Signalling network inference in single cells from spatial transcriptomics data

Evangelia Petsalaki (EMBL-EBI) & Sarah Teichmann (Wellcome Sanger Institute)

Recent technological and methodological advances in scRNAseq and spatial transcriptomics, allow the characterization of the abundance and spatial distribution of RNAs within single cells in their tissue context1–5. Such approaches have already led to spatially resolved maps for the zebrafish embryo6, the mouse hippocampus1 and are currently being applied in human tissue samples, e.g. in the context of the Human Cell Atlas project. Studies so far have focused on understanding the spatial patterning of gene expression. Going forwards, scRNAseq and spatial transcriptomics provide a unique opportunity to study the effect of cell- cell interactions and communication on cell signalling networks at single cell resolution.

The aim of the proposed project is to develop methodologies that will: 1) Integrate scRNAseq data and prior knowledge to identify cell-specific signalling networks 2) Extract the spatial pattern of signalling pathways and gene regulatory networks 3) Infer the spatially-dependent interplay of these networks, focusing on bi-directional signalling mechanisms across cells.

Research Plan Aim 1: Cell-specific signalling networks We propose to initially calculate transcription factor (TF) activities in each cell based on scRNAseq data, either publicly available or obtained within Sanger. TFs are typically activated by signalling networks responding to external stimuli, or environmental conditions. The ESPOD will develop an approach to identify and score the potential signalling networks that could lead to the activation of this . Using annotations from pathway databases, such as Reactome7,8, he/she can extract the pathways known to activate the specific TFs. Further filtering taking into consideration the membrane receptors and other relevant genes expressed can be performed. The Petsalaki group is developing a computational approach to diffuse signal from nodes of interest in signalling networks and identify active signalling module signatures in cells from phosphoproteomics data. Such an approach can be adapted to score the signalling modules most likely to be active in the individual cells. In addition, over- or under- expression of components of known signalling complexes even if they are not known to lead to known transcription factor activation will also be considered.

Aim 2: Spatial pattern of differentially regulated signalling and gene regulatory networks Next, we will develop a method to identify the spatial patterns of differentially regulated signalling modules and gene regulatory network modules. The Teichmann & Stegle groups have recently developed such a method focusing on the spatial distribution of expression of individual genes from spatial transcriptomics data9. Extending this method to consider groups of genes (modules) instead of genes can be used to tackle this aim.

Aim 3: Inference of spatially-dependent interplay of signalling and gene regulatory networks The Teichmann group has recently assembled a collection of ligand-receptor pairs (CellphoneDB) that was used to create cell-cell communication networks from single cell transcriptomics data (unpublished). The successful ESPOD will use this information to study the influence of the signalling modules and gene regulatory networks active in one cell on those of the neighbouring cells in the tissue. Examples of approaches that he/she can use range from mutual information-based approaches to Bayesian or Markov logic statistics. The Petsalaki group (with colleagues) has also systematically characterised complexes involved in Rho signalling regulation (unpublished). Rho signalling is responsible for the regulation of the cell’s actin cytoskeleton and is involved in every major cell function from the cell cycle to cell polarity and migration. In this study, a high prevalence of these complexes in cell adhesions has been found, underlining their role in regulating these structures and cell-cell communication. To investigate their influence in the signalling responses and complex assemblies in the neighbouring cells, they will also be included in this project by considering the spatially distributed expression levels of the Rho signalling complex components and their influence on signalling networks of neighbouring cells. Resulting biological findings will be validated in the Teichmann lab.

Summary The main objective of this proposal is the development of computational methodologies to study the spatial distribution and interplay of cell signalling modules at single cell resolution within the physiological context of a tissue. The successful candidate will benefit from the extensive experience of the Teichmann group in single cell and spatial transcriptomics and of the Petsalaki group in cell signalling and network analyses. He/she will also have the opportunity to develop both advanced computational method development skills and will get exposed to experimental methodologies for scRNAseq, spatial transcriptomics, and molecular and cell biology methods to be used to validate his/her findings. We expect that the methods developed in this project will provide improved understanding of biological systems and will have several exciting applications ranging from development to cancer studies. They will also contribute to extracting additional value from the Human Cell Atlas project.

References

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