PhD Comprehensive Exam | Mehrshad Sadria, Deep learning meets single cell omics: how to bend the cells fate

MC 5501 and Zoom (please email amgrad@uwaterloo.ca for the meeting link)

Candidate

Mehrshad Sadria | Applied Mathematics, University of Waterloo

Title

Deep learning meets single cell omics: how to bend the cells fate

Abstract

Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration. Typically, a group of key genes, or master regulators, are manipulated to control cell fate, with the ultimate goal of accelerating recovery from diseases or injuries. Of importance is the ability to correctly identify the crucial genes from single-cell transcriptomics datasets. To accomplish that goal, I focus on developing different computational methods that combines in silico perturbation experiments with cell trajectory modeling using deep learning to predict master regulators and key pathways controlling cell fate. Our method can only use only scRNA-seq data from wild-type samples to learn and predict how cell type distribution changes following a perturbation or by combining scRNA-seq with lineage tracing techniques the method can predict the effect of different perturbations on cells fate. We assessed the performance of the developed methods using simulations from a mechanistic gene regulatory network model and diverse gene expression profiles covering blood and brain development. Our results show that our methods can detect known master regulators of cells fate from single-cell transcriptomics datasets. That capability points to its potential in accelerating the discovery of cell fate regulators that can be used to engineer and grow cells for therapeutic use in regenerative medicine applications.