Multi-scale imaging and informatics pipeline for in situ pluripotent stem cell analysis

Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 86-97).
Human pluripotent stem (hPS) cells have the ability to reproduce indefinitely and differentiate into any cell type of the body, making them a potential source of cells for medical therapy and an ideal system to study fate decisions in early development. However, hPS cells exhibit a high degree of heterogeneity, which may be an obstacle to their clinical translation. Heterogeneity is at least partially induced as an artifact of removing the cells from the embryo and culturing them on a plastic dish. hPS cells grow in spatially patterned colony structures, which necessitates in situ quantitative single-cell image analysis. This dissertation offers a tool for analyzing the spatial population context of hPS cells that integrates automated fluorescent microscopy with an analysis pipeline. It enables high-throughput detection of colonies at low resolution, with single-cellular and sub-cellular analysis at high resolutions, generating seamless in situ maps of single-cellular data organized by colony. We demonstrate the tool's utility by analyzing inter- and intra-colony heterogeneity of hPS cell cycle regulation and pluripotency marker expression. We measured the heterogeneity within individual colonies by analyzing cell cycle as a function of distance. Cells loosely associated with the outside of the colony are more likely to be in G1, reflecting a less pluripotent state, while cells within the first pluripotent layer are more likely to be in G2, possibly reflecting a G2/M block. Our analysis tool can group colony regions into density classes, and cells belonging to those classes have distinct distributions of pluripotency markers and respond differently to DNA damage induction. Our platform also enabled noninvasive texture analysis of live hPS colonies, which was applied to monitoring subtle changes in differentiation state. Lastly, we demonstrate that our pipeline can robustly handle high-content, high-resolution single molecular mRNA FISH data by using novel image proc
by Bryan Robert Gorman.
Ph. D.