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). GAM resolved haplotype-specific 3D genome structures with high sensitivity, revealing extensive allelic differences in chromatin compartments, topologically associating domains (TADs), long-range enhancer-promoter contacts, and CTCF loops. Architectural differences often coincide with allele-specific differences in gene expression, mediated by Polycomb repression. We show that histone genes are expressed with allelic imbalance in mESCs, are involved in haplotype-specific chromatin contact marked by H3K27me3, and are targets of Polycomb repression through conditional knockouts of Ezh2 or Ring1b. Our work reveals highly distinct 3D folding structures between homologous chromosomes, and highlights their intricate connections with allelic gene expression.

CRISPR-Cas12a-integrated transgenes in genomic safe harbors retain high expression in human hematopoietic iPSC-derived lineages and primary cells.

Discovery of genomic safe harbor sites (SHSs) is fundamental for multiple transgene integrations, such as reporter genes, chimeric antigen receptors (CARs), and safety switches, which are required for safe cell products for regenerative cell therapies and immunotherapies. Here we identified and characterized potential SHS in human cells. Using the CRISPR-MAD7 system, we integrated transgenes at these sites in induced pluripotent stem cells (iPSCs), primary T and natural killer (NK) cells, and Jurkat cell line, and demonstrated efficient and stable expression at these loci. Subsequently, we validated the differentiation potential of engineered iPSC toward CD34+ hematopoietic stem and progenitor cells (HSPCs), lymphoid progenitor cells (LPCs), and NK cells and showed that transgene expression was perpetuated in these lineages. Finally, we demonstrated that engineered iPSC-derived NK cells retained expression of a non-virally integrated anti-CD19 CAR, suggesting that several of the investigated SHSs can be used to engineer cells for adoptive immunotherapies.

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. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.

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. Mutated KRAS can direct a variety of signaling pathways and modulate downstream targets including MYC, which play an important role in cancer progression. In this review, we discuss recent literature shedding light on the origins of PDAC from the perspective of major oncogenic signaling pathways. We highlight how MYC directly and indirectly, with cooperation with KRAS, affect epigenetic reprogramming and metastasis. Additionally, we summarize the recent findings from single cell genomic approaches that highlight heterogeneity in PDAC and tumor microenvironment, and provide molecular avenues for PDAC treatment in the future.

A single-cell, time-resolved profiling of Xenopus 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 Xenopus 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. Combining in silico lineage inference, in situ hybridization, and single-cell multiplexed RNA imaging, we capture the initial bifurcation into early epithelial and multiciliated progenitors and chart cell type emergence and fate progression into specialized cell types. Comparative analysis of nine airway atlases reveals an evolutionary conserved transcriptional module in ciliated cells, whereas secretory and basal types execute distinct function-specific programs across vertebrates. We uncover a continuous nonhierarchical model of MCE development alongside a data resource for understanding respiratory biology.

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. Across performance benchmarks, scEpiSearch outperformed multiple methods in accuracy of search and low-dimensional coembedding of single-cell profiles, irrespective of platforms and species. Here we also demonstrate the unconventional utilities of scEpiSearch by applying it on single-cell epigenome profiles of K562 cells and samples from patients with acute leukaemia to reveal different aspects of their heterogeneity, multipotent behavior, and dedifferentiated states. Applying scEpiSearch on our single-cell open-chromatin profiles from embryonic stem cells (ESCs), we identified ESC subpopulations with more activity and poising for endoplasmic reticulum stress and unfolded protein response. Thus, scEpiSearch solves the nontrivial problem of amalgamating information from a large pool of single cells to identify and study the regulatory states of cells using their single-cell epigenomes.

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. Here we take advantage of two previously uncharacterized lipodystrophy mutations, R212Q and E379K, that are predicted to interfere with the interaction of the hinge of PPARγ with DNA and with the interaction of PPARγ ligand binding domain (LBD) with the DNA-binding domain (DBD) of the retinoid X receptor, respectively. Using biochemical and genome-wide approaches we show that these mutations impair PPARγ function on an overlapping subset of target enhancers. The hinge region-DNA interaction appears mostly important for binding and remodelling of target enhancers in inaccessible chromatin, whereas the PPARγ-LBD:RXR-DBD interface stabilizes the PPARγ:RXR:DNA ternary complex. Our data demonstrate how in-depth analyses of lipodystrophy mutants can unravel molecular mechanisms of PPARγ function.

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. Alternatively, CC may allow an uncontrolled expansion of super-fit cancer cells to outcompete their less fit neighbors thereby fueling tumorigenesis. Recent research discussed herein highlights the various non-cell-autonomous principles, including interclonal competition and cancer microenvironment competition supporting the ability of a tumor to progress from the initial stages to tissue colonization. In addition, we extend current insights from CC-mediated clonal interactions and selection in normal tissues to better comprehend those factors that contribute to cancer development.

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. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.

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. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

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. At single-cell resolution, we here explored the heterogeneity of individual cell types and dissected their transitions and crosstalk during fibrogenesis. APPROACH AND RESULTS: We applied single-cell transcriptomics to map the heterogeneity of sinusoid-associated cells in healthy and injured livers and reconstructed the single-lineage HSC trajectory from pericyte to myofibroblast. Stratifying each sinusoidal cell population by activation state, we projected shifts in sinusoidal communication upon injury. Weighted gene correlation network analysis of the HSC trajectory led to the identification of core genes whose expression proved highly predictive of advanced fibrosis in patients with nonalcoholic steatohepatitis (NASH). Among the core members of the injury-repressed gene module, we identified plasmalemma vesicle-associated protein (PLVAP) as a protein amply expressed by mouse and human HSCs. PLVAP expression was suppressed in activated HSCs upon injury and may hence define hitherto unknown roles for HSCs in the regulation of microcirculatory exchange and its breakdown in chronic liver disease. CONCLUSIONS: Our study offers a single-cell resolved account of drug-induced injury of the mammalian liver and identifies key genes that may serve important roles in sinusoidal integrity and as markers of advanced fibrosis in human NASH.

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. We identify molecular markers that are predictive of differentiation efficiency of individual lines, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.

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. This allows us to provide a detailed phenotypical and transcriptional profile of CD44-positive arterial endothelial cells from which HSPCs emerge. They are characterized with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists, a downregulation of genes related to glycolysis and the TCA cycle, and a lower rate of cell cycle. Moreover, we demonstrate that by inhibiting the interaction between CD44 and its ligand hyaluronan, we can block EHT, identifying an additional regulator of HSPC development.

The Malaria Cell Atlas: Single parasite transcriptomes across the complete Plasmodium life cycle.

Malaria parasites adopt a remarkable variety of morphological life stages as they transition through multiple mammalian host and mosquito vector environments. We profiled the single-cell transcriptomes of thousands of individual parasites, deriving the first high-resolution transcriptional atlas of the entire Plasmodium berghei life cycle. We then used our atlas to precisely define developmental stages of single cells from three different human malaria parasite species, including parasites isolated directly from infected individuals. The Malaria Cell Atlas provides both a comprehensive view of gene usage in a eukaryotic parasite and an open-access reference dataset for the study of malaria parasites.

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. Herein, I describe the STRT-C1 method and the steps involved in capturing 96 cells across C1 microfluidics chip, cDNA synthesis, and preparing single-cell libraries for Illumina short-read sequencing.

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. We sequence these libraries on both platforms using single- and paired-end reads. The platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardized scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms.

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. We demonstrate that our method works robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors.

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. Second, we assembled a transcriptome-wide atlas of nuclear lincRNA degradation by identifying targets of the exosome cofactor Mtr4. Third, we developed a reversible depletion system to separate the role of a lincRNA gene from that of its RNA. Our approach distinguished lincRNA loci functioning in trans from those modulating local gene expression. Some genes express stable and/or abundant lincRNAs in single cells, but many prematurely terminate transcription and produce lincRNAs rapidly degraded by the nuclear exosome. This suggests that besides RNA-dependent functions, lincRNA loci act as DNA elements or through transcription. Our integrative approach helps distinguish these mechanisms.

Genome-wide analyses reveal the IRE1a-XBP1 pathway promotes T helper cell differentiation by resolving secretory stress and accelerating proliferation.

BACKGROUND: The IRE1a-XBP1 pathway is a conserved adaptive mediator of the unfolded protein response. The pathway is indispensable for the development of secretory cells by facilitating protein folding and enhancing secretory capacity. In the immune system, it is known to function in dendritic cells, plasma cells, and eosinophil development and differentiation, while its role in T helper cell is unexplored. Here, we investigated the role of the IRE1a-XBP1 pathway in regulating activation and differentiation of type-2 T helper cell (Th2), a major T helper cell type involved in allergy, asthma, helminth infection, pregnancy, and tumor immunosuppression. METHODS: We perturbed the IRE1a-XBP1 pathway and interrogated its role in Th2 cell differentiation. We performed genome-wide transcriptomic analysis of differential gene expression to reveal IRE1a-XBP1 pathway-regulated genes and predict their biological role. To identify direct target genes of XBP1 and define XBP1's regulatory network, we performed XBP1 ChIPmentation (ChIP-seq). We validated our predictions by flow cytometry, ELISA, and qPCR. We also used a fluorescent ubiquitin cell cycle indicator mouse to demonstrate the role of XBP1 in the cell cycle. RESULTS: We show that Th2 lymphocytes induce the IRE1a-XBP1 pathway during in vitro and in vivo activation. Genome-wide transcriptomic analysis of differential gene expression by perturbing the IRE1a-XBP1 pathway reveals XBP1-controlled genes and biological pathways. Performing XBP1 ChIPmentation (ChIP-seq) and integrating with transcriptomic data, we identify XBP1-controlled direct target genes and its transcriptional regulatory network. We observed that the IRE1a-XBP1 pathway controls cytokine secretion and the expression of two Th2 signature cytokines, IL13 and IL5. We also discovered that the IRE1a-XBP1 pathway facilitates activation-dependent Th2 cell proliferation by facilitating cell cycle progression through S and G2/M phase. CONCLUSIONS: We confirm and detail the critical role of the IRE1a-XBP1 pathway during Th2 lymphocyte activation in regulating cytokine expression, secretion, and cell proliferation. Our high-quality genome-wide XBP1 ChIP and gene expression data provide a rich resource for investigating XBP1-regulated genes. We provide a browsable online database available at http://data.teichlab.org .