Immunology PhD candidate in Dr. Ravi Majeti lab; looking at AML through the lenses of cancer biology, stem cell biology, and immunology. Dancer & BioAIMS president.
Honors & Awards
Amgen Scholar, Amgen Foundation (Jun - Aug 2014)
Gene Brown Prize for Teaching, MIT Department of Biology (June 2015)
Stanford Graduate Fellowship, Stanford University
NSF Graduate Research Fellowship, National Science Foundation
Stanford Biosciences Travel Grant, Stanford Biosciences (Aug 2018)
Professional Affiliations and Activities
Member, International Society of Experimental Hematology (2018 - Present)
Education & Certifications
BS, Massachusetts Institute of Technology, Biology (2015)
Ravindra Majeti, Doctoral Dissertation Advisor (AC)
Current Research and Scholarly Interests
Germline mutations in RUNX1 cause an autosomal dominant disorder characterized by lifelong thrombocytopenia and increased risk of progression to acute myeloid leukemia (AML). Indeed, unlike sporadic AML, which commonly presents in the elderly, the average age of onset for RUNX1 familial AML cases is 35, with over one-third of patients developing leukemia as a child. While megakaryocyte defects have been shown to be a cell-autonomous effect of RUNX1 mutations in hematopoietic stem and progenitor cells (HSPCs), the mechanisms by which germline RUNX1 mutations progress to leukemia remains unclear. Interestingly, RUNX1 is also expressed in bone marrow mesenchymal stromal cells (BM-MSCs), which have been shown to contribute to the pathogenesis of some hematopoietic malignancies. The goal of my thesis research is to determine how RUNX1 mutations may be contributing to leukemogenesis through both cell autonomous and non-autonomous mechanisms.
Ravindra Majeti, (6/1/2017)
Academic Research Technician, Broad Institute of MIT & Harvard (January 2015 - May 2016)
Investigate how heterogeneous regulation of LPS modifications by PhoPQ and PmrAB in S. Typhimurium modulate Type I IFN response in mouse macrophages. Developed single-cell RNA-Seq method to simultaneously probe host and pathogen transcriptomes.
- Azacitidine and Ascorbate Inhibit the Competitive Outgrowth of Human TET2 Mutant HSPCs in a Xenograft Model of Pre-Leukemia AMER SOC HEMATOLOGY. 2018
scDual-Seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNA-sequencing
2017; 18: 200
The interaction between a pathogen and a host is a highly dynamic process in which both agents activate complex programs. Here, we introduce a single-cell RNA-sequencing method, scDual-Seq, that simultaneously captures both host and pathogen transcriptomes. We use it to study the process of infection of individual mouse macrophages with the intracellular pathogen Salmonella typhimurium. Among the infected macrophages, we find three subpopulations and we show evidence for a linear progression through these subpopulations, supporting a model in which these three states correspond to consecutive stages of infection.
View details for DOI 10.1186/s13059-017-1340-x
View details for Web of Science ID 000413769600001
View details for PubMedID 29073931
View details for PubMedCentralID PMC5658913
A highly multiplexed and sensitive RNA-seq protocol for simultaneous analysis of host and pathogen transcriptomes.
2016; 11 (8): 1477-1491
The ability to simultaneously characterize the bacterial and host expression programs during infection would facilitate a comprehensive understanding of pathogen-host interactions. Although RNA sequencing (RNA-seq) has greatly advanced our ability to study the transcriptomes of prokaryotes and eukaryotes separately, limitations in existing protocols for the generation and analysis of RNA-seq data have hindered simultaneous profiling of host and bacterial pathogen transcripts from the same sample. Here we provide a detailed protocol for simultaneous analysis of host and bacterial transcripts by RNA-seq. Importantly, this protocol details the steps required for efficient host and bacteria lysis, barcoding of samples, technical advances in sample preparation for low-yield sample inputs and a computational pipeline for analysis of both mammalian and microbial reads from mixed host-pathogen RNA-seq data. Sample preparation takes 3 d from cultured cells to pooled libraries. Data analysis takes an additional day. Compared with previous methods, the protocol detailed here provides a sensitive, facile and generalizable approach that is suitable for large-scale studies and will enable the field to obtain in-depth analysis of host-pathogen interactions in infection models.
View details for DOI 10.1038/nprot.2016.090
View details for PubMedID 27442864