Single cell multi-omics approaches in identifying driving cells and genes in pediatric MODS patients requiring ECMO support

PROJECT SUMMARY My long-term goal is to develop innovative diagnostic and therapeutic methods for pediatric diseases in the intensive care unit (ICU), using cutting-edge single-cell multi-omics techniques and machine learning approaches. My previous research focused on identifying novel drugs, biomarkers, prognostic, and diagnostic

markers for various diseases and cancers using big data approaches. However, MODS in pediatric patients with veno-arterial extracorporeal membrane oxygenation (ECMO) support has drawn my attention, as the survival rate for severe MODS patients with ECMO support is very low and there are no clinical or molecular biomarkers

available to diagnose deterioration of MODS patients requiring ECMO support. To gain a deeper understanding of the cellular and molecular mechanisms associated with MODS deterioration who may require advanced life support, I aim to utilize advanced single-cell multi-omics and machine learning approaches. My previous

research using bulk RNA-seq analysis on a small sample size showed that dysregulation of the immune response and epigenetic variations are significant factors associated with MODS deterioration in patients requiring ECMO support. However, a more detailed analysis using advanced single-cell multi-omics and machine learning is

needed. The objective of this award is to use cutting-edge single-cell multi-omics techniques to investigate the cellular heterogeneity and their dynamics, as well as their transcriptional regulatory elements in MODS and ECMO patients at two different time points. Furthermore, a machine learning approach along with bulk RNA-seq

will be implemented to identify crucial cells and genes associated with MODS patients’ deterioration who may require ECMO support. The successful completion of this research will expand my knowledge in single-cell multi- omics, statistics, machine learning approaches, immune responses, and pediatric diseases. I have gathered a

team of eight renowned experts in the fields of big data science, immunology, single-cell multi-omics, pediatrics rare diseases, statistics, rare diseases, machine learning, and immunology to act as my mentors, advisors, and collaborators. These experts include: Primary Mentor Dr. Bin Chen in translational bioinformatics from MSU, co-

mentor Dr. Qing-Sheng Mi in immunology and single-cell multi-omics from Henry Ford Health System, co-mentor Dr. Surender Rajasekeran in pediatrics rare diseases from Spectrum Health Hospital, co-mentor Dr. Lana Garmier in single-cell multi-omics and immune disease from the University of Michigan, Advisor Dr. Haiyan

Huang in statistics from the University of California, Dr. Jeremy Prokop in rare diseases from MSU, Dr. Jilian Tang in machine learning and single-cell multi-omics from MSU, and Dr. Mei-Sze Chua in immunology from Stanford University. With the support of my mentors, advisors, and collaborators, this award will prepare me to

develop a deeper understanding of MODS research and the application of single-cell multi-omics in translational research with a broad impact.