How do centenarians delay and defy aging?
The Center for Quantitative Methods and Data Science, in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center and the Data-Intensive Studies Center (DISC) will host a virtual seminar series on a Wednesday each month from 2:00-3:00PM.
The session on Wednesday, January 20 will feature Research Assistant and PhD Candidate Tanya Karagiannis, MS. She will give a talk titled Analysis of Single Cell Transcriptomics Data as it Relates to Aging and Longevity.
Studies of aging have shown a gradual decline in the immune system, such that people experience age-related disabilities and diseases as well as differences in immune population composition and functions over time. However, a rare population of individuals who reach 100 years of age known as centenarians, experience delay in age-related disabilities and diseases and in fact live the majority of their lives in good health. In order to investigate how centenarians delay and defy aging, we utilize single cell transcriptomic methods to investigate longevity related differences in the peripheral blood immune system of centenarians.
Single cell level transcriptomic data has allowed for the profiling of thousands of cells to characterize cell states and populations in specific tissues. More specifically, these methods can be used to identify rare populations and assess transcriptional similarities and differences within a population of cells. We describe integrated analyses using four single cell RNA-sequencing datasets that we conducted to investigate compositional and gene expression differences in immune populations of centenarians and younger age controls (20-80 years).
Early findings demonstrate gene expression differences between centenarians and younger age controls that are specific to populations of cells. We also find centenarians not only have cell type specific compositional differences but overall have more cell type diversity than younger age controls.
Tanya Karagiannis is a Research Assistant at Tufts Medical Center in the Institute for Clinical Research and Health Policy Studies, working with Dr. Paola Sebastiani. She has an MS in Bioinformatics from Boston University where she is also continuing her PhD in Bioinformatics under the advisement of Dr. Paola Sebastiani and Dr. Stefano Monti. Her research focus is in the application and development of single cell transcriptomic methods utilizing machine learning and Bayesian statistics, with interest in multi-omics as well.
Date: Wednesday, January 20, 2020, 2:00-3:00PM
To attend, please enroll via Tufts CTSI I LEARN here.