RT Journal Article
SR Electronic
T1 Exact Bayesian lineage tree-based inference identifies Nanog negative autoregulation in mouse embryonic stem cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 053231
DO 10.1101/053231
A1 Justin Feigelman
A1 Stefan Ganscha
A1 Simon Hastreiter
A1 Michael Schwarzfischer
A1 Adam Filipczyk
A1 Timm Schroeder
A1 Fabian J. Theis
A1 Carsten Marr
A1 Manfred Claassen
YR 2016
UL http://biorxiv.org/content/early/2016/05/13/053231.abstract
AB The autoregulatory motif of Nanog, a heterogeneously expressed core pluripotency factor in mouse embryonic stem cells, remains debated. Although recent time-lapse microscopy data provide the unparalleled ability to monitor Nanog expression at the single-cell level, the extraction of mechanistic knowledge is precluded by the lack of inference techniques suitable for noisy, incomplete and heterogeneous data obtained from proliferating cell populations.This work identifies Nanog’s autoregulatory motif from quantified time-lapse fluorescence line-age trees with STILT (Stochastic Inference on Lineage Trees), a novel particle-filter based algorithm for exact Bayesian parameter inference and model selection of stochastic models. We first verify STILT’s ability to accurately infer parameters and select the correct autoregulatory motif from simulated data. We then apply STILT to time-lapse microscopy movies of a fluorescent Nanog fusion protein reporter and reject the possibility of positive autoregulation. Finally, we use STILT for experimental design, perform in silico overexpression simulations, and experimentally validate model predictions via exogenous Nanog overexpression. We finally conclude that the protein expression dynamics and overexpression experiments strongly suggest a weak negative feedback from the protein on the DNA activation rate.We find that a simple autoregulatory mechanism can explain the observed heterogeneous Nanog dynamics. This finding has implications on the understanding of the core pluripotency network, such as supporting the ability of mESC populations to diversify their proteomic profile to respond to a spectrum of differentiation cues. Beyond this application STILT constitutes a generally applicable fully Bayesian approach for model selection of gene regulatory models on the basis of time-lapse imaging data of proliferating cell populations. STILT is freely available at: http://www.imsb.ethz.ch/research/claassen/Software/stilt—stochastic-inference-on-lineage-trees.html