Stem Cell Lineage Analysis and Control

Eric Jervis
Associate Professor,
Department of Chemical Engineering
University of Waterloo

May 11, 2005
3:30 PM - 5:00 PM
Davis Centre 1304, University of Waterloo

View Video of Presentation in HI Alive Archive: Research Seminars Archive 2004-2005

Abstract
The Jervis laboratory has a big problem – we have too much image data. Currently we have terabytes of high resolution microscopy data from long term time course imaging of stem cells in culture. Our image sets allow us to create lineage maps of the cells during growth and ideally relate observed behaviors to cell potential for the controlled production of cells for implantation.

There are very few systems that afford the ability to fully characterize the potential of a single individual stem cell. To address this, the Stem Cell Network NCE provided support for the “Long Term Imaging and Cell Tracking for Stem Cell Lineage Analysis” project. The primary objective of the project is to produce high resolution time course image data sets of stem cells with a variety of tissue formation potentials. It is hypothesized that these observational data sets may yield characteristic stem cell behaviors that can be used to identify “desired” cells in real time during culture – in essence a bioinformatics approach to stem cell lineage analysis based on observed cell properties for process control. Specifically, we seek to identify the environmental influences on a stem cell that determine its lineage progression (i.e. symmetric versus asymmetric cell division) and proliferation kinetics (i.e. expansion of stem cell numbers).

The lineage informatics approach will be illustrated with 3 recent studies. In the first, we have applied lineage analysis to the growth of neural stem cell colonies and examined the cell types generated following differentiation. The power of this approach is demonstrated by studying the genesis, heritability, fate determinants, and plasticity of a readily identifiable, novel phenotype that contributes to the heterogeneity of the progenitor population. In this test case intrinsic factors were shown to be dominant over microenvironment.

In a second example, we imaged highly purified populations of blood stem cells in an array chamber where proliferation and survival history of each cell and its progeny could be monitored over 104 hours. At the end of tracking, each clone of cells was harvested individually and evaluated using in vivo repopulation assays. We are currently evaluating the predictive ability of lineage patterns for identifying blood repopulating cells. In a final example, lineage tracking was used to examine inheritance patterns of reporter gene expression in retrovirus transformed embryonic stem cells (as a model for gene therapy and delivery). Synchronized switching of reporter gene expression demonstrated that expression levels were inherited and that expression level trajectories can be maintained over several generations.

Help. I am drowning in high resolution space-time data of stem cells “behaving” in culture. I have terabytes of data that needs to be feature-extracted and classified. To date I have been using CO-OP students for this work. However, this can not go on as I have been cited for driving too many Co-op students crazy.

Biosketch
Eric Jervis is an Associate Professor in the Department of Chemical Engineering at the University of Waterloo. Eric obtained a Bachelor of Applied Science (1986 Bio-Resource Engineering), a Master of Applied Science (1992 Chemical Engineering), and a Ph.D. in Applied Science (1998 Chemical Engineering), all from the University of British Columbia. He joined the University of Waterloo in 1998. He has cross appointments in the departments of Biology and Physics at the University of Waterloo.