scholarly journals Estimating drivers of cell state transitions using gene regulatory network models

2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Schlauch ◽  
Kimberly Glass ◽  
Craig P. Hersh ◽  
Edwin K. Silverman ◽  
John Quackenbush
2016 ◽  
Author(s):  
Daniel Schlauch ◽  
Kimberly Glass ◽  
Craig P. Hersh ◽  
Edwin K. Silverman ◽  
John Quackenbush

AbstractSpecific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Our results demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.


2018 ◽  
Vol 457 ◽  
pp. 137-151 ◽  
Author(s):  
Takayuki Ohara ◽  
Timothy J. Hearn ◽  
Alex A.R. Webb ◽  
Akiko Satake

Author(s):  
Jose Eduardo H. da Silva ◽  
Heder S. Betnardino ◽  
Helio J.C. Barbosa ◽  
Alex B. Vieira ◽  
Luciana C.D. Campos ◽  
...  

2020 ◽  
Vol 57 ◽  
pp. 171-179
Author(s):  
Mónica L García-Gómez ◽  
Aaron Castillo-Jiménez ◽  
Juan Carlos Martínez-García ◽  
Elena R Álvarez-Buylla

2019 ◽  
Author(s):  
Jasper Wouters ◽  
Zeynep Kalender-Atak ◽  
Liesbeth Minnoye ◽  
Katina I. Spanier ◽  
Maxime De Waegeneer ◽  
...  

AbstractMelanoma is notorious for its cellular heterogeneity, which is at least partly due to its ability to transition between alternate cell states. Similarly to EMT, melanoma cells with a melanocytic phenotype can switch to a mesenchymal-like phenotype. However, scattered emerging evidence indicates that additional, intermediate state(s) may exist. In order to search for such new melanoma states and decipher their underlying gene regulatory network (GRN), we extensively studied ten patient-derived melanoma cultures by single-cell RNA-seq of >39,000 cells. Although each culture exhibited a unique transcriptome, we identified shared gene regulatory networks that underlie the extreme melanocytic and mesenchymal cell states, as well as one (stable) intermediate state. The intermediate state was corroborated by a distinct open chromatin landscape and governed by the transcription factors EGR3, NFATC2, and RXRG. Single-cell migration assays established that this “transition” state exhibits an intermediate migratory phenotype. Through a dense time-series sampling of single cells and dynamic GRN inference, we unraveled the sequential and recurrent arrangement of transcriptional programs at play during phenotype switching that ultimately lead to the mesenchymal cell state. We provide the scRNA-Seq data with 39,263 melanoma cells on our SCope platform and the ATAC-seq data on a UCSC hub to jointly serve as a resource for the melanoma field. Together, this exhaustive analysis of melanoma cell state diversity indicates that additional states exists between the two extreme melanocytic and mesenchymal-like states. The GRN we identified may serve as a new putative target to prevent the switch to mesenchymal cell state and thereby, acquisition of metastatic and drug resistant potential.


2007 ◽  
Vol 10 (1) ◽  
pp. 83-91 ◽  
Author(s):  
E ALVAREZBUYLLA ◽  
M BENITEZ ◽  
E DAVILA ◽  
A CHAOS ◽  
C ESPINOSASOTO ◽  
...  

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