DMS/NIGMS 2: Bayesian modeling of single cell spatial transcriptomics data to identify significant spatial - Physiological processes such as the sleep-wake cycle, metabolism, hormone secretion,neuro transmitter release, sensory capabilities, and a variety of behaviors including sleep, aggression, and mating are controlled by a circadian rhythm adapted to 24h day-night periodicity. The suprachiasmatic nucleus (SCN), which is in the anterior hypothalamus, controls the physiological responses in vertebrates. The peripheral tissues such as liver, heart, kidney, and mammary glands contain functional endogenous glands. SCN influences both the peripheral clock (which regulates numerous physiological processes including proliferation and apoptosis) as well as the central clock via a combination of neural and hormonal signals. Several epidemiological studies revealed that circadian rhythm disruption (CRD) impacts human health and increases the risk of developing metabolic disorders, cardiovascular diseases, and mood disorders. As the exact molecular mechanisms by which CRD alters mammary microenvironment are not known, therefore, in this study we propose to leverage the strength of next-generation sequencing and statistical bioinformatics approaches by performing single–cell spatial proteomics of the studied samples. We hypothesize using this high throughput technique will not only give an unbiased global and complete view of the cellular activities but will also provide pivotal insights about the affected cellular pathways. Using spatial transcriptomics, we will measure gene expressions at the single cell level along with the information of spatial locations of these cells in the tissue. In spatial transcriptomics, the gene expression data, along with spatial co- ordinates of the single cells, provides information on both gene expression significance and cell spatial dependencies. We propose flexible Bayesian approaches to investigate how CRD affects cell composition of a tissue by using spatial clustering algorithms to the spatial transcriptomic data. Next, after obtaining the cell types we will identify the cell-type-specific spatially varying genes. These will be utilized to see the effect of CRD disorder in gene and cell levels. The proposed research is targeted to single cell spatial transcriptomics; however, the derived methods and the results will have deep impact on the research fields of Bioinformatics and data science. Finally, computationally efficient, and tractable software (R/Python) packages will be developed, will be delivered and will be regularly updated to maximize impact across both statistics and medicine. The proposed research has immense transformative potential in the areas of basic cell and development biology, and single cell bioinformatics. It will bring together researchers from multiple disciplinary areas to conduct research on these fundamental themes. RELEVANCE (See instructions): The proposed research will provide critical answers as to whether circadian disruption via shift-work or traveling across the zones can impact mammary gland development and set the stage for further investigation of the underlying mechanisms. This study will lead to a better understanding of how circadian rhythm disruption (CRD) interrupts cell- cell interaction during different developmental stages using the data from single cell transcriptomic technique and will identify the key genes.
