Extensive folding variability between homologous chromosomes in mammalian cells.

Genetic variation and 3D chromatin structure have major roles in gene regulation. Due to challenges in mapping chromatin conformation with haplotype-specific resolution, the effects of genetic sequence variation on 3D genome structure and gene expression imbalance remain understudied. Here, we applied Genome Architecture Mapping (GAM) to a hybrid mouse embryonic stem cell (mESC) line with high density of single nucleotide polymorphisms (SNPs).

Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition.

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential.

Oncogenic signaling pathways in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is the most common (∼90% cases) pancreatic neoplasm and one of the most lethal cancer among all malignances. PDAC harbor aberrant oncogenic signaling that may result from the multiple genetic and epigenetic alterations such as the mutation in driver genes (KRAS, CDKN2A, p53), genomic amplification of regulatory genes (MYC, IGF2BP2, ROIK3), deregulation of chromatin-modifying proteins (HDAC, WDR5) among others. A key event is the formation of Pancreatic Intraepithelial Neoplasia (PanIN) that often results from the activating mutation in KRAS.

A single-cell, time-resolved profiling of mucociliary epithelium reveals nonhierarchical model of development.

The specialized cell types of the mucociliary epithelium (MCE) lining the respiratory tract enable continuous airway clearing, with its defects leading to chronic respiratory diseases. The molecular mechanisms driving cell fate acquisition and temporal specialization during mucociliary epithelial development remain largely unknown. Here, we profile the developing MCE from pluripotent to mature stages by single-cell transcriptomics, identifying multipotent early epithelial progenitors that execute multilineage cues before specializing into late-stage ionocytes and goblet and basal cells.

Matching queried single-cell open-chromatin profiles to large pools of single-cell transcriptomes and epigenomes for reference supported analysis.

The true benefits of large single-cell transcriptome and epigenome data sets can be realized only with the development of new approaches and search tools for annotating individual cells. Matching a single-cell epigenome profile to a large pool of reference cells remains a major challenge. Here, we present scEpiSearch, which enables searching, comparison, and independent classification of single-cell open-chromatin profiles against a large reference of single-cell expression and open-chromatin data sets.

PPARγ lipodystrophy mutants reveal intermolecular interactions required for enhancer activation.

Peroxisome proliferator-activated receptor γ (PPARγ) is the master regulator of adipocyte differentiation, and mutations that interfere with its function cause lipodystrophy. PPARγ is a highly modular protein, and structural studies indicate that PPARγ domains engage in several intra- and inter-molecular interactions. How these interactions modulate PPARγ's ability to activate target genes in a cellular context is currently poorly understood.

Cell Competition in Carcinogenesis.

The majority of human cancers evolve over time through the stepwise accumulation of somatic mutations followed by clonal selection akin to Darwinian evolution. However, the in-depth mechanisms that govern clonal dynamics and selection remain elusive, particularly during the earliest stages of tissue transformation. Cell competition (CC), often referred to as 'survival of the fittest' at the cellular level, results in the elimination of less fit cells by their more fit neighbors supporting optimal organism health and function.

TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data.

Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach 'TENET' to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets.

Predicting gene regulatory networks from cell atlases.

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type-specific regulons.

Publisher Correction: Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Transcriptional Dynamics of Hepatic Sinusoid-Associated Cells After Liver Injury.

Background And Aims: Hepatic sinusoidal cells are known actors in the fibrogenic response to injury. Activated hepatic stellate cells (HSCs), liver sinusoidal endothelial cells, and Kupffer cells are responsible for sinusoidal capillarization and perisinusoidal matrix deposition, impairing vascular exchange and heightening the risk of advanced fibrosis. While the overall pathogenesis is well understood, functional relations between cellular transitions during fibrogenesis are only beginning to be resolved.

Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression.

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation.

Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition.

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening, we identify CD44 as a marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta-gonad-mesonephros (AGM) region.

Single-Cell Tagged Reverse Transcription (STRT-Seq).

Single-cell RNA sequencing (scRNA-seq) has become an established approach to profile entire transcriptomes of individual cells from different cell types, tissues, species, and organisms. Single-cell tagged reverse transcription sequencing (STRT-seq) is one of the early single-cell methods which utilize 5' tag counting of transcripts. STRT-seq performed on microfluidics Fluidigm C1 platform (STRT-C1) is a flexible scRNA-seq approach that allows for accurate, sensitive and importantly molecular counting of transcripts at single-cell level.

Comparative analysis of sequencing technologies for single-cell transcriptomics.

Single-cell RNA-seq technologies require library preparation prior to sequencing. Here, we present the first report to compare the cheaper BGISEQ-500 platform to the Illumina HiSeq platform for scRNA-seq. We generate a resource of 468 single cells and 1297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on two cell lines with RNA spike-ins.

A rapid and robust method for single cell chromatin accessibility profiling.

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting.

Distinctive features of lincRNA gene expression suggest widespread RNA-independent functions.

Eukaryotic genomes produce RNAs lacking protein-coding potential, with enigmatic roles. We integrated three approaches to study large intervening noncoding RNA (lincRNA) gene functions. First, we profiled mouse embryonic stem cells and neural precursor cells at single-cell resolution, revealing lincRNAs expressed in specific cell types, cell subpopulations, or cell cycle stages.

Single cell transcriptomics of pluripotent stem cells: reprogramming and differentiation.

Single-cell transcriptomics serves as a powerful tool to identify cell states within populations of cells, and to dissect underlying heterogeneity at high resolution. Single-cell transcriptomics on pluripotent stem cells has provided new insights into cellular variation, subpopulation structures and the interplay of cell cycle with pluripotency. The single-cell perspective has helped to better understand gene regulation and regulatory networks during exit from pluripotency, cell-fate determination as well as molecular mechanisms driving cellular reprogramming of somatic cells to induced pluripotent stage.

Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression.

Polycomb repressive complexes (PRCs) are important histone modifiers, which silence gene expression; yet, there exists a subset of PRC-bound genes actively transcribed by RNA polymerase II (RNAPII). It is likely that the role of Polycomb repressive complex is to dampen expression of these PRC-active genes. However, it is unclear how this flipping between chromatin states alters the kinetics of transcription.

SC3: consensus clustering of single-cell RNA-seq data.

Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3).

Power analysis of single-cell RNA-sequencing experiments.

Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing.

Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation.

Background: Differentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells.

Results: We perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells.