1/n: 🔬We introduce NicheCompass (NC) a graph learning model to build spatial atlases across millions of cells, quantitatively characterize cell niches, infer cellular communication, and map new data into these atlases. NC can be used with various spatial technologies, ranging

2/n: The model draws inspiration from cellular signalling principles as an inductive bias. NC encodes cells (self) and their neighbourhoods (microenvironments) jointly, learning their relationships through the loss and architecture. It models cells as signal receivers from their microenvironment. NC learns interpretable cell representations, with each latent feature capturing the spatially localized activity of a gene program (GP). These GPs range from known ones encoding signalling to learning their targets and discovering new ones encoding spatial variability and potentially longer-range effects

3/n: Deciphering tissue architecture! Using NC, we can integrate distinct tissue samples, identify cell niches, and quantitatively characterize these based on learned GP activities. NC also enables the inference of cell-cell communication within and across niches on the GP level.

4/n: We systematically benchmarked NC across datasets, showing that the identified cell niches are anatomically meaningful and that the inferred representations and niches have high spatial and biological conservation, measured by a comprehensive suite of metrics.

5/n: To complement existing signalling knowledge, we design a mechanism to de novo learn gene programs (GPs). This enables effective use of data with limited or suboptimal gene panels, allowing us, for example, to identify spatially variable GPs in basal and luminal breast cancer niches.

6/n: We introduce spatial reference mapping using NC to efficiently contextualize a spatial query dataset within a reference atlas, enabling the seamless transfer of niche annotation (as opposed to cell type) and identification of novel niches present only in the query. We demonstrated this by mapping a new patient into a reference of non-small cell lung cancer patients, revealing a SPP1+ macrophage-dominated TME not present in other patients.

7/n: NC is extensible to spatial multi-omics data, including gene expression and chromatin accessibility. GPs then comprise genes and peaks based on chromosomal location or gene regulatory networks, enhancing characterization and unveiling niches previously undetectable.

Super exciting work!! Congratulations! 🎉🎉

Thanks Shila

Can’t wait to read this - how exciting ☺️⭐️

Thanks 🙏🏻