# Graph Learning of Cell-cell Communications in Spatial Transcriptomics > **NIH NIH R01** · YALE UNIVERSITY · 2022 · $125,623 ## Abstract PROJECT SUMMARY Research Project: Studies have shown that extreme weather is associated with changes in disease severity and activity. However, the molecular mechanisms influenced by atmospheric conditions that contribute to asthma severity and activity are poorly understood, which prevents us in designing effective asthma treatments. Furthermore, given climate change and increasing variability in daily atmospheric conditions, it is critical to understand these gene-environment interactions on asthma for better control of asthma symptoms. Gene- environment interactions have been previously reported as important determinants of risk for asthma, but the exact nature of the relationships and the molecular signals associated with these interactions remain unclear. Gene-environment interaction studies have mostly focused on exposure to pets, mold, smoking, occupational exposure, air pollution, and other allergens. None of the studies have considered changes in atmospheric conditions in the analysis, leaving a knowledge gap on the molecular mechanism of the interaction between climate change and gene and its contribution to phenotypes of asthma severity and activity. To fill this knowledge gap, we will explore the relationships between environmental factors collected from the nearest observatory and genome-wide cell type-specific gene expression levels in patients with asthma as well as its contribution to asthma severity and activity. To achieve this goal, we propose to 1) assess cell type-specific transcriptomic changes in the circulation and airway of asthma patients associated with fluctuations in atmospheric conditions and the contribution of their interaction to the phenotypes of asthma severity and activity, and 2) evaluate perturbations in intercellular communication induced by fluctuations in atmospheric conditions. Research design and methods: Tools developed in Aim 1 of the parent R01 will be applied to deconvolve the bulk expression data based on single-cell RNA sequencing (scRNA-seq) data so cell type-specific transcriptomic changes associated with fluctuations in atmospheric conditions can be identified. Tools developed in Aims 2 and 3 of the parent R01 grant will be applied to construct cell-cell communication networks in each patient and detect perturbations in these networks associated with fluctuations of atmospheric conditions using the deconvolved data. The contribution of identified atmospheric condition associated changes to the phenotypes of asthma severity and activity will be evaluated. The bulk expression data, scRNA-seq data and clinical phenotypes of asthma have been generated in Dr. Chupp’s lab and stored in the online YCAAD database that is constructed and maintained by Dr. Rajeevan. The daily atmospheric condition data from the closest weather station associated with each patient based on their zip codes will be downloaded and organized by Dr. Rajeevan. Preprocessing of all the data and the analysis of the data using tool... ## Key facts - **NIH application ID:** 10672669 - **Project number:** 3R01LM014087-01S1 - **Recipient organization:** YALE UNIVERSITY - **Principal Investigator:** Zuoheng Wang - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $125,623 - **Award type:** 3 - **Project period:** 2022-07-06 → 2026-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10672669 ## Citation > US National Institutes of Health, RePORTER application 10672669, Graph Learning of Cell-cell Communications in Spatial Transcriptomics (3R01LM014087-01S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10672669. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*