Cell-Cell Communication Analysis of Cancer Organoids Using Multimodal Single-Cell PTM and RNA Profiling

Studying how different cell-types collaborate is essential to our understanding of healthy tissues and diseases such as cancer. To investigate how tissues function, we need to measure both how signals are transferred between cells and how that information is processed within cells. Unfortunately, no technology currently exists to simultaneously measure signalling both within and between cells.

We have previously developed a powerful technology to measure lots of signals within individual cell-types. Each of these signalling events describe how the cells are behaving - so being able to measure lots of them across different cells is extremely exciting. Unfortunately, our technology cannot measure communication signals between different cells. It has recently been shown that DNA-sequencing technologies can be adapted to study communication between cells. Unfortunately, these sequencing technologies cannot measure signals inside cells.

The aim of this project is to develop a technology capable of measuring signalling both within and between single-cells in cancer. By integrating our existing single-cell signalling methods with new single-cell sequencing approaches, we will develop a 'multimodal' technology to study how different cell-types communicate in 'mini-organ' models of bowel cancer. Through understanding how different cells types collaborate to drive tumours, this work will provide novel therapeutic opportunities to treat bowel cancer.

Studying how different cell-types communicate is essential for our understanding of metazoan tissues. Recent advances in 3D organoid technology are enabling researchers to accurately model cell-cell interactions. Unfortunately, technologies to measure cell-cell communication in organoids are lacking.

We have recently developed a mass-cytometry (MC) technology to measure ~30 post-translational modifications (PTMs) across single epithelial, fibroblast, and macrophage organoid cells (Qin et al., bioRxiv, 2019). While powerful for studying intracellular PTM signalling, MC cannot describe the 1,000s of ligands and receptors responsible for intercellular signalling. As a result, we cannot molecularly 'connect' signals between epithelial, stromal, and immune cells in organoids. To better understand how cells communicate with one another, we need to simultaneously measure which ligands are expressed by each cell, which receptors are found on each cell, and how these drive downstream intracellular PTMs signalling within each cell.

This MRC Research Grant will develop a 'multimodal' platform to measure ligand and receptor gene expression and PTM signals within single-cells from colorectal cancer (CRC) tumour microenvironment (TME) organoids. This will be achieved across the following work packages:

WP 1 - Integrate 10x Genomics scRNA-Seq and Anti-PTM MC

WP 2 - Integrate SPLiT-Seq and Anti-PTM MC

WP 3 - Develop a Multimodal SPLiT-Seq with Anti-PTM Oligo-Antibodies

WP 4 - Use Multimodal scRNA-PTM to Study Signalling in CRC TME Organoids.

MC and scRNA-Seq data will be integrated using Manifold-Aligned Generative Adversarial Networks (MAGANs) in with Prof. Krishnaswamy (Yale University) and ligand-receptor pairs will be established via CellPhoneDB with Dr. Roser Vento-Tormo (Sanger Institute). By measuring single-cell PTM, ligands, and receptors in a single multimodal assay, this project will describe novel cell-cell signalling networks in cancer organoids.

All tissues involve communication between different cell-types. This project will novel multimodal single-cell technologies to study communication between cells in organoid models of the colorectal cancer (CRC) tumour microenvironment (TME). Despite focusing on the colon, the tools developed during this project will also be applicable to other organoid models of healthy and diseased tissue (e.g. stomach, liver, pancreas, lung etc). Moreover, these tools will also be applicable to solution-phase heterocellular systems (e.g. lymph nodes, circulating immune-system etc). As a result, this project will impact any researcher studying cell-cell communication in metazoan biology.

Protocols developed during this project will be made publicly available on our lab website (www.tape-lab.com). Software generated during this project will be shared via GitHub (www.github.com) (for Python/TensorFlow) and Bioconductor (www.bioconductor.org) (for R packages). Heterocellular signalling models generated during this project will be shared using the BioModels Database (www.ebi.ac.uk/biomodels-main). All CyTOF data generated during this project will be shared on Cytobank (www.cytobank.org) and scRNA-seq data will be shared via ArrayExpress (www.ebi.ac.uk/arrayexpress) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). Data sharing will mainly accompany pre-print (e.g. bioRxiv) and peer-reviewed publications, but will also be used to share on-going work with collaborators.

Collectively, this project will enable researchers around the world to perform cell-cell communication analysis on organoid models of healthy and diseased tissue.