Biology subjects

Ghahramani, A.

Publications and source records attributed to Ghahramani, A..

3 recordsLinked to original sources

bioRxiv2018

Identifying the genetic basis of variation in cell behaviour in human iPS cell lines from healthy donors

Large cohorts of human iPSCs from healthy donors are potentially a powerful tool for investigating the relationship between genetic variants and cellular phenotypes. Here we integrate high content imaging, gene expression and DNA sequence datasets for over 100 human iPSC lines to identify the genetic basis of inter-individual variability in cell behaviour. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we identified genes that correlated in expression with intrinsic (genetic) and extrinsic (ECM) factors. However, variation in mRNA levels could not account for outlier cell behaviour. Instead, we identified rare, deleterious SNVs in the coding sequence of genes involved in ECM adhesion that occurred in cell lines that were outliers for one or more phenotypes such as cell spreading. These also correlated with altered germ layer differentiation on micropatterned surfaces. Our study thus establishes a strategy for integrating genetic and cell biological measurements for high-throughput analysis.

cell biology↗

bioRxiv2018

Generative adversarial networks uncover epidermal regulators and predict single cell perturbations

Recent advances have enabled gene expression profiling of single cells at lower cost. As more data is produced there is an increasing need to integrate diverse datasets and better analyse underutilised data to gain biological insights. However, analysis of single cell RNA-seq data is challenging due to biological and technical noise which not only varies between laboratories but also between batches. Here for the first time, we apply a new generative deep learning approach called Generative Adversarial Networks (GAN) to biological data. We apply GANs to epidermal, neural and hematopoietic scRNA-seq data spanning different labs and experimental protocols. We show that it is possible to integrate diverse scRNA-seq datasets and in doing so, our generative model is able to simulate realistic scRNA-seq data that covers the full diversity of cell types. In contrast to many machine-learning approaches, we are able to interpret internal parameters in a biologically meaningful manner. Using our generative model we are able to obtain a universal representation of epidermal differentiation and use this to predict the effect of cell state perturbations on gene expression at high time-resolution. We show that our trained neural networks identify biological state-determining genes and through analysis of these networks we can obtain inferred gene regulatory relationships. Finally, we use internal GAN learned features to perform dimensionality reduction. In combination these attributes provide a powerful framework to progress the analysis of scRNA-seq data beyond exploratory analysis of cell clusters and towards integration of multiple datasets regardless of origin.

genomics↗

bioRxiv2017

Epidermal Wnt signalling regulates transcriptome heterogeneity and proliferative fate in neighbouring cells

Canonical Wnt/beta-catenin signalling regulates self-renewal and lineage selection within the mouse epidermis. Although the transcriptional response of keratinocytes that receive a Wnt signal is well characterised, little is known about the mechanism by which keratinocytes in proximity to the Wntreceiving cell are co-opted to undergo a change in cell fate. To address this, we performed single-cell mRNA-Seq on mouse keratinocytes co-cultured with and without the presence of beta-catenin activated neighbouring cells. We identified seven distinct cell states in cultures that had not been exposed to the beta-catenin stimulus and show that the stimulus redistributes wild type subpopulation proportions. Using temporal single-cell analysis we reconstruct the cell fate changes induced by neighbour Wnt activation. Gene expression heterogeneity was reduced in neighbouring cells and this effect was most dramatic for protein synthesis associated genes. The changes in gene expression were accompanied by a shift from a quiescent to a more proliferative stem cell state. By integrating imaging and reconstructed sequential gene expression changes during the state transition we identified transcription factors, including Smad4 and Bcl3, that were responsible for effecting the transition in a contact-dependent manner. Our data indicate that non cell-autonomous Wnt/beta-catenin signalling decreases transcriptional heterogeneity and further our understanding of how epidermal Wnt signalling orchestrates regeneration and self-renewal.

genomics↗