CAREER: Learning Mechanisms from Single Cell Multi-Omics Data

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Cells are the fundamental units of life. Understanding how cells differentiate into various cell types and how cells change during evolutionary process has been a long-standing scientific problem. Recent sequencing and imaging technologies can provide large scale data for each single cell in a tissue, including the amount of mRNA products of a gene, the amount of protein products of certain genes, the chromatin 3D structure, and the spatial location of each cell. The project will develop computational methods to learn mechanisms in cell differentiation and development from complex and high dimensional data. The project will provide open-source tools for the community and can be integrated with educational activities and outreach, such as courses on topics of algorithms in computational biology. Opportunities for high school and undergraduate students especially those from underrepresented groups will be provided to participate in cutting-edge research in computational biology.During recent years, single cell technologies have progressed in terms of both “modality” and “scale”. In terms of modality, multi-omic technologies allow researchers to profile each single cell from multiple aspects, including transcriptome, genome, chromatin accessibility and protein abundance. In terms of scale, more cells, tissues, individuals, and species are profiled with single cell RNA-sequencing (scRNA-seq) technology. Computational integration methods have been developed to integrate single cell data from different modalities or different batches. The goal of the project is to develop integration tools with a strong emphasis on mechanisms, and to learn molecular mechanisms from the large-scale, multi-modality single cell data. The specific aims are: (1) develop methods to integrate paired and unpaired single cell multi-omics data from the same tissue, and in particular, propose a method to learn consensus cell identity considering cross-modality relationships; (2) develop deep learning methods to integrate scRNA-seq data from multiple individuals or species. The method aims to remove technical batch effects but preserve biological variation between data matrices and infer the genes which are associated with each meta feature like age and race; (3) develop methods to learn cell-specific gene regulatory networks (GRNs) for cells in a temporal or spatial context. When spatial locations of cells are available, the project will implement a method to learn both cell-specific GRNs and cell-cell interactions. This research has the potential to make a significant step forward in understanding mechanisms in cells and diseases using large-scale, multi-modality single cell data. The results of the project can be found at the PI’s website: https://xiuweizhang.wordpress.com/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.