We are a computational biology group at the Department of Biomedical Informatics of the Harvard Medical School. Our broad aim is to understand the epigenetic mechanisms regulating growth and maintenance of normal tissues, and the extent to which disruptions of the epigenetic state contribute to aging, cancers, and other diseases that distort tissue balance.
We specialize in development of methods for statistical and integrative analysis of the functional state of the cell from high-throughput data, such as RNA-seq, ChIP-seq, quantitative mass spectrometry, or flow cytometry. A wetlab component of the lab is aimed at automating and scaling up key functional genomics assays using microfluidics. Much of our work is conducted in close collaborations with experimental groups, working on a variety of disease and organism models.
Single-cell RNA-seq measurements provide a powerful approach for studying complex biological tissues. Some of the more interesting contexts involve dynamic processes, such as development or disease progression. However single-cell measurements only capture a snapshot of a transcriptional state at a single point in time. To infer dynamics of the cells, together with Sten Linnarsson's group, we have developed a method (velocyto) to estimate time derivative of the transcriptional state for individual cells. This provides basis for quantitative modeling of cell dynamics and the associated regulatory processes.
Exploring Transcriptional Heterogeneity
Single-cell mRNA-seq now allows to measure snapshots of transcriptional state for thousands of cells. Such measurements can be used to explore composition of complex tissues in the context of both healthy homeostasis and disease. Identifying transcriptional subpopulations and features that separate within a given cell population can be challenging, particularly when there are multiple valid criteria on which the cells can be distinguished. For instance, multiple cell types in the mixture may be going through cell cycle and therefore share a very prominent mitosis signature, which may dominate the resulting cell classification. We have developed Pagoda and Pagoda2 for analysis, visualization and interactive exploration of single-cell RNA-seq datasets.
Repression of Fetal Hemoglobin
Mutations in adult-type globins underlie several diseases, including sickle cell disease and thalassemia, and are a significant public concern. One promising approach for their treatment would be re-activation of the fetal-type hemoglobin in adult erythroid cells. Fetal-type hemoglobin is normally repressed soon after birth, as the adult-type globin genes take over. The exact mechanism of this repression is not known, though earlier studies have implicated BCL11A as one of the necessary factors. In our collaboration with the laboratory of Takahiro Maeda from the Children's Hospital, Boston, we have investigated the contribution of another factor, LRF, to repression of fetal-type hemaglobin. Through transcriptional and epigenetic analysis we show that LRF acts independently of BCL11A to repress the fetal hemaglobin through direct interaction with these loci. Science [DOI:10.1126/science.aad3312]
The throughput and accuracy of the modern sequencing assays can be improved by performing key steps within automated microfluidic devices. Small volumes allow one to increase the effective concentration of the reagents while reducing sample requirements, and computer-driven control can reduce technical variability.
We are adapting the devices designed by leading microfluidics groups, and working on custom chips to enable high-throughput versions of assays developed by our experimental collaborators.