Single-cell Transcriptomic Studies Research Articles

A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice

AbstractHematopoietic stem and progenitor cells (HSPCs) maintain the adult blood system, and their dysregulation causes a multitude of diseases. However, the differentiation journeys toward specific hematopoietic lineages remain ill defined, and system-wide disease interpretation remains challenging. Here, we have profiled 44 802 mouse bone marrow HSPCs using single-cell RNA sequencing to provide a comprehensive transcriptional landscape with entry points to 8 different blood lineages (lymphoid, megakaryocyte, erythroid, neutrophil, monocyte, eosinophil, mast cell, and basophil progenitors). We identified a common basophil/mast cell bone marrow progenitor and characterized its molecular profile at the single-cell level. Transcriptional profiling of 13 815 HSPCs from the c-Kit mutant (W41/W41) mouse model revealed the absence of a distinct mast cell lineage entry point, together with global shifts in cell type abundance. Proliferative defects were accompanied by reduced Myc expression. Potential compensatory processes included upregulation of the integrated stress response pathway and downregulation of proapoptotic gene expression in erythroid progenitors, thus providing a template of how large-scale single-cell transcriptomic studies can bridge between molecular phenotypes and quantitative population changes.

Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network

Cell-to-cell variation in gene expression and the propagation of such variation (PoV or "noise propagation") from one gene to another in the gene network, as reflected by gene-gene correlation across single cells, are commonly observed in single-cell transcriptomic studies and can shape the phenotypic diversity of cell populations. While gene network "rewiring" is known to accompany cellular adaptation to different environments, how PoV changes between environments and its underlying regulatory mechanisms are less understood. Here, we systematically explored context-dependent PoV among genes in human macrophages, utilizing different cytokines as natural perturbations of multiple molecular parameters that may influence PoV. Our single-cell, epigenomic, computational, and stochastic simulation analyses reveal that environmental adaptation can tune PoV to potentially shape cellular heterogeneity by changing parameters suchas the degree of phosphorylation and transcription factor-chromatin interactions. This quantitative tuning of PoV may be a widespread, yet underexplored, property of cellular adaptation to distinct environments.

Open questions: A rose is a rose is a rose - or not?

Open questions: A rose is a rose is a rose - or not?

From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing

Single-cell RNA-seq mammalian transcriptome studies are at an early stage in uncovering cell-to-cell variation in gene expression, transcript processing and editing, and regulatory module activity. Despite great progress recently, substantial challenges remain, including discriminating biological variation from technical noise. Here we apply the SMART-seq single-cell RNA-seq protocol to study the reference lymphoblastoid cell line GM12878. By using spike-in quantification standards, we estimate the absolute number of RNA molecules per cell for each gene and find significant variation in total mRNA content: between 50,000 and 300,000 transcripts per cell. We directly measure technical stochasticity by a pool/split design and find that there are significant differences in expression between individual cells, over and above technical variation. Specific gene coexpression modules were preferentially expressed in subsets of individual cells, including one enriched for mRNA processing and splicing factors. We assess cell-to-cell variation in alternative splicing and allelic bias and report evidence of significant differences in splice site usage that exceed splice variation in the pool/split comparison. Finally, we show that transcriptomes from small pools of 30–100 cells approach the information content and reproducibility of contemporary RNA-seq from large amounts of input material. Together, our results define an experimental and computational path forward for analyzing gene expression in rare cell types and cell states.

SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization

Motivation: Recent transcriptome studies have revealed that total transcript numbers vary by cell type and condition; therefore, the statistical assumptions for single-cell transcriptome studies must be revisited. SAMstrt is an extension code for SAMseq, which is a statistical method for differential expression, to enable spike-in normalization and statistical testing based on the estimated absolute number of transcripts per cell for single-cell RNA-seq methods.Availability and Implementation: SAMstrt is implemented on R and available in github (https://github.com/shka/R-SAMstrt).Contact: shintaro.katayama@ki.seSupplementary Information: Supplementary data are available at Bioinformatics online.