The record of all cell divisions from fertilized egg to adult animal is knownas the cell lineage tree. Current lineage tracing approaches scale poorly towhole, complex organisms. I will describe a new method to trace the lineagetree which uses CRISPR genome editing to progressively introduce andaccumulate mutations in a DNA barcode. The tree can be reconstructed from thepatterns of mutations shared between cells. Combining this approach withsingle-cell RNA sequencing allows cell type and lineage to be measured fromthousands of cells within single animals. This method and related approacheswill enable new studies of cell fate determination, stem cell dynamics anddevelopmental statistics.
Stem cells are important for tissue maintenance, regeneration, and repair. Understanding the mechanisms of stem cell fate is essential to control their behavior for treating damaged and diseased tissue. Hematopoietic stem cells (HSCs) are adult stem cells that reside in the bone marrow and have the potential to self-renew, and to differentiate into more specialized blood and immune cells via a process called hematopoiesis. A significant challenge to studying this process, in addition to the diseases that arise from hematopoiesis going awry (e.g. leukemia), resides in the difficulty of growing HSCs in vitro due to the complexity of the bone marrow microenvironment. In this seminar, I will present recent work from my laboratory to offer new insights gained from using biomedical engineering approaches to better understand the complex bone marrow microenvironment, and how changes in this environment may affect HSC cell-fate decisions. Finally, I will demonstrate how novel genetic tools can be used to control genes and pathways that result in changes in stem cell fate decisions, in addition to reprogramming platelets to function as novel therapeutic diagnostic and delivery vehicles.
Epithelial cells lining different organs turnover by death and division at different rates. While the turnover rate may impact their neoplastic potential, what sets these differential rates is unknown. I investigated what sets the turnover rate in epithelia that turn over the fastest—those lining the gut. While blocking cell extrusion and subsequent cell death or proliferation does not affect the cell proliferation or extrusion rate, respectively, cell migration appears to control extrusion. Moreover, contractile forces play important roles for cell turnover that could impact diseases like Irritable Bowel Syndrome.
Tools such as Crispr/Cas9 have been developed recently that allow us to generate sequence changes at any selected location in a genome. Frequently the technology is used to generate alleles with premature stop codons with the intention of creating loss-of-function mutations in genes of interest. To everyone’s surprise, many of these targeted mutations do not appear to have any effect on development or viability, even when other means of interfering with gene function seem to indicate that the genes in question have essential functions. I think we are learning a lot from these failed experiments! I propose we have a poor understanding of how best to generate loss-of-function mutations. My discussion will explore some aspects of our ignorance about how coding sequences guide the production of proteins. I will discuss evidence that some types of presumed null mutations trigger compensatory responses that obscure the loss-of-function phenotype. It is a fun era ahead to re-think the relation between gene sequence and gene function – geneticists will need help from their math modeling colleagues.
Conjugated polymers form intricate structures at length scales ranging from sub-nanometer to hundreds of microns – spanning the conformations of monomers in the same chain and the packing of adjacent polymer segments from distinct molecules, to a continuum from local aggregate stacking to long range ordering, and mesoscopic grain texture. Due to the many conformational degrees of freedom of their molecular components, the resulting structures are quite heterogeneous.
In this talk, I will discuss experimental and computational work aimed at characterizing the structure of conjugated polymers at the molecular scale with the ultimate goal of understanding (and controlling) their unique optoelectronic properties. I will focus on the connection between structure and charge transport, paying attention to multiscale processes necessary for practical applications in energy generation, conversion, and storage.
Using a variety of machine learning techniques, we look at predictive methods for forecasting both individual chronic diseases andindividual healthcare costs. We also examine some new techniques from high-dimensional geometry to show how certain relations in the datacan be identified that might otherwise be missed by both data analysts and clinicians.