We develop computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing technologies. We apply these methods to study regulatory genomics of cell function and cell-cell interactions in vivo, with a focus on immunology and cancer.
Examples of our past and ongoing work are presented below.
CD8 T cells form the central component of adaptive immune system and are essential in defense against viral and bacterial infections and in tumor immunity. Better molecular characterization of CD8 T cells in different contexts is fundamentally important and can lead to improved clinical results in cancer immunotherapies, infectious diseases, autoimmunity. We performed an extensive genomic, single-cell, and transcription factor analysis of CD8 T cell functional and dysfunctional states. We now continue studying regulatory mechanisms and cell-cell interactions governing CD8 T cell activation and functional commitment across immune challenges using single-cell and spatial multi-omics.
Regulatory T (Treg) cells are critical for tolerance to self-antigens and preventing autoimmunity. Their differentiation and function are controlled by transcription factor Foxp3, but mechanistic understanding of Foxp3 role remains elusive. Using functional genomic analysis, we explored Foxp3 function and its interaction with a highly expressed closely related factor Foxp1. We are now studying the role of Foxp3 and other factors in chromatin organization of Treg cells.
Programmable genome editing using CRISPR has tremendously advanced life sciences. To facilitate the use of this technology, especially in the noncoding genome and for batch screens, we developed GuideScan, a fully customizable CRISPR guide RNA design tool. We now continue working on tools for design and analysis of CRISPR-based genome perturbations and their combinations with single-cell functional genomic assays.
Cross-linking immunoprecipitation followed by sequencing (CLIP-seq) is a family of methods for profiling sites of protein binding to RNA. In particular, CLIP has been used to identify targets of microRNAs, small non-coding RNA molecules that, when bound to a protein Ago2, regulate gene expression post-transcriptionally. We developed a new algorithm CLIPanalyze for analysis of such data and used it for comprehensive analysis of microRNA targets in vivo in mouse embryonic stem cells, developing embryos, adult tissues and multiple cancer models. We now continue development and application of methods for studies of post-transcriptional regulation.
We are actively looking for outstanding scientists at all levels (postdocs, graduate and undergraduate students, staff scientists) across disciplines (computational biology, computer science, machine learning, applied mathematics, immunology, molecular biology) to join our team. Please contact us if interested.
Postdoctoral positions in computational biology are available. We seek candidates with computational, bioinformatics, machine learning, statistics, data science and/or other quantitative backgrounds who are enthusiastic about bringing their expertise to address fundamental problems in biology and medicine using cutting-edge technologies. A successful candidate will have an opportunity to lead and contribute to a range of exciting collaborative projects, and develop new projects. Please apply here or contact us directly.
We are also looking to co-sponsor exceptional candidates for Lewis-Sigler Scholars Program (next application deadline in Fall 2022) and Princeton Bioengineering Initiative Research Scholars (deadline December 15, 2021).
Prospective PhD students interested in working with us are encouraged to apply to Computer Science or Quantitative and Computational Biology graduate programs. We are also accepting Masters students via CS MSE program.
Current Princeton graduate and undergraduate students interested in working with us are encouraged to contact us directly.