A stochastic model dissects cell states in biological transition processes

Abstract

Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations.

Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines

“…We circumvented this problem by first determining the transition rates (which are common to the entire gene system) on a smaller subset of genes which captures the dynamical response of the whole system [3]. To select those representative genes, we clustered z-scores of log-transformed time-course data using k-means.…”

“…The method was previously used to investigate reprogramming of mouse embryonic fibroblasts into induced pluripotent stem cells over four weeks [3]. Here we consider a different biological system, a BC model, characterized by a much shorter time scale, 32 hours.…”

Chemically Induced Reprogramming of Somatic Cells to Pluripotent Stem Cells and Neural Cells

“…They reported that by using the histone deacetylase (HDAC) inhibitor Valproic acid (VPA), they could eliminate the need for oncogenes c-Myc and Klf4 (two of the four Yamanaka factors), and also found that the iPSC reprogramming efficiency was increased 100-fold over that of the four transcription factor method. Studies from Ding’s laboratory used the histone methyltransferase (HMT) inhibitor BIX-01294, to activate calcium channels in the plasma membrane, and improved the reprogramming efficiency using the four Yamanaka factors [ 15 , 16 , 17 ]. Lin et al , 2009 [ 18 ] tested several inhibitors of transforming growth factor-β (TGFβ) receptor and MAPK/ERK kinase (MEK) on primary human fibroblasts (CRL2097 or BJ) that were transduced with retrovirus carrying genes encoding the four Yamanaka factors.…”

Inter-genus gene expression analysis in livestock fibroblasts using reference gene validation based upon a multi-species primer set

“…Cellular states are the result of differential gene expression that arises from stepwise instructive factors during development [1,2]. Alternatively, disease states or phenotypic perturbation of cellular transcriptional programs by environmental or/and genetic factors can be readily identified by gene expression changes [3,4].…”