Title: ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell data
Legend: a.) Cellular processes are complex and dynamic, and undergo cell population changes driven by birth, proliferation, and death with time. b.) Single-cell sequencing provides snapshots of unaligned cells at discrete timepoints. To reconstruct cellular trajectories, we propose ARTEMIS. c.) Visualization of the drift inferred by ARTEMIS trained on nine timepoints. d.) Top 20 drift-genes identified for t = 8. e.) Comparison of normalized ratios of relative cell population changes between ground truth and ARTEMIS-predicted cell statuses as live.
Citation: Alatkar, S. A., & Wang, D. (2025). ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell data. bioRxiv : the preprint server for biology, 2025.01.23.634618. https://doi.org/10.1101/2025.01.23.634618
Abstract: Cellular processes like development, differentiation, and disease progression are highly complex and dynamic (e.g., gene expression). These processes often un-dergo cell population changes driven by cell birth, proliferation, and death. Single-cell sequencing enables gene expression measurement at the cellular resolution, allowing us to decipher cellular and molecular dynamics underlying these pro-cesses. However, the high costs and destructive nature of sequencing restrict observations to snapshots of unaligned cells at discrete timepoints, limiting our understanding of these processes and complicating the reconstruction of cellular trajectories. To address this challenge, we propose ARTEMIS, a generative model integrating a variational autoencoder (VAE) with unbalanced Diffusion Schrödinger Bridge (uDSB) to model cellular processes by reconstructing cellular trajectories, reveal gene expression dynamics, and recover cell population changes. The VAE maps input time-series single-cell data to a continuous latent space, where trajectories are reconstructed by solving the Schrödinger bridge problem using forward-backward stochastic differential equations (SDEs). A drift function in the SDEs captures deterministic gene expression trends. An additional neural network estimates time-varying kill rates for single cells along trajectories, enabling recovery of cell population changes. Using three scRNA-seq datasets-pancreatic β -cell differentiation, zebrafish embryogenesis, and epithelial-mesenchymal transition (EMT) in cancer cells-we demonstrate that ARTEMIS: (i) outperforms state-of-art methods to predict held-out timepoints, (ii) recovers relative cell population changes over time, and (iii) identifies “drift” genes driving deterministic expression trends in cell trajectories. Furthermore, in silico perturbations show that these genes influence processes like EMT. The code for ARTEMIS: https://github.com/daifengwanglab/ARTEMIS .
Investigator: Daifeng Wang, PhD
About the Lab: The Wang lab develops machine learning and artificial intelligence (ML/AI) approaches and bioinformatics tools for understanding the cellular and molecular mechanisms from genotypes to phenotypes in complex brains (such as single-cell multimodal learning as above). Their applications focus on functional genomics, gene regulation, and neural circuits, particularly for brain development, intelligence, and brain diseases.