Single cell proteomics to detect emergent cell states associated with ageing

• s.wilkinson@ed.ac.uk

Humans and other mammals exhibit an increasing heterogeneity in the molecular composition (“cell state”) of cells within organs as they age. This process impacts on healthy ageing and disease susceptibility. In order to understand the ageing process, therefore, including future detection of differences in the rate of ageing between individuals, we need to discover emergent cell states in ageing tissue. Single-cell techniques have promise to do this, being able to low abundance cell types. However, the most widely-used technique of single-cell RNA-Seq is limited - there is a disconnect between the transcriptome and protein levels with ageing.

Thus, this PhD project will employ the cutting-edge technique of single-cell proteomics to determine the protein signature of emergent cell states in the pancreas with age. It is already clear that novel or rare cell states increase in abundance here with age; it is suspected that this process underpins age-related changes in pancreatic function, inflammation and even diabetes and cancer risk.

With remarkable new equipment now available through the supervisory team we can perform unbiased mass spectrometric determination of approximately the 5000 most abundant proteins in a single cell, currently at 100 cells per day. In a pilot study, the supervisors have been able to determine the protein signatures of cell states in young pancreas. 

Aim 1 is thus deep characterisation of cell states from 21-month aged C57/BL6J mouse pancreas versus corresponding young mouse pancreas. This will involve optimisation of the workflow to expedite sample processing to up to 300 cells per day, using multiplex sample handling with a new BBSRC-funded cellenONE robot. Ultimately, this will allow greater than a thousand epithelial cells to be catalogued. Practically, this will involve tissue dissociation and single-cell proteomic data acquisition. Analysis will involve optimisation of clustering methodology to identify rare outlier cell states and differential protein expression analysis. 

Aim 2 is use of the optimised workflow to generate a reference dataset from a small sample of aged human pancreas, providing by the supervisors’ long-standing surgical collaborator, and perform species cross comparison.

Aim 3 is to extract key protein biomarkers of rare cell states from the above datasets, and optimise immunodetection in archived mouse and human samples, allowing analysis of the spatial biology of rare cell state(s) by incorporation with multiplex spatial proteomic staining that enables a complete range of known cell types to be mapped. From these data, one can infer the association of age-related cell states with local changes in tissue function. 

This project will give profound insight into the ageing process, using and developing cutting-edge technology.