DALI Learns Rules Generating Spatiotemporal Transcriptomics

Salil S Bhate, Sandeep Kambhampati, Mehrtash Babadi, Fei Chen, Caroline Uhler

Track: Full / long paper (5-8 pages)

Keywords: Neural Cellular Automata, Self-organization, Spatial Transcriptomics

TL;DR: We introduce Dynamical Analysis of Latent Interactions (DALI), a framework that fits a latent partial differential equation to time-series spatial transcriptomics to learn rules that generate tissue development.

Abstract: Learning how molecular interactions dictate the self-organization of biological systems is a longstanding goal in developmental biology. Time-series spatial transcriptomics (spatiotemporal transcriptomics) provides data to interrogate such dynamic tissue processes. Existing computational methods for spatiotemporal transcriptomics focus on aligning cells and spatial regions across timepoints and learning the spatiotemporal determinants of cell-fate transitions, but do not identify interactions between local spatial processes that generate developmental patterns. To address this, we introduce \textit{Dynamical Analysis of Latent Interactions} (DALI), a framework that takes as input a time series of spatial transcriptomic snapshots and simultaneously fits a latent partial differential equation (PDE) and a spatial registration model to learn rules that generate tissue development. On two Stereo-seq datasets of Zebrafish and Mouse embryogenesis, DALI learns rules that predict gene expression from held-out tissue regions better than baseline approaches. We furthermore interpret these rules: the learned latent variables correspond to meaningful gene programs, and their learned interactions to developmentally important signaling interactions with previous causal evidence.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 63