An integrative computational-experimental method resolving single cell regulatory genomic networks.

Important results from the project

Objective of this project (updated) is to develop an integrative computational-experimental method, which will enable us to stratify a population of cells into their corresponding functional sub-groups. Our laboratory has established well customized protocols for single cell sequencing for CD4+ T-cells and their basic pipeline for data processing. In addition, we are now working on state of the art technology (10x genomics machine) to extend our target cell types to more global.

Expected long term effects

My project is still in progress, we could have established well customized protocol, and this is the first data showing uniqueness of regulatory T-cells (Treg) with unsupervised way (basically Treg cells have been analyzed as a cell population with CD25 positive CD4 T-cells, but we could identify it more correctly). Now we plan to do more single cell analysis with human T-cells from patients with Multiple Sclerosis, which is one of autoimmune diseases. These established technologies will contribute to the development of new therapy for immune-related diseases.

Approach and implementation

This project has been changed partially, because of more difficulty in single cell analysis of target cell population (CD4+ T-cells) than expected. But established protocol works with human T-cells clearly better than published data. From this point of view, this project is delayed, but meaningful and will be continued with this. We have had several established mathematical methods for analysis of single cell data. In addition, we are now especially interested in applying artificial intelligence (AI) to single cell data. These will be applied to experimental data near future.