Robi Mitra, PhD

Robi Mitra, PhD

Alvin Goldfarb Distinguished Professor of Computational Biology

Mitra Lab


Bio

Rob Mitra is the Alvin Goldfarb Professor of Computational Biology in the Department of Genetics and the Edison Family Center for Genome Sciences and Systems Biology at Washington University in St. Louis. He received his BS, MS and PhD from MIT. His current research interests are focused on understanding how transcription factors achieve their in vivo specificities, developing new genomic technologies, and applying these to understand disease processes.

Research

Deciphering the Transcriptional Networks that Control Vertebrate Development

Transposon “Calling Cards”

There is still a great deal to learn about the design principles that guide vertebrate development.  Since all cells in an organism contain the same genes, transcribing different sets of genes is what confers a cell’s specialized role. Which genes get turned on or off to create a particular cell type at the right time, in the right place during the development of an organism?  This is one of the pivotal questions in developmental biology. We have developed a technology, Transposon “Calling Cards,” to attack this question in a novel way, and we have shown that it works in yeast and mammalian cell culture. We will produce a record of gene activation at various stages of neural differentiation, watching as stem cells and their progeny specialize. These data will contribute to the field by providing a blueprint for the generation of many cell types, and could ultimately guide the reprogramming of embryonic or induced pluripotent stem cells to produce specific cell types.

Relevant Publications

  1. Wang H, Mayhew D, Chen X, Johnston M, Mitra RD. “Calling Cards” for DNA-Binding Proteins in Mammalian Cells.  Genetics. 2012 Jan 3. PMID: 22214611
  2. Wang H, Mayhew D, Chen X, Johnston M, Mitra RD.  Calling Cards enable multiplexed identification of the genomic targets of DNA-binding proteins. Genome Res. 2011 May;21(5):748-55. Epub 2011 Apr 6. PMID: 21471402
  3. Wang, H, Heinz, ME, Crosby, SD, Johnston, HM, Mitra, RD.  ‘Calling Cards’ method for high-throughput identification of targets of yeast DNA-binding proteins.  Nature Protocols, 2008; 3:1569-1577. PMID: 18802438
  4. Varley KE, Mutch DG, Edmonston TB, Goodfellow, PJ, Mitra, RD. Intra-tumor heterogeneity of MLH1 promoter methylation revealed by deep single molecule bisulfite sequencing.  Nucleic Acids Research 2009Aug; 37(14):4603-12. PMID: 1949418
  5. Varley KE, Mitra RD. (2009)Nested Patch PCR for Highly Multiplexed Amplification of Genomic Loci.  Cold Spring Harb Protoc. Jul;2009(7):pdb.prot5252.PMID: 20147217

Single Molecule Proteomics

Our ability to detect and quantify proteins has lagged behind our ability to analyze nucleic acids. Closing this gap by developing more sensitive and quantitative protein analysis methods would greatly aid efforts to understand cellular processes and the search for protein biomarkers that reveal disease state. We are developing methods to unite the field of protein detection with single molecule counting, with the long-term goal of sequencing single peptide molecules. In collaboration with the Elbert lab, we have characterized two low background surfaces that are highly resistant to protein adsorption.  Using these surfaces, we have demonstrated accurate and sensitive quantification of proteins in serum by single molecule counting on a solid surface. In these experiments, we were able to detect 80% of immobilized molecules by binding fluorescently labeled antibodies and single molecule counting.

Relevant Publications:

  1. Tessler LA, Reifenberger J, Mitra RD. Protein Quantification in Complex Mixtures by Solid Phase Single-Molecule CountingAnalytical Chemistry, 2009, Sep 1;81(17):7141-8.
  2. Tessler LA, Donahoe CD, Garcia DJ, Jun YS, Elbert DL, Mitra RD, Nanogel surface coatings for improved single-molecule imaging substratess. Journal of the Royal Society, Interface, 2011 Epub Feb 16. PMID: 21325313
  3. Tessler, L. A. and Mitra, R. D. (2011), Sensitive single molecule protein quantification and protein complex detection in a microarray format. PROTEOMICS, 11: 4731–4735.

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