We are exploring the use of cell mass and mass distribution measurements via quantitative phase microscopy (QPM) to develop new approaches to study and treat human disease, including cancer. Our work includes collaborations with researchers at the University of Utah, Huntsman Cancer Institute, and other institutions.

Cancer Immunotherapy

Adoptive immunotherapies against cancer, in which cytotoxic, CD8+ T cells engineered to express T cell receptors (TCRs) targeting cancer-associated antigens are transplanted into a patient, have shown dramatic promise in clinical trials. A major impediment to the widespread use of this technique for treatment of diverse cancers is the lack of a fast approach for the identification of TCRs from patient samples. QPM provides a method for high-throughput screening of T cell/cancer cell interactions by measurements of cell biomass. This live cell approach is label-free and allows cells to be recovered for downstream analysis. To ensure specificity, three parameters are tracked: target (cancer) cell appearance, target cell mass loss during cell death, and T cell mass during and after the cytotoxic event (Figure panels a-c). Our results demonstrate the kinetics of T cell mass increase during activation as well as a rapid approach to identify specific, activated patient-derived T cells for applications in cancer immunotherapy. (link)

T Cell

Stem Cell Pluripotency

Despite the potential high impact of human pluripotent stem cell (hPSC) research in developmental biology, cancer biology, and regenerative medicine, surprisingly little is known about how hPSCs grow, divide, and respond to their environment. hPSC colony mass measurements via QPM provide a new, biophysical measurement approach for precisely quantifying hPSC colony mass distributions and growth rates (Figure A,B,D,F). Our measurements with QPM show that retinoic acid-induced differentiation minimally slows the rate of mass accumulation, a surprising result considering the large metabolic and proliferative changes associated with the transition away from the pluripotent state. We also present methods to quantify the rate and coordination of intracolony motion from colony mass distribution measurements (Figure C,G). Differentiated colonies exhibit a significantly slower rate of mass motion and significantly less coordination of motion, a previously unknown behavior that may provide new information on the health and differentiation potential of available hPSC lines. (link)


Cancer Cell Drug Response

A major challenge in the use of chemotherapeutics is the selection of appropriate drug treatments. Conventional proliferation assays for measurement of drug sensitivity require monitoring cells for days in culture, which limits their applicability to patient tumor samples. QPM precisely quantifies the mass of hundreds of individual cells over time (Figure). Responsiveness to a drug treatment can be determined from this data by looking for cell death or decreases in the rate of mass accumulation rate, on a cell-by-cell basis. QPM therefore provides a sensitive, label-free method to track the effects of drug treatments on cancer cell growth, in a fraction of the time of conventional proliferation assays. (link 1) (link 2)

Drug Response

Mass Partitioning During Cell Division

Cell division is fundamental to many processes in human health and disease and the equal partitioning of cellular mass between daughters is the usual and expected outcome of cytokinesis for self-renewing cells. QPM quantifies the partitioning of daughter cell mass during and following cytokinesis. On average, mass asymmetries present at the time of cleavage furrow ingression persist through cytokinesis. The addition of multiple cytoskeleton-disrupting agents leads to increased asymmetry in mass partitioning which suggests the absence of active mass partitioning mechanisms after cleavage furrow positioning. (link)

Cell Division


Ongoing work will focus on

  • Development of complementary measurements and cell-handling capabilities
  • Transport modeling
  • Development of image processing algorithms for QPM
  • Collaboration with colleagues on applications of QPM to clinical or basic biological research