Core B: Human and Clinical Phenotyping Core

PROJECT SUMMARY CORE B Core B has the overarching goal of supporting the Scientific Projects by providing reliable and readily available clinical data from patients and will support the Project Investigators by collecting, processing, and analyzing a rich multi-omics dataset from patients undergoing lung transplantation. Investigators in the Core will collect

detailed clinical data from lung transplant recipients and manually adjudicate clinical episodes and the diagnosis of PGD and CLAD. Core B investigators will identify clinical and research procedures that will generate biologic material and enroll patients in observational studies. Core B will work synergistically with the Core C investigators

to process the samples to generate single cell suspensions that will be analyzed using flow cytometry and single cell RNA-sequencing/CITE seq, paired fixed tissue for spatial transcriptomics, and other assays. Core B will apply robust machine learning approaches to organize clinical data and use this framework as a scaffold to

overlay longitudinally collected genomic and proteomic data collected over the clinical course of transplantation. The resulting models of clinical PGD and CLAD will be used to identify biomarkers and generate hypotheses about biologic pathways that will be causally tested in the Projects, credentialling the pathways as therapeutic

targets. Hence, Core B will directly support the Projects through the following aims. Aim 1. To enroll lung transplant recipients for prospective tissue collection and data integration throughout longitudinal care. We have developed integrated clinical and research teams to enable patient enrollment and collection of events

over the clinical course of patients. The clinical data extracted from the electronic health record will complement the molecular phenotyping data and enable the Scientific Projects to test the hypotheses outlined. This integrated model will facilitate the identification of biomarkers and clinical predictors of transitions in clinical state after

transplantation. Aim 2. To process, immunophenotype, and cryopreserve biological material from the lung and esophagus collected as part of clinical care or research. We will leverage our experience with sample collection, processing, flow cytometry, single cell RNA-sequencing, metabolomic and spatial transcriptomic

profiling to develop a robust multi-omics dataset of patients undergoing lung transplantation. Aim 3. To perform next-generation sequencing assays, including single-cell RNA-seq, CITE-seq, and single cell TCR and BCR clonotyping as well as spatial transcriptomics on selected samples. A major challenge in data science

has been the integration of large-scale datasets coming from multiple groups. Through collaborations with the Chan Zuckerberg Human Cell Atlas Consortium, we have contributed to approaches based on transfer learning that allow integration of single cell transcriptomic data rapidly and at scale. We will apply these approaches to

the multi-omics data collected from our patients to inform analyses proposed by the Project Investigators.