Novel multimodal approaches of decoding cellular dynamics for effective cell fate manipulation

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BACKGROUND: The recent advance of single-cell technologies presents new opportunities to deconvolve cell heterogeneity and examine the cellular dynamics for each cell population in cell differentiation, yet existing single-cell methods mostly focus on a single modality. Even though these unimodal methods have yielded significant transformative insights into cellular state dynamics, they are limited by the incomplete profiling of the cellular states and thus impede the discovery of more efficient cell fate manipulation. The ever-increasing availability of single-cell multi-omics data offers unprecedented opportunities to overcome this limitation and thereby derive a deep understanding of cellular dynamics in cell differentiation. However, the multimodal methods required to decode cellular dynamics in cell differentiation from single-cell multi-omics data are still greatly lacking. OBJECTIVES: My long-term career goal is to decode the complex cellular dynamics (how the cellular states change temporally and spatially) in various biological processes with computational approaches employing machine learning. Since the cellular states in most biological processes are substantially heterogeneous, single-cell measurements, particularly single-cell multi-omics data that could comprehensively profile the cellular states, are indispensable for decoding the cellular dynamics. Therefore, as a mid-term objective in the next five years, I will focus on developing multimodal approaches to reconstruct the cellular dynamics and the underlying regulatory networks in cell differentiation from single-cell multi-omics data. This mid-term objective is composed of three complementary short-term aims Aim1: Develop a multimodal clustering and annotation method to identify cell populations in cell differentiation from single-cell multi-omics data. Aim2: Reconstruct the cellular dynamics and underlying gene regulatory networks for identified cell populations in cell differentiation from single-cell multi-omics data. Aim3: Build an in-silico model to evaluate simulated cell fate manipulation based on reconstructed cellular dynamics in cell differentiation from single-cell multi-omics data. IMPACT: Single-cell omics data do not automatically lead to predictions that drive more effective cell fate manipulation in cell differentiation. Most existing computational single-cell approaches are unimodal and thus provide only a minimal understanding of the studied biological process, which impedes biological discoveries from the single-cell datasets. Single-cell multimodal approaches will bridge this gap and thus dramatically improve our understanding of cellular dynamics. The computationally reconstructed cellular dynamics models that we develop can be used to explore new strategies to manipulate cell fates in various applications such as cell and tissue engineering, and the biosynthesis of biofuels, all of which have great socio-economic benefits.