differentiation

Alignment of single-cell trajectories to compare cellular expression dynamics

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Our next meeting will be at 2pm on April 23rd, in room 4160 of the Discovery building. Our Selected paper is Alignment of single-cell trajectories to compare cellular expression dynamics.
The abstract is as follows.

Single-cell RNA sequencing and high-dimensional cytometry can be used to generate detailed trajectories of dynamic biological processes such as differentiation or development. Here we present cellAlign, a quantitative framework for comparing expression dynamics within and between single-cell trajectories. By applying cellAlign to mouse and human embryonic developmental trajectories, we systematically delineate differences in the temporal regulation of gene expression programs that would otherwise be masked.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.

Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming.

This entry was posted in differentiation single-cell on by

Our next meeting will be at 11:00 on Nov 7th, in room 4160 of the Discovery building. Our Selected paper is Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming.
The abstract is as follows.

Understanding the molecular programs that guide cellular differentiation during development is a major goal of modern biology. Here, we introduce an approach, WADDINGTON-OT, based on the mathematics of optimal transport, for inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories from large-scale single-cell RNA-seq (scRNA-seq) data collected along a time course. We demonstrate the power of WADDINGTON-OT by applying the approach to study 65,781 scRNA-seq profiles collected at 10 time points over 16 days during reprogramming of fibroblasts to iPSCs. We construct a high-resolution map of reprogramming that rediscovers known features; uncovers new alternative cell fates including neural- and placental-like cells; predicts the origin and fate of any cell class; highlights senescent-like cells that may support reprogramming through paracrine signaling; and implicates regulatory models in particular trajectories. Of these findings, we highlight Obox6, which we experimentally show enhances reprogramming efficiency. Our approach provides a general framework for investigating cellular differentiation.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.

Reversed graph embedding resolves complex single-cell trajectories

This entry was posted in differentiation single-cell on by

Our next meeting will be at 11:00 on September 26th, in room 4160 of the Discovery building. Our Selected paper is Reversed graph embedding resolves complex single-cell trajectories.
The abstract is as follows.

Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.

Discovering sparse transcription factor codes for cell states and state transitions during development

This entry was posted in differentiation sequence_to_expression on by

Our next meeting will be at 3:00 on April 28th, in room 4160 of the Discovery building. Our Selected paper is Discovering sparse transcription factor codes for cell states and state transitions during development.
The abstract is as follows.

Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.

Epigenomic Co-localization and Co-evolution Reveal a Key Role for 5hmC as a Communication Hub in the Chromatin Network of ESCs

This entry was posted in differentiation Epigenome and tagged chromatin DNA methylation epigenomes on by

Our selected paper for this week is titled Epigenomic Co-localization and Co-evolution Reveal a Key Role for 5hmC as a Communication Hub in the Chromatin Network of ESCs, from Cell.The abstract is as follows:

Epigenetic communication through histone and cytosine modifications is essential for gene regula- tion and cell identity. Here, we propose a framework that is based on a chromatin communication model to get insight on the function of epigenetic modifica- tions in ESCs. The epigenetic communication network was inferred from genome-wide location data plus extensive manual annotation. Notably, we found that 5-hydroxymethylcytosine (5hmC) is the most-influential hub of this network, connecting DNA demethylation to nucleosome remodeling complexes and to key transcription factors of plurip- otency. Moreover, an evolutionary analysis revealed a central role of 5hmC in the co-evolution of chro- matin-related proteins. Further analysis of regions where 5hmC co-localizes with specific interactors shows that each interaction points to chromatin remodeling, stemness, differentiation, or meta- bolism. Our results highlight the importance of cyto- sine modifications in the epigenetic communication of ESCs.

Feel free to begin our discussion in the comments section below. Our meeting will be at 12:30 PM in room 3160 of the Discovery building on July 18th.

Predicting tissue specific transcription factor binding sites

This entry was posted in causal_networks differentiation network_inference on by

Our selection for our meeting on the 16th of May is Predicting tissue specific transcription factor binding sites. We will meet as usual in room 3160 of the Discovery building at 12:30 PM. The abstract is as follows.

Background

Studies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Most existing prediction methods are based on sequence information alone, ignoring biological contexts such as developmental stages and tissue types. Experimental methods to study in vivo binding, including ChIP-chip and ChIP-seq, can only study one transcription factor in a single cell type and under a specific condition in each experiment, and therefore cannot scale to determine the full set of regulatory interactions in mammalian transcriptional regulatory networks.

Results

We developed a new computational approach, PIPES, for predicting tissue-specific TF binding. PIPES integrates in vitro protein binding microarrays (PBMs), sequence conservation and tissue-specific epigenetic (DNase I hypersensitivity) information. We demonstrate that PIPES improves over existing methods on distinguishing between in vivo bound and unbound sequences using ChIP-seq data for 11 mouse TFs. In addition, our predictions are in good agreement with current knowledge of tissue-specific TF regulation.

Conclusions

We provide a systematic map of computationally predicted tissue-specific binding targets for 284 mouse TFs across 55 tissue/cell types. Such comprehensive resource is useful for researchers studying gene regulation.

We look forward to seeing all who can attend and feel free to begin our discussion in the comments section below.

08.19.15

This entry was posted in differentiation methodological network_inference on by

Wanderlust with special guest Monacle

“Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development”

Bendall et al, Cell 2014

Abstract

Tissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naive B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and “corrupted” developmental processes including cancer.

Copyright © 2014 Elsevier Inc. All rights reserved.

Monocle method

(Trapnell et al, Nature 2014)

Abstract

Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.