to as scPLS (single cell partial least squares), for robust and accurate inference of confounding effects. scPLS takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. With extensive simulations and comparisons with other methods, we demonstrate the effectiveness of scPLS. Finally, we apply scPLS to analyze two scRNAseq data sets to illustrate its benefits in removing technical confounding effects as well as for removing cell cycle effects.

Inflammatory Ly6Chi monocytes and their conversion to M2 macrophages drive atherosclerosis regression.

Atherosclerosis is a chronic inflammatory disease, and developing therapies to promote its regression is an important clinical goal. We previously established that atherosclerosis regression is characterized by an overall decrease in plaque macrophages and enrichment in markers of alternatively activated M2 macrophages. We have now investigated the origin and functional requirement for M2 macrophages in regression in normolipidemic mice that received transplants of atherosclerotic aortic segments. We compared plaque regression in WT normolipidemic recipients and those deficient in chemokine receptors necessary to recruit inflammatory Ly6Chi (Ccr2-/- or Cx3cr1-/-) or patrolling Ly6Clo (Ccr5-/-) monocytes. Atherosclerotic plaques transplanted into WT or Ccr5-/- recipients showed reduced macrophage content and increased M2 markers consistent with plaque regression, whereas plaques transplanted into Ccr2-/- or Cx3cr1-/- recipients lacked this regression signature. The requirement of recipient Ly6Chi monocyte recruitment was confirmed in cell trafficking studies. Fate-mapping and single-cell RNA sequencing studies also showed that M2-like macrophages were derived from newly recruited monocytes. Furthermore, we used recipient mice deficient in STAT6 to demonstrate a requirement for this critical component of M2 polarization in atherosclerosis regression. Collectively, these results suggest that continued recruitment of Ly6Chi inflammatory monocytes and their STAT6-dependent polarization to the M2 state are required for resolution of atherosclerotic inflammation and plaque regression.

Hierarchical Specification of Pruriceptors by Runt-Domain Transcription Factor Runx1.

The somatic sensory neurons in dorsal root ganglia (DRG) detect and transmit a diverse array of sensory modalities, such as pain, itch, cold, warm, touch, and others. Recent genetic and single-cell RNA sequencing studies have revealed a group of DRG neurons that could be particularly relevant for acute and chronic itch information transmission. They express the natriuretic peptide type B (NPPB), as well as a cohort of receptors and neuropeptides that have been implicated in chronic itch manifestation, including the interleukin-31 receptor A (IL-31ra) and its coreceptor oncostatin M receptor (Osmr), the cysteinyl leukotriene receptor 2 (Cysltr2), somatostatin, and neurotensin. However, how these neurons are generated during development remains unclear. Here we report that Runx1 is required to establish all these molecular features of NPPB+ neurons. We further show that while early embryonic Runx1 activity is required for the formation of NPPB+ cells, at later stages Runx1 switches to a genetic repressor and thus its downregulation becomes a prerequisite for the proper development of these pruriceptors. This mode by Runx1 is analogous to that in controlling another group of pruriceptors that specifically express the chloroquine receptor MrgprA3. Finally, behavioral studies using both sexes of mice revealed marked deficits in processing acute and chronic itch in Runx1 conditional knock-out mice, possibly attributable to impaired development of various pruriceptors.SIGNIFICANCE STATEMENT Our studies reveal a generalized control mode by Runx1 for pruriceptor development and consolidate a hierarchical control mechanism for the formation of sensory neurons transmitting distinct modalities. Among dorsal root ganglion neurons that initially express the neurotrophin receptor TrkA, Runx1 is necessary for the proper development of those neurons that innervate tissues derived from the ectoderm such as skin epidermis and hair follicles. These Runx1-dependent cutaneous sensory neurons are then divided into two groups based on persistent or transient Runx1 expression. The Runx1-persistent group is involved in transmitting mechanical and thermal information, whereas the Runx1-transient group transmits pruriceptive information. Such hierarchical control mechanisms may provide a developmental solution for the formation of sensory circuits that transmit distinct modalities.

Single-cell RNA-sequencing: The future of genome biology is now

ABSTRACTGenome-wide single-cell analysis represents the ultimate frontier of genomics research. In particular, single-cell RNA-sequencing (scRNA-seq) studies have been boosted in the last few years by an explosion of new technologies enabling the study of the transcriptomic landscape of thousands of single cells in complex multicellular organisms. More sensitive and automated methods are being continuously developed and promise to deliver better data quality and higher throughput with less hands-on time. The outstanding amount of knowledge that is going to be gained from present and future studies will have a profound impact in many aspects of our society, from the introduction of truly tailored cancer treatments, to a better understanding of antibiotic resistance and host-pathogen interactions; from the discovery of the mechanisms regulating stem cell differentiation to the characterization of the early event of human embryogenesis.

Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.

BackgroundDifferentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question.ResultsWe present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved.ConclusionsSingle-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0683-4) contains supplementary material, which is available to authorized users.