MOFI reveals dynamic multi-omics coordination during cell fate progression

Authors

Shen, J., Zhang, Z., Peng, H., Peng, Q., Wang, Z., Liu, R., Zhou, P.

Abstract

Cell fate decisions are shaped by dynamic coordination across transcriptomic, chromatin, and protein layers. Time-series single-cell multi-omics assays now enable these molecular layers to be profiled across developmental stages, yet each cell is still observed only once, and the continuous multi-omics trajectories connecting discrete snapshots remain unresolved. A central challenge is therefore to jointly integrate paired omics measurements while reconstructing their shared developmental progression. Here we present Multi-Omics Fate Inference (MOFI), a dynamically constrained multi-omics integration framework designed to reconstruct continuous cell fate transitions from paired multi-omics snapshots sampled across multiple time points. MOFI jointly models omics-specific state dynamics, cross-omics correspondence, and shared population growth within a unified framework, enabling cross-omics perturbation response analysis and identification of candidate cross-omics transformation programs. Across simulated benchmarks and paired RNA-ATAC/RNA-protein time-series datasets, MOFI reconstructed continuous fate transitions, population dynamics and cross-omics changes consistent with observed measurements. In kidney organoids, MOFI captured coordinated RNA-ATAC progression of nephron lineage specification. In hematopoietic progenitors, MOFI predicted perturbation-responsive lineage redistribution and nominated multi-omics transformation programs linking transcriptional modules with protein-level responses. In neural developmental systems, MOFI resolved gradual brain organoid maturation and interneuron lineage allocation during human cortical development. Together, MOFI provides a dynamics-grounded method for converting discrete paired multi-omics snapshots into interpretable models of cell fate progression, perturbation response, and cross-omics transformation analysis.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 17 Oct 2026.

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