Fellow or Postdoc
Hyunsun Lee, Majd Al Suqri, Joyce Kang, Cristian Valencia, Yuriy Baglaenko, Laura Cooney, Lauren Higdon, Mark Anderson, Soumya Raychaudhuri
Recent advancements in single-cell sequencing methods unveiled the cellular heterogeneity and dynamics of individual cells, surpassing the limitations of conventional bulk-population level measurements. Moreover, the emergence of simultaneous multimodal single-cell profiling opens new frontiers in single-cell genomics, necessitating robust computational approaches to define cell states based on multimodal data. In this study, we present an experimental and analytical framework based on peripheral blood mononuclear cells obtained at the baseline visit of patients in the autoimmunity-blocking antibody for tolerance (AbATE) clinical trial and in the high-dose immunosuppression and autologous transplantation for multiple sclerosis (HALT-MS) clinical trial at Immune Tolerance Networks (ITN). Equal numbers of sorted T cells (CD3+CD19-CD14-CD56-) and B cells (CD3-CD19+CD14-CD56-) from each subject were pooled for droplet-based single-cell assays and subsequent analyses. Simultaneous sequencing of the multiplexed samples was performed using two sequencing platforms: single-cell multiome ATAC+Gene Expression and CITE-seq along with TCR/BRC repertoire. For downstream analysis, we merge the RNA data from both platforms through reference mapping technique and integrate surface protein, BCR/TCR, and open chromatin information within the annotated clusters to identify and characterize T/B cells. This experimental and analytical framework exemplifies the potential applications of simultaneous multimodal single-cell profiling in tracking temporal changes during immunomodulatory therapies.
New technologies have recently allowed scientists to look at individual cells in a whole new way. They found that cells in our blood are not all the same and can change over time. Studying different types of cells in an individual cell level is a big improvement over older methods where we looked at groups of cells together. We now study many things about a single cell all at once, like its genes and proteins. This helps us understand how cells work and how they respond to different treatments. In this study, we looked at specific immune cells taken from patients in clinical trials. They mixed these cells together and used special tools to learn about their genes, proteins, and other important information. By looking at different aspects of the cells, it can help us learn more about how treatments affect the immune system and improve therapies for diseases. In simpler terms, this research uses advanced AI methods to examine individual cells and possible changes when patients receive treatments. This can give us valuable insights into how our immune system responds to therapies and helps us find better ways to treat diseases.