Unravelling the proteome of chromatin bound RNA polymerase II using Proteome-ChIP in murine stem cells

Regulation of gene expression is critical to govern distinct transcriptional programs for a cell type, lineage specification and developmental stage. Transcription is the first step in gene expression wherein RNA Polymerase II (RNAPII) transcribes protein-coding genes. Transcription is a highly coordinated process that involves a range of chromatin interactions including transcription machinery, chromatin remodellers and co-transcriptional RNA processing. Embryonic stem (ES) cells are pluripotent, self-renewing cells that can differentiate to give rise to all lineages making them an invaluable tool to study early development and in therapy. Genome-wide analysis in murine mES cells has identified 30% of known genes harbouring bivalent chromatin modifications along with repressive Polycomb complexes and a novel variant of RNAPII (modified as S5p+S7p-S2p-) with mechanistic implications in stem cell pluripotency, differentiation potential and lineage specification. To explore chromatin composition associated with different variants of RNAPII, I developed an unbiased method, ‘Proteome-ChIP’ (pChIP) wherein crosslinked chromatin is purified by immunoprecipitation followed by protein extraction and identification by Mass Spectrometry. Using an unbiased comprehensive experimental strategy and a novel systems biology approach, I qualitatively and quantitatively dissect the proteome composition and dependencies on RNAPII modifications during different stages of the transcription cycle. The work done in this thesis provides an invaluable resource of RNAPII chromatin interactions. We identify known and novel components of the co-transcriptional machinery, chromatin remodelling and RNA processing machinery. The work also uncovers novel processes associated with unusual RNAPII (S5p+S7p-S2p-) including DNA replication, Polycomb proteins and chromatin remodellers; many of these processes critical for stem cell viability and regulation. Extending the RNAPII-pChIP analysis on low complexity samples by Native-pChIP and Gradient-pChIP highlights the versatility of robustness of our method. The work described in this sheds light on regulatory chromatin processes specific to mES cells, which informs our understanding of stem cell biology and reprogramming

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

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. Here, we integrate histone modifications and RNAPII states derived from bulk ChIP-seq data with single-cell RNA-sequencing data. We find that Polycomb repressive complex-active genes have greater cell-to-cell variation in expression than active genes, and these results are validated by knockout experiments. We also show that PRC-active genes are clustered on chromosomes in both two and three dimensions, and interactions with active enhancers promote a stabilization of gene expression noise. These findings provide new insights into how chromatin regulation modulates stochastic gene expression and transcriptional bursting, with implications for regulation of pluripotency and development.Polycomb repressive complexes modify histones but it is unclear how changes in chromatin states alter kinetics of transcription. Here, the authors use single-cell RNAseq and ChIPseq to find that actively transcribed genes with Polycomb marks have greater cell-to-cell variation in expression

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

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

BioModels: ten-year anniversary

BioModels (http://www.ebi.ac.uk/biomodels/) is a repository of mathematical models of biological processes. A large set of models is curated to verify both correspondence to the biological process that the model seeks to represent, and reproducibility of the simulation results as described in the corresponding peer-reviewed publication. Many models submitted to the database are annotated, cross-referencing its components to external resources such as database records, and terms from controlled vocabularies and ontologies. BioModels comprises two main branches: one is composed of models derived from literature, while the second is generated through automated processes. BioModels currently hosts over 1200 models derived directly from the literature, as well as in excess of 140 000 models automatically generated from pathway resources. This represents an approximate 60-fold growth for literature-based model numbers alone, since BioModels’ first release a decade ago. This article describes updates to the resource over this period, which include changes to the user interface, the annotation profiles of models in the curation pipeline, major infrastructure changes, ability to perform online simulations and the availability of model content in Linked Data form. We also outline planned improvements to cope with a diverse array of new challenges

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

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

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

Erratum to: 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. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing. Conclusion: The link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity