Single-cell label-free identification of senescence by Raman microscopy and spatial genomics - PROJECT SUMMARY The biochemical, molecular and cellular heterogeneity of senescent cells remains poorly characterized. The knowledge gap is mainly due to the lack of proper technology to characterize the cell states, types, and circuits in intact tissues. Thus, we will need novel technologies that would allow us to map the multidimensional parameters of senescence across diverse tissue environments at biochemical, molecular, cellular, and morphological levels and over longitudinal time frames. Single cell multi-omics and molecular profiling assays (e.g., single-cell RNA-seq, single-cell ATAC-seq, single-cell proteomics, methylomics, metabolomics) have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive. Cells need to be dissociated, fixed, or lysed for these molecular profiling assays. Raman microscopy offers a unique opportunity to comprehensively report on the vibrational energy levels of biomolecules in a label-free, nondestructive manner with subcellular spatial resolution. With recent advances in Raman microscopy, single-cell and spatial multi- omics, and machine learning, we have developed “Raman2RNA” (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images combined with multi-modal data integration and domain translation. In this proposal, we aim to develop “SenNetRaman”, an innovative experimental and computational platform to character the molecular heterogeneity of senescent cells through label-free hyperspectral Raman microscopy, single cell and spatial genomics, and machine learning. In the UG3 phase, we aim to develop “SenNetRaman” for characterizing single cells in mouse tissues corresponding to young, naturally aged or stress-induced senescence states from well- established mouse models. We will develop a high-throughput Raman microscopy system for label-free characterization of the biochemical heterogeneity of senescent cells and identify Raman signals/markers predictive of gene expression and corresponding to various senescent cell states and types. In the UH3 phase, we will demonstrate “SenNetRaman” for characterizing senescent cells across multiple senescence model systems including human tissues from established human senescence tissue mapping centers. Overall, “SenNetRaman” is a unique, modular and universal framework to link imaging data with single-cell multi-omics data for building quantitative biochemical and molecular tissue maps of senescent cells. Our application is innovative in the approach to study senescence by leveraging the recent advances in imaging, single-cell genomics, and machine learning. The results of this project will help identify novel markers and reveal new biology of senescence. “SenNetRaman” builds upon the SenNet Initiative and can be readily adapted to existing NIH single-cell tissue mapping efforts, including the Human Tumor Atlas (HTAN), Human Biomolecular Atlas Program (HuBMAP), and Human Cell Atlas (HCA) that will transform future biomedical and clinical research.
