The Heilmeier Award honors a Penn Engineering faculty member whose work is scientifically meritorious and has high technological impact and visibility. It is named for the late George H. Heilmeier, a Penn Engineering alumnus and member of the School’s Board of Advisors, whose technological contributions include the development of liquid crystal displays and whose honors include the National Medal of Science and Kyoto Prize.
Raj, who also holds an appointment in Genetics in the Perelman School of Medicine, is a pioneer in the burgeoning field of single-cell engineering and biology. Powered by innovative techniques he has developed for molecular profiling of single cells, his scientific discoveries range from the molecular underpinnings of cellular variability to the behavior of single cells across biology, including in diseases such as cancer.
Raj will deliver the 2023-24 Heilmeier Lecture at Penn Engineering during the spring 2024 semester.
This story originally appeared in Penn Engineering Today.
Speaker: Siyu (Sisi) Chen, Ph.D.
Senior Research Scientist
Director of Beckman Institute Single-cell Profiling and Engineering Center
California Institute of Technology
Date: Thursday, February 25, 2021
Time: 3:00-4:00 PM EST
Zoom – check email for link or contact firstname.lastname@example.org
Title: “Dissecting Multicellular Therapeutic Responses Using a Large-scale Single-cell Profiling Platform”
Human diseases are fundamentally multicellular in nature with many different cell types contributing to disease progression and treatment response. However, how therapeutics impact each cell type in a heterogeneous population remains poorly understood because most studies are focused on isolated cell types or a handful of pathways. Now, single-cell transcriptional profiling methods allow us to collect a deep molecular portrait of the collective response of heterogeneous populations of cells to any perturbation. In my talk, I will present my research in harnessing the power of single-cell transcriptional profiling measurements to dissect therapeutic response in heterogeneous cell populations. In the first part, I will describe the probabilistic modeling framework I developed for analyzing single-cell population data across perturbations at scale (PopAlign). PopAlign models single-cell data with semantically interpretable, low-error, highly-compressed probabilistic models, which allows fast comparisons across hundreds of samples. In the second part, I will discuss how I applied this framework to analyze a drug response study of over 1.6M human primary immune cells to 500 commercially-available immunomodulatory compounds. While most compounds in the library exert broad impact across multiple cell types in the population, my analysis also reveals highly cell-type specific activity, including a novel myeloid-suppressing function of a group of compounds including NSAIDs and an artificial sweetener. My work provides new depth and insight into how existing compounds reshape immune populations, and a general platform for evaluating and designing population-level responses to therapeutic interventions.
Sisi Chen is a Senior Research Scientist and the Director of the Beckman Single-cell Profiling and Engineering Center (SPEC) at Caltech, where she leads a team focused on single-cell technology development. She completed her B.S. in Electrical Engineering at MIT, and her Ph.D. in Bioengineering at UC-Berkeley/UCSF, where she was an NSF and NDSEG fellow working on microfluidic tools for single-cell biology. Most recently, she has developed a computational platform to analyze single-cell transcriptional data at large-scale, and has used this platform to map human immune system responses to hundreds of small molecule immunomodulatory compounds. Her research blends experimental and computational approaches to learning and controlling the collective response of multicellular tissues to therapeutic interventions.