Multi-Scale Imaging and Informatics Pipeline for In Situ Pluripotent Stem Cell Analysis

Human pluripotent stem (hPS) cells are a potential source of cells for medical therapy and an ideal system to study fate decisions in early development. However, hPS cells cultured in vitro exhibit a high degree of heterogeneity, presenting an obstacle to clinical translation. hPS cells grow in spatially patterned colony structures, necessitating quantitative single-cell image analysis. We offer 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 multi-scale analysis tool groups colony regions into density classes, and cells belonging to those classes have distinct distributions of pluripotency markers and respond differently to DNA damage induction. Lastly, we demonstrate that our pipeline can robustly handle high-content, high-resolution single molecular mRNA FISH data by using novel image processing techniques. Overall, the imaging informatics pipeline presented offers a novel approach to the analysis of hPS cells that includes not only single cell features but also colony wide, and more generally, multi-scale spatial configuration

The Distribution of Genomic Variations in Human iPSCs Is Related to Replication-Timing Reorganization during Reprogramming

Cell fate change involves significant genome reorganization, including change in replication timing, but how these changes are related to genetic variation has not been examined. To study how change in replication timing that occurs during reprogramming impacts the copy number variation (CNV) landscape, we generated genome-wide replication timing profiles of induced pluripotent stem cells (iPSCs) and their parental fibroblasts. A significant portion of the genome changes replication timing as a result of reprogramming, indicative of overall genome reorganization. We found that early and late replicating domains in iPSCs are differentially affected by copy number gains and losses, and that in particular CNV gains accumulate in regions of the genome that change to earlier replication during the reprogramming process. This differential relationship was present irrespective of reprogramming method. Overall, our findings reveal a functional association between reorganization of replication timing and the CNV landscape that emerges during reprogramming

The Distribution of Genomic Variations in Human iPSCs Is Related to Replication-Timing Reorganization during Reprogramming

Cell-fate change involves significant genome reorganization, including changes in replication timing, but how these changes are related to genetic variation has not been examined. To study how a change in replication timing that occurs during reprogramming impacts the copy-number variation (CNV) landscape, we generated genome-wide replication-timing profiles of induced pluripotent stem cells (iPSCs) and their parental fibroblasts. A significant portion of the genome changes replication timing as a result of reprogramming, indicative of overall genome reorganization. We found that early- and late-replicating domains in iPSCs are differentially affected by copy-number gains and losses and that in particular, CNV gains accumulate in regions of the genome that change to earlier replication during the reprogramming process. This differential relationship was present irrespective of reprogramming method. Overall, our findings reveal a functional association between reorganization of replication timing and the CNV landscape that emerges during reprogramming

Single-cell RNA sequencing reveals metallothionein heterogeneity during hESC differentiation to definitive endoderm

Differentiation of human pluripotent stem cells towards definitive endoderm (DE) is the critical first step for generating cells comprising organs such as the gut, liver, pancreas and lung. This in-vitro differentiation process generates a heterogeneous population with a proportion of cells failing to differentiate properly and maintaining expression of pluripotency factors such as Oct4. RNA sequencing of single cells collected at four time points during a 4-day DE differentiation identified high expression of metallothionein genes in the residual Oct4-positive cells that failed to differentiate to DE. Using X-ray fluorescence microscopy and multi-isotope mass spectrometry, we discovered that high intracellular zinc level corresponds with persistent Oct4 expression and failure to differentiate. This study improves our understanding of the cellular heterogeneity during in-vitro directed differentiation and provides a valuable resource to improve DE differentiation efficiency. Keywords: hPSC, Differentiation, Definitive endoderm, Heterogeneity, Single cell, RNA sequencin

Cells in different regions of colonies respond differently to DNA damage.

<p>(A–C) In order to investigate regional heterogeneity, colonies of differentiated and NCS-treated cells were computationally divided into windows, which were then classified according to the density of cells contained. 25 colonies were divided into 164 windows, containing a total of 13,133 cells. As demonstrated, high cell density regions (purple) tend to have low Oct4 (B), and reduced induction of cPARP (C) upon DNA damage, than low (blue) and mixed (red) density regions. (D) Oct4 distribution of the cells in the above classified subregions. Black line shows the distribution of all cells. (E) Proportion of Oct4 positive cells in each subpopulation. (F) proportion of cPARP positive cells.</p