(7ai) Uncovering Cellular Heterogeneity in Complex Tissues through Single Cell Transcriptomics: Structure, Development, and Dysfunction

Molecular characterization and classification of cell types is increasingly viewed as essential for understanding the structure, development, function and dysfunction of complex tissues such as the nervous system. For example, it enables genetic access; facilitates comparison of results across laboratories; and provides a foundation for seeking alterations in disorders such as stroke, glaucoma and Alzheimer’s disease. As a starting point, we are applying large-scale single-cell transcriptomic analysis to generate a complete “cell atlas” of the mouse retina, a part of the central nervous system that is highly complex but uniquely accessible to study. Current estimates are that there are 100-150 retinal cell types in vertebrates. An initial proof-of-principle survey of 44,808 mouse retinal cells using a droplet microfluidics method that I helped developed (Drop-seq), identified only 39 types (Macosko et al., Cell, 2015); much of the heterogeneity was likely masked due to the preponderance of rod photoreceptors (~80% frequency). To circumvent this limitation, I am collaborating with leading retina groups at Harvard to isolate three most heterogeneous classes (bipolar, amacrine and retinal ganglion cells (RGCs)) before transcriptomic profiling. In a study of 27,994 bipolar cells (Shekhar et al., Cell, 2016), I developed improved computational approaches and with experimental collaborators, combined FISH with virus-mediated sparse labeling to match molecular with morphological criteria for defining types. We thereby identified all 12 types described previously, and found 3 novel types. We are now advancing these results in several ways: (1) By analyzing transcriptomes of >40,000 adult RGCs using improved computational methods, we have classified, and identified molecular markers for ~50 RGC types. (2) Transcriptomic comparison with RGCs profiled at P5 has provided insights into the maturation of specific cell types. (3) We are conducting a similar classification of RGC types in macaque and zebrafish with the hope of generating a systematic cross-species comparison of neuronal types within a diverse class. (4) Many RGCs die following nerve injury, with some RGC types exhibiting better survival rates than others (Duan et al., Neuron, 2015). We are comparing changes in gene expression among types following injury to identify early transcriptional signatures that correlate with, and may underlie, selective resilience. The approaches and technologies we are developing will, in the future, aid similar studies in other tissues.

Teaching Interests:

My undergraduate degree in Chemical Engineering at the Indian Institute of Technology (IIT), Mumbai, and my graduate work at the Massachusetts Institute of Technology (Advisor: Prof. Arup K. Chakraborty) exposed me to a rigorous academic curriculum enabling me to build a solid foundation in mathematics, physics and computation. From a very early stage, I was most excited about working at the interface of the physical and the life sciences. As a teacher, my dream is to inspire my students to deeply engage with the core academic rigor in Chemical Engineering, and push their boundaries to apply these skills to fundamental challenges in biology. Given my background in thermodynamics and statistical physics, I would be comfortable teaching core undergraduate and graduate level courses in these areas, as well as an elective course that has a biological orientation. I was a teaching assistant for the Graduate Thermodynamics course at MIT (10.40, Instructors: Arup Chakraborty and Bradley Olsen), for which I received the “Best Teaching Assistant Award” in 2012. A lot of my current work is focused on developing single-cell measurement technologies rooted in microfluidics (on the experimental side) and developing fast and efficient analysis approaches in genomics rooted in machine learning and data science (on the computational side). One of my dreams as a teacher is to develop an early graduate course that exposes chemical engineering students to these areas, while at the same time leveraging core skills that are integral to a chemical engineering education.