Biology subjects

Trapnell, C.

Publications and source records attributed to Trapnell, C..

58 records · Page 4Linked to original sources

bioRxiv2017

Chromatin accessibility dynamics of myogenesis at single cell resolution

Over a million DNA regulatory elements have been cataloged in the human genome, but linking these elements to the genes that they regulate remains challenging. We introduce Cicero, a statistical method that connects regulatory elements to target genes using single cell chromatin accessibility data. We apply Cicero to investigate how thousands of dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of co-accessible regulatory elements linked by Cicero meet criteria of \"chromatin hubs\", in that they are physically proximal, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of gene expression. Pseudotemporal analysis revealed a subset of elements bound by MYOD in myoblasts that exhibit early opening, potentially serving as the initial sites of recruitment of chromatin remodeling and histone-modifying enzymes. The methodological framework described here constitutes a powerful new approach for elucidating the architecture, grammar and mechanisms of cis-regulation on a genome-wide basis.

genomics↗

bioRxiv2017

Aligning single-cell developmental and reprogramming trajectories identifies molecular determinants of reprogramming outcome

Cellular reprogramming through manipulation of defined factors holds great promise for large-scale production of cell types needed for use in therapy, as well as for expanding our understanding of the general principles of gene regulation. MYOD-mediated myogenic reprogramming, which converts many cell types into contractile myotubes, remains one of the best characterized model system for direct conversion by defined factors. However, why MYOD can efficiently convert some cell types into myotubes but not others remains poorly understood. Here, we analyze MYOD-mediated reprogramming of human fibroblasts at pseudotemporal resolution using single-cell RNA-Seq. Successfully reprogrammed cells navigate a trajectory with two branches that correspond to two barriers to reprogramming, with cells that select incorrect branches terminating at aberrant or incomplete reprogramming outcomes. Differential analysis of the major branch points alongside alignment of the successful reprogramming path to a primary myoblast trajectory revealed Insulin and BMP signaling as crucial molecular determinants of an individual cells reprogramming outcome, that when appropriately modulated, increased efficiency more than five-fold. Our single-cell analysis reveals that MYOD is sufficient to reprogram cells only when the extracellular milieu is favorable, supporting MYOD with upstream signaling pathways that drive normal myogenesis in development.

genomics↗

bioRxiv2017

Reversed graph embedding resolves complex single-cell developmental trajectories

Organizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.

genomics↗

bioRxiv2017

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Conventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold \"shotgun cellular coverage\" of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

genomics↗