Most diseases – ranging from cancers to neurodegenerative disorders - are only clinically diagnosed when they have advanced enough to disrupt the body’s normal function. Unfortunately, by this point, the damage inflicted is often irreversible, and traditional clinical treatments can be detrimental to both the surrounding healthy tissue and the overall well-being of the patient. Understanding the early steps in disease onset would enable earlier diagnosis and facilitate the development of more effective targeted treatments.
The Clement lab aims to shift the paradigm of disease study, diagnosis, and treatment by unraveling the genetic and epigenetic mechanisms of disease initiation. We capitalize on three cutting-edge transformative computational and experimental technologies to discover, model, and validate the fundamental mechanisms underpinning disease.
First, single-cell technology enables the characterization of individual cell states that are otherwise muddled in studies of heterogenous samples. Recently, methods have been developed to assay the single cell genetic mutation status, gene expression, DNA methylation, chromatin accessibility, and other characteristics that allow us to study and compare cells across samples. We develop methods to analyze single-cell data to develop a clear picture of the cellular signals that drive complex diseases.
Second, CRISPR technology allows for targeted genomic modification that can be used in research settings as well as clinical treatment. Our lab harnesses the power of CRISPR perturbation to discover and validate fundamental genetic and epigenetic mechanisms underlying diseases. Our lab is also involved in assuring the accuracy of CRISPR technology in research settings and the efficacy and safety of CRISPR applications in clinical settings, by developing novel tools to assess the on- and off-target activity of CRISPR proteins.
Third, the integration of machine learning and big data methods is transforming almost every aspect of modern life. Our lab harnesses these tools to aggregate large datasets and identify subtle signals that are hard to identify using standard statistical methods. In particular, we are interested in the integration of data from different diseases and genomic modalities to discover elements of disease initiation that may be common across diseases.
By synergistically combining these three innovative technologies, we strive to unravel the complex tapestry of disease initiation to pave the way for improved understanding, detection, and treatment of disease. Our work has previously focused on disease initiation in chronic lymphocytic leukemia, but we are eager to collaborate in the study of additional diseases.
Join us on our journey to use cutting-edge genomics to make a lasting impact on human health.
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