MOFI reveals dynamic multi-omics coordination during cell fate progression

Cell fate decisions are influenced by dynamic coordination between various molecular layers, including transcriptomic, chromatin, and protein levels. Recent advancements in time-series single-cell multi-omics allow for profiling these molecular layers across different developmental stages; however, understanding the continuous trajectories linking discrete snapshots remains challenging. The primary obstacle is to effectively integrate paired omics measurements while reconstructing their shared developmental progression.

Introducing Multi-Omics Fate Inference (MOFI), a novel framework designed to dynamically integrate multi-omics data and reconstruct continuous cell fate transitions from paired snapshots collected at various time points. MOFI simultaneously models the dynamics of each omics layer, identifies cross-omics correspondences, and considers shared population growth within a unified framework.

This integration enables the analysis of cross-omics perturbation responses and the identification of potential transformation programs connecting different molecular layers.

MOFI has been evaluated using both simulated benchmarks and paired RNA-ATAC/RNA-protein time-series datasets. The results demonstrate MOFI's ability to accurately reconstruct continuous fate transitions, population dynamics, and cross-omics changes that align with observed measurements. In kidney organoids, MOFI effectively captured the coordinated progression of RNA-ATAC in nephron lineage specification.

In hematopoietic progenitors, MOFI successfully predicted lineage redistribution in response to perturbations and proposed multi-omics transformation programs that link transcriptional modules with protein-level responses. Furthermore, MOFI was able to resolve the gradual maturation of brain organoids in neural developmental systems and identify interneuron lineage allocation during human cortical development.

In conclusion, MOFI offers a dynamics-grounded approach for transforming discrete paired multi-omics snapshots into interpretable models of cell fate progression, perturbation response, and cross-omics transformation analysis. By providing a comprehensive understanding of the complex interactions between multiple molecular layers during cell fate decisions, MOFI represents a significant advancement in the field of single-cell multi-omics research.