Computational analysis of single cell RNA-seq data to identify signalling networks underpinning tissue specific macrophage function during development and pathology

Supervisors: Nizar BatadaVeronique MironStephen Jenkins

Project Description:

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge genomic method that quantifies transcriptomes of individual cells. Computational analysis of this data allows unbiased identification of cell subtypes, their developmental relationships, cell-to-cell signalling and the intracellular regulatory pathways that regulate them. However, scRNA-seq data are challenging to work with due to the large scale and high dimensionality of the data and due to high rate of dropouts. Although single-cell transcriptomic datasets are measured at a single point from a tissue, cellular dynamics processes can be modelled using trajectory inference methods, also known as ‘pseudotemporal ordering methods’; these use single-cell profiles from a population (in which the cells are at different unknown points in the dynamic process) and computationally order the cells along a trajectory topology that can be linear, bifurcating or have a tree or a graph structure.  We (Batada Lab) are interested in developing new computational approaches for analysis of scRNA-seq data in order to identify cell subtypes[1], to infer alterations during development and disease, to infer previously hidden developmental relationships among cell-types and to identify regulatory factors underlying these interactions and changes. 

We are particularly interested in cell types known as macrophages which are present in nearly all tissues of the human body where they play diverse roles in tissue homeostasis, development, and immunity. However, the tissue-specific and pathology-driven factors that influence macrophage function, and the identity of factors made by macrophages that in turn influence the tissue, are poorly understood. This projects aims to apply a novel bioinformatics tool to investigate the signalling pathways involved in regulating heterogeneity of macrophage function and, in turn, tissue development/homeostasis, in: i) the brain, where macrophages (a.k.a. microglia) regulate white matter (myelin) health (Miron Lab) [2], and ii) the peritoneal cavity, where sex plays a unique role in controlling macrophage development and susceptibility to infection (Jenkins Lab) [3]. More specifically, this project will thus involve developing, implementing, assessing and iteratively improving methods for identifying relationship between clusters via a novel probabilistic pseudotemporal ordering method, first validating the approach using the above mentioned existing cell atlases, then applying it to two (2) newly generated unpublished single-cell RNAseq datasets from tissues where changes in macrophage heterogeneity over time are linked to tissue development and response to injury/infection. The student will use the developed methodology to predict 1) the bi-directional signals that regulate how microglia drive myelination of nerve fibres during development, and how perturbation of these pathways following injury may prevent successful myelin repair and 2) how the sex of an organism influences peritoneal macrophage behaviour during homeostasis and infection. This project will provide interdisciplinary training at the intersection of computation, immunology, and tissue repair and prepare the student for the upcoming revolution in data-driving biology.

This project will allow cross-institutional collaboration between the IGMM (Batada) and the QMRI (Miron, Jenkins), exploiting expertise in bioinformatics and immunology, respectively. This project builds on ongoing collaborations between the supervisors that has generated single cell sequencing data on macrophages in the brain and peritoneum; the student will be able to apply novel bioinformatics approaches to further these collaborations. The student will be able to attend seminars relating to both disciplines and participate in lab meetings at both sites.

1. Boufea & Batada, (under review)
2. Miron et al., 2013, Nature Neuroscience, doi: 10.1038/nn.3469. 
3. Bain et al., 2016, Nature Communications, doi: 10.1038/ncomms11852

If you wish to apply for this project, please check this link and send your application to this email.