Kah Teong Soh, Ph.D.
I have over 10 years of experience in flow cytometry, beginning my career as an operator sorter (BD FACS Aria) at the Department of Flow and Image Cytometry at Roswell Park Comprehensive Cancer Center. Since then, I have operated a range of instruments including BD’s LSR II and LSRFortessa, Agilent/ACEA Bioscience’s NovoCyte, Beckman Coulter’s CytoFLEX, SONY’s MA900 Cell Sorter, and Cytek’s Aurora. Aside from conventional flow, I am also familiar with confocal microscopy, molecular techniques (e.g. qPCR), and ImageStream cytometry.
I am presently employed as a Scientist at Agenus, where my primary role is to develop assays that facilitate drug discovery. I created and implemented an in-house immunogenicity assay that uses immature dendritic cells pulsed with therapeutic agents, for which upon maturation can induce the proliferation of CD4 T lymphocytes, using flow cytometry as the final readout. I am also responsible for managing the Flow Cytometry Core Facility at Agenus, ensuring that all flow cytometers are maintained, and instrument downtime is minimized.
As part of my role, I have developed curriculums to provide introductory and advanced flow cytometry courses for new and existing employees. I have also established a network connecting all computers in the FACS room to the High-Performance Computing (HPC) cloud server, providing higher computing power for high-dimensional flow cytometric data analysis and enabling direct data transfer. Additionally, I routinely collaborate with other investigators to optimize and validate high-dimensional flow cytometric immunophenotyping panels to address various scientific inquiries.
Prior to joining Agenus, I spearheaded an international initiative that brought together clinical cytometrists from 13 countries to create a consensual flow cytometric approach for measuring measurable/minimal residual (MRD) disease in patients with multiple myeloma. In 2021, I designed a high-dimensional 27-color panel using Full Spectrum Flow Cytometry (FSFC) that employs a supervised, automated approach to data analysis to identify MRD in patients with acute myeloid leukemia.