MOFI reveals dynamic multi-omics coordination during cell fate progression
- 1Peking University
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.
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BibTeX
@misc{shen2026mofi,
title = {{MOFI reveals dynamic multi-omics coordination during cell fate progression}},
author = {J. Shen and Z. Zhang and H. Peng and Q. Peng and Z. Wang and R. Liu and P. Zhou},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.09.757904},
url = {https://www.biorxiv.org/content/10.64898/2026.10.09.757904v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - MOFI reveals dynamic multi-omics coordination during cell fate progression
AU - J. Shen
AU - Z. Zhang
AU - H. Peng
AU - Q. Peng
AU - Z. Wang
AU - R. Liu
AU - P. Zhou
PY - 2026
DA - 2026/10/09
PB - bioRxiv
DO - 10.64898/2026.10.09.757904
UR - https://www.biorxiv.org/content/10.64898/2026.10.09.757904v1
AB - 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.
ER -