PROJECT SUMMARY OVERALL This proposal seeks to establish the KAPP-Sen Tissue Mapping Center Collaborative (TMC) as part of the Cellular Senescence Network: Tissue Mapping Centers effort {RFA-RM-21-008). Our application represents a multidisciplinary collaborative effort involving six leading institutions and aging research programs working together to characterize the distribution and biological heterogeneity of senescent cells in different healthy human tissues in full alignment with the objectives of the Cellular Senescence Network {SenNet). KAPP-Sen brings together skills, resources and perspectives needed to address Sen-Net goals in the framework of the healthy human Kidney. Adipose tissues, Pancreas and Placenta. Given our ability to obtain full thickness skin tissues from individuals providing KAPP samples, we may explore the possibility of future collaborative studies with TMCs selected for their major focus on skin. Our six collaborating institutions located in Farmington. CT (UConn Health, Jackson Laboratory for Genomic Medicine), Boston {Brigham & Women's Hospital, Joslin Diabetes Center), Rochester. MN {Mayo Clinic) and San Antonio. TX {UTHCSA) have all been carefully selected for their essential and unique individual contributions to the field and this effort. Our objectives will be achieved through the following aims: Aim 1 : Coordinate research activities across KAPP-Sen TMC Collaborative sites in support of Sen-Net goals towards mapping cellular senescence and its associated secretory phenotype in the healthy human kidney, adipose tissues, pancreas and placenta. Aim 2: Obtain tissues from healthy kidney transplant donors {kidneys, fat, skin), C-section pregnancies {placenta, cord, fat, skin), outpatient healthy donor biopsies {fat, skin), beating heart brain dead donors {full pancreas) and IIPD/Prodo {dispersed pancreas). Aim 3: Generate highest quality data pertaining to cellular senescence including scRNA-Seq, snRNA-Seq, spatial transcriptomics, immunohistochemistry and Telomere-associated DDR foci {TAFs) in all tissues Aim 4: Perform high level integrative data analysis required for the creation of aUases of human cellular senescence in collaboration with other TMCs, CODDC and NIH staff.
The Administrative Core will coordinate and oversee all research activities across KAPP-Sen TMC Collaborative sites in support of SenNet goals towards mapping cellular senescence and its associated secretory phenotype in the healthy human kidney, adipose tissues, pancreas, and placenta. Efforts will be made to ensure ongoing robust and productive dialogue and input from KAPP-Sen TMC investigators and staff with varied relevant areas of scientific expertise, disciplinary backgrounds, and perspectives. Moreover, continued innovation will be pursued by leveraging the ability of investigators from across SenNet to provide unique cutting-edge novel insights arising from their individual disciplines. The Core will also participate in ongoing program evaluation using both informal strategies and the established capacities of the UConn Center on Aging Evaluation and Population Assessment Research Core to perform rigorous program evaluation throughout KAPP-Sen and of Sen-Net partners as a means of ensuring the achievement of our objectives and pre-defined metrics, thus enabling optimal allocation of resources. Core leadership and staff will also collaborate with NIH staff and other SenNet leaders in ensuring the successful implementation of an innovative Phased Adaptive Approach to study design and data analysis, thereby providing a rational path from an Initial Data Generation Phase to an Interim Evaluation and Recalibration Phase leading ultimately to the Study Full Scale-Up and Completion Phase.
The KAPP-Sen Data Analysis core will curate, process, share, and analyze the rich expression and imaging datasets from the KAPP-Sen projects on kidney, fat, pancreas, and placenta tissues. Our team (led by Drs. Ucar and Chuang) has broad and extensive expertise in management, integration, and interpretation of complex high-resolution cellular datasets which will allow us to centralize the output of the core teams. The core will be in charge of processing of datasets, analyses and integration, including the construction of senescence maps. The Data Analysis Core will interact closely with the Biospecimen Core and the Biological Analysis Core to systematically receive, assess, organize, and share data from the contributing sites to ensure FAIR (findability, accessibility, interoperability, and reusability) principles throughout this project. Pipelines and analysis methods will be developed with a focus on reproducibility of workflows and robustness of results. Our team has broad and extensive expertise in management, integration, and interpretation of complex high-resolution cellular datasets which will allow us to centralize the output of the core teams. This centralization will maximize the value of the data generated for the KAPP-Sen, for the overall SenNet, and for the broader research community.
The KAPP-Sen Tissue Mapping Center (TMC) Biological Analysis Core will be responsible for generating high- resolution and high-content datasets to define senescent cells and their microenvironment in aged non-diseased human tissues, and measure how such cells compare across a range of ages. We will utilize state-of-the-art single cell technologies on dissociated tissues and on intact tissue sections to study this biology. We will coordinate with our KAPP-Sen Biospecimen Core to obtain high-quality human normal kidney, pancreas, placenta, and adipose tissue. By employing unbiased, sequencing-based, single-cell resolution methods, we will generate high-content spatially resolved data to enable the identification of senescent cells. We will work with our KAPP-Sen Data Analysis Core to discover comprehensive mRNA biomarkers for human senescent cells. A selection of target epitopes derived from these biomarkers will be detected within tissue sections at high resolution (1 µm) utilizing a highly multiplex antibody imaging approach. Additional tangential experiments in human tissues and ex vivo and induced pluripotent stem cell (iPSC) models will further inform and validate senescence signatures, and identify associated epigenomic features, within intact human tissues. The Biological Analysis Core will achieve its objectives through the following Aims: Aim 1. To establish optimal tissue dissociation and preparation techniques to implement both dissociative and spatially-resolved single-cell transcriptome methods for the identification of senescent cells in human tissues. Aim 2. To scale and standardize the pipeline to generate high-quality, high-resolution, and high-throughput datasets and construct maps of cellular senescence in the four target tissues. Aim 3. To identify mRNA biomarkers of human senescent cells and construct and apply a multiplex antibody panel derived from these. Aim 4. Leverage ex vivo human models to further characterize the functional features of senescent cells. Together, this analytic approach will define the comprehensive tissue signature of senescence at 1 µm resolution and begin to uncover the molecular foundations of the senescent cell and its response to therapy. In addition, the data set generated will provide insight into senescence-associated secreted proteins that may inform the design of blood biomarker of senescence. Altogether, our approach and its associated tools will be applicable across a wide array of human tissues types.
