I am a computational biologist at Immunai, where I work as part of the the deep analytics team to understand human immune response using a wealth of single-cell omics data.
Previously, I did my undergraduate degree at the Ohio State University, where I obtained my B.S. in Applied Mathematics and B.A. in English. During my undergrad I dabbled in two main research projects, the first involving biophysical modeling of ischemic stroke, and the second involving statistical method development for outlier detection in cancer RNA-Seq data. Subsequently, I decided to pursue a PhD in the Tri-Institutional Computational Biology and Medicine program at Weill Cornell Medicine where I worked in the Tilgner lab. During my time there I focused on technology and algorithm development for studying alternative splicing in the brain at the single cell/single-nucleus level. I then did a short postdoc in the New York Genome Center (NYGC) and Columbia University DBMI where I develope algorithms to study how the 3D structure of the genome impacts nuclear splicing in different cell types.
While my interest in the omics has persisted, I have realized over time that there are different ways of tackling questions related to the structural and molecular aspects of function. Long term, I want to use the skills and intuition I develop to contribute to understand how the human immune system works and contribute to translational medicine.
In general, I get very excited when people push the boundaries of what is known in science, particularly when a clever new technology is involved. Outside of science, I enjoy theater and all things literary, and occasionally indulge in the circus arts.
_Nature Communications, 2021_
Abstract:
Splicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 (www.isoformAtlas.com). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity.
_Nature Biotechnology, 2022_
Abstract:
Single-nuclei RNA sequencing characterizes cell types at the gene level. However, compared to single-cell approaches, many single-nuclei cDNAs are purely intronic, lack barcodes and hinder the study of isoforms. Here we present single-nuclei isoform RNA sequencing (SnISOr-Seq). Using microfluidics, PCR-based artifact removal, target enrichment and long-read sequencing, SnISOr-Seq increased barcoded, exon-spanning long reads 7.5-fold compared to naive long-read single-nuclei sequencing. We applied SnISOr-Seq to adult human frontal cortex and found that exons associated with autism exhibit coordinated and highly cell-type-specific inclusion. We found two distinct combination patterns: those distinguishing neural cell types, enriched in TSS-exon, exon-polyadenylation-site and non-adjacent exon pairs, and those with multiple configurations within one cell type, enriched in adjacent exon pairs. Finally, we observed that human-specific exons are almost as tightly coordinated as conserved exons, implying that coordination can be rapidly established during evolution. SnISOr-Seq enables cell-type-specific long-read isoform analysis in human brain and in any frozen or hard-to-dissociate sample.