Biology subjects

Yuan, G.-C.

Publications and source records attributed to Yuan, G.-C..

30 records · Page 2Linked to original sources

bioRxiv2019

In vivo CRISPR screening identifies RNF20/40 as epigenetic regulators of cardiomyocyte maturation

Between birth and adulthood cardiomyocytes (CMs) undergo dramatic changes in size, ultrastructure, metabolism, and gene expression, in a process collectively referred to as CM maturation. The transcriptional network that coordinates CM maturation is poorly understood, creating a bottleneck for cardiac regenerative medicine. Forward genetic screens are a powerful, unbiased method to gain novel insights into transcriptional networks, yet this approach has rarely been used in vivo in mammals because of high resource demands. Here we utilized somatic mutagenesis to perform the first reported in vivo CRISPR genetic screen within a mammalian heart. We discovered and validated several novel transcriptional regulators of CM maturation. Among them were RNF20 and RNF40, which form a complex that monoubiquitinates H2B on lysine 120. Mechanistic studies indicated that this epigenetic mark controls dynamic changes in gene expression required for CM maturation. These insights into CM maturation will inform efforts in cardiac regenerative medicine. More broadly, our approach will enable unbiased forward genetics across mammalian organ systems.

genetics↗

bioRxiv2019

GiniClust3: a fast and memory-efficient tool for rare cell type identification

MotivationWith the rapid development of single-cell RNA sequencing technology, it is possible to dissect cell-type composition at high resolution. A number of methods have been developed with the purpose to identify rare cell types. However, existing methods are still not scalable to large datasets, limiting their utility. To overcome this limitation, we present a new software package, called GiniClust3, which is an extension of GiniClust2 and significantly faster and memory-efficient than previous versions.\n\nResultsUsing GiniClust3, it only takes about 7 hours to identify both common and rare cell clusters from a dataset that contains more than one million cells. Cell type mapping and perturbation analyses show that GiniClust3 could robustly identify cell clusters.\n\nAvailabilityGiniCluster3 is implemented in the open-source python package, with source code freely available through the Github (https://github.com/rdong08/GiniClust3).\n\nContactgcyuan@jimmy.harvard.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data

The rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.

bioinformatics↗

bioRxiv2018

CDK12 loss in cancer cells affects DNA damage response genes through premature cleavage and polyadenylation

The cyclin-dependent kinase 12 (CDK12) modulates transcription elongation by phosphorylating the carboxy-terminal domain of RNA polymerase II and appears to selectively affect the expression of DNA damage response (DDR) and mRNA processing genes. Yet, the mechanism(s) by which it achieves this selectivity remains unclear. Using a highly selective CDK12/13 inhibitor, THZ531, and nascent RNA sequencing, we show that CDK12 inhibition results in gene length-dependent elongation defects, leading to premature cleavage and polyadenylation (PCPA) as well as loss of expression of long (>45 kb) genes, a substantial proportion of which participate in the DDR. This early termination phenotype correlated with an increased proportion of intronic polyadenylation sites, a feature that was especially prominent among DDR genes. Finally, phosphoproteomic analysis indicated that pre-mRNA processing factors, including those involved in PCPA, are direct phosphotargets of CDKs 12 and 13. These results support a model in which DDR genes are uniquely susceptible to CDK12 inhibition due primarily to their relatively longer lengths and lower ratios of U1 snRNP binding to intronic polyadenylation sites.

cancer biology↗

bioRxiv2018

Revealing the critical regulators of cell identity in the mouse cell atlas

Recent progress in single-cell technologies has enabled the identification of all major cell types in mouse. However, for most cell types, the regulatory mechanism underlying their identity remains poorly understood. By computational analysis of the recently published mouse cell atlas data, we have identified 202 gene regulatory networks whose activities are highly variable across different cell types, and more importantly, predicted a small set of essential regulators for each of over 800 cell types in mouse for the first time. Systematic validation by automated literature- and data-mining provides strong additional support for our predictions. Thus, these predictions serve as a valuable resource that would be useful for the broad biological community. Finally, we have built a user-friendly, interactive, web-portal to enable users to navigate this mouse cell network atlas.

bioinformatics↗

bioRxiv2018

STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

genomics↗

bioRxiv2018

Decomposing spatially dependent and cell type specific contributions to cellular heterogeneity

Both the intrinsic regulatory network and spatial environment are contributors of cellular identity and result in cell state variations. However, their individual contributions remain poorly understood. Here we present a systematic approach to integrate both sequencing-and imaging-based single-cell transcriptomic profiles, thereby combining whole-transcriptomic and spatial information from these assays. We applied this approach to dissect the cell-type and spatial domain associated heterogeneity within the mouse visual cortex region. Our analysis identified distinct spatially associated signatures within glutamatergic and astrocyte cell compartments, indicating strong interactions between cells and their spatial environment. Using these signatures as a guide to analyze single cell RNAseq data, we identified previously unknown, but spatially associated subpopulations. As such, our integrated approach provides a powerful tool for dissecting the roles of intrinsic regulatory networks and spatial environment in the maintenance of cellular states.

bioinformatics↗

bioRxiv2018

A cluster-aware, weighted ensemble clustering method for cell-type detection

Single-cell analysis is a powerful tool for dissecting the cellular composition within a tissue or organ. However, it remains difficult to detect rare and common cell types at the same time. Here we present a new computational method, called GiniClust2, to overcome this challenge. GiniClust2 combines the strengths of two complementary approaches, using the Gini index and Fano factor, respectively, through a cluster-aware, weighted ensemble clustering technique. GiniClust2 successfully identifies both common and rare cell types in diverse datasets, outperforming existing methods. GiniClust2 is scalable to very large datasets.

bioinformatics↗

bioRxiv2017

Haystack: systematic analysis of the variation of epigenetic states and cell-type specific regulatory elements

MotivationWith the increasing amount of genomic and epigenomic data in the public domain, a pressing challenge is how to integrate these data to investigate the role of epigenetic mechanisms in regulating gene expression and maintenance of cell-identity. To this end, we have implemented a computational pipeline to systematically study epigenetic variability and uncover regulatory DNA sequences that play a role in gene regulation.\n\nResultsHaystack is a bioinformatics pipeline to characterize hotspots of epigenetic variability across different cell-types as well as cell-type specific cis-regulatory elements along with their corresponding transcription factors. Our approach is generally applicable to any epigenetic mark and provides an important tool to investigate cell-type identity and the mechanisms underlying epigenetic switches during development. Additionally, we make available a set of precomputed tracks for a number of epigenetic marks across several cell types. These precomputed results may be used as an independent resource for functional annotation of the human genome.\n\nAvailabilityThe Haystack pipeline is implemented as an open-source, multiplatform, Python package called haystack_bio available at https://github.com/pinellolab/haystack_bio.\n\nContactlpinello@mgh.harvard.edu, gcyuan@jimmy.harvard.edu

bioinformatics↗

bioRxiv2017

Dissecting super-enhancer hierarchy based on chromatin interactions

Recent studies have highlighted super-enhancers (SEs) as important regulatory elements for gene expression, but their intrinsic properties remain incompletely characterized. Through an integrative analysis of Hi-C and ChIP-seq data, we find that a significant fraction of SEs are hierarchically organized, containing both hub and non-hub enhancers. Hub enhancers share similar histone marks with non-hub enhancers, but are distinctly associated with cohesin and CTCF binding sites and disease-associated genetic variants. Genetic ablation of hub enhancers results in profound defects in gene activation and local chromatin landscape. As such, hub enhancers are the major constituents responsible for SE functional and structural organization.

bioinformatics↗

bioRxiv2017

Challenges And Emerging Directions In Single-Cell Analysis

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances, and look to future opportunities.

genomics↗

bioRxiv2017

Integrated Computational Guide Design, Execution, And Analysis Of Arrayed And Pooled CRISPR Genome Editing Experiments

CRISPR genome editing experiments offer enormous potential for the evaluation of genomic loci using arrayed single guide RNAs (sgRNAs) or pooled sgRNA libraries. Numerous computational tools are available to help design sgRNAs with optimal on-target efficiency and minimal off-target potential. In addition, computational tools have been developed to analyze deep sequencing data resulting from genome editing experiments. However, these tools are typically developed in isolation and oftentimes not readily translatable into laboratory-based experiments. Here we present a protocol that describes in detail both the computational and benchtop implementation of an arrayed and/or pooled CRISPR genome editing experiment. This protocol provides instructions for sgRNA design with CRISPOR, experimental implementation, and analysis of the resulting high-throughput sequencing data with CRISPResso. This protocol allows for design and execution of arrayed and pooled CRISPR experiments in 4-5 weeks by non-experts as well as computational data analysis in 1-2 days that can be performed by both computational and non-computational biologists alike.

molecular biology↗