Peter V. Kharchenko, Ph.D.

Peter V. Kharchenko, Ph.D.

Gilbert S. Omenn Associate Professor of Biomedical Informatics
Center for Biomedical Informatics, Harvard Medical School
My group aims to develop quantitative tools necessary to analyze and model the state of cells with modern genomic assays. 

This includes statistical analysis of noisy data, characterization of stochastic processes underlying variation of cells within a given sample, as well as modeling of cellular ensembles and tissue organization.  Major methodological and biological directions are briefly described below:

I.  Singlecell measurements.  Most tissues rely on the interplay of multiple cell types to sustain their functions.  Yet the contribution of cell heterogeneity has been largely hidden from our view. The new array of singlecell analysis methods are promising to change that.  The ability to examine genomic properties of thousands of individual cells simultaneously opens the door for unbiased characterization of tissue composition, micro-anatomical organization, interactions between different cell types, and reexamination of the cell type notion itself.

The interpretation of such singlecell genomic studies will be inherently statistical.  However, as illustrated by the transcriptome data, current statistical models may not capture the variability characteristic of singlecell measurements, and for many of the desired analysis no statistical or computational frameworks are available.  Development and novel application of such quantitative methods is the main strength of my group.  We have delivered a number of genomic analysis tools to the scientific community, including methods for analysis of ChIPseq data (Kharchenko et al, Nature Biotech. 2008), analysis of repetitive elements (Day et al, Genome Biol. 2010), or identification of transposable element insertions (Lee E et al, Science 2012).  We are actively developing several statistical and computational tools specifically tailored for analysis of singlecell genomic data (Kharchenko et al., Nature Methods 2014).  My group is applying such methods in the context of close collaborations with multiple experimental groups to study organization of healthy and diseased tissues.

II.  Intratumoral heterogeneity.  The impact of cell heterogeneity on the human health is perhaps most conspicuous in the context of cancer treatment.  Studies of diverse cancers have now revealed that tumor samples typically contain multiple subclonal populations, whose ability to evolve and adapt in response to treatment present a critical challenge to cancer therapy.  The transcriptional heterogeneity within such tumors and its impact on disease progression is poorly understood.  Furthermore, the extent to which genetic and transcriptional subpopulations correspond to each other cannot be currently assessed.  Using singlecell transcriptional measurements and other data we are investigating the distinguishing characteristics of subclonal populations that are resistant to current therapeutic interventions.

III.  Epigenetic regulation.  In addition to studies of human leukemia we are applying similar approaches to understand normal hematopoiesis in human and model organisms.  The model organism setting, in particular, has allowed us to examine the relationship between cellspecific molecular characteristics such as epigenetic state, cellular phenotypes and the overall tissue function.  Current efforts are focused on transitioning to singlecell measurement, analysis and modeling of epigenetic state in normal, cancer and other disease samples.

Selected Publications:

Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol. 2008 Dec; 26(12):1351-9.

Kharchenko PV, Alekseyenko AA, Schwartz YB, Minoda A, Riddle NC, Ernst J, Sabo PJ, Larschan E, Gorchakov AA, Gu T, Linder-Basso D, Plachetka A, Shanower G, Tolstorukov MY, Bishop EP, Canfield TP, Sandstrom R, Thurman RE, Stamatoyannopoulos JA, Kellis M, Elgin SC, Kuroda MI, Pirrotta V, Karpen GH*, Park PJ*. Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Nature, 2011 Mar 24;471(7339):480-5.

Kharchenko PV*, Silberstein L, Scadden DT,  Bayesian approach to single-cell differential expression analysis. Nature Methods, 2014 Jul;11(7):740-2.

Schmidt SC, Jiang S, Zhou H, Willox B, Holthaus AM, Kharchenko PV, Johannsen EC, Kieff E, Zhao B.  Epstein-Barr virus nuclear antigen 3A partially coincides with EBNA3C genome-wide and is tethered to DNA through BATF complexes. Proc Natl Acad Sci USA. 2015 Jan 13;112(2):554-9.

Contact Information

Harvard Medical School
10 Shattuck Street, Room 312
Boston, MA 02115
p: 617 432-7377

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