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Live Cell Imaging of Induced Pluripotent Stem Cell Populations

Summary

Induced pluripotent stem cell (iPSC) populations are complex, dynamic and heterogeneous. Individual cells within a population are constantly changing, while maintaining the capacity to differentiate in numerous possible cell types. Sophisticated measurement tools are required to adequately describe and develop predictive models for complex cellular systems such as these. The technology to record live cell images from cellular populations has been available for some time, but only recently has it become routine to derive quantitative data from these image sets using image analysis. We have focused on developing live cell imaging tools to monitor large numbers of single cells and to quantify changes in morphology and gene expression using fluorescence protein reporters.

Description

Live cell autosegmentation and tracking

Automated segmentation and tracking of individual cells

Our live cell image program supports the advancement of iPSC technology in three ways:

1) Identification of process control measurements: A critical component to the translation of iPSCs into therapeutic applications is to design principles for predictably and reproducibly culturing cells and efficiently differentiating them into cell types of interest. Live cell imaging provides ‘high-resolution’ measurements in the sense that we collect time-dependent data from large numbers of individual cells. We then use this data to discover lower resolution measurements, such as the activity of a biomarker at a single point in time, that can serve as critical process control points during processing of pluripotent stem cells.

2) Interpreting biomarkers: Cells are stochastic and dynamic and may interconvert between states and the expression of biomarkers can change over time. The predictive power of a biomarker or a set of biomarkers the indicate the differentiated state of a cell can be evaluated by examining the history of that cell by tracking forward and backward in time through a time lapse image set.

3) Predictive modeling: We have shown that fluctuations in promoter activity can be used in combination with appropriate models to predict rates of state change in cell populations. Similar mathematical models that can inform bioprocessing decisions during scale-up will be critical to obtaining iPSC populations with a desired set of characteristics.

Over the past several years, we have developed tools for measuring parameters related to size, shape and intensity from single cells over time (Halter Cytometry 2011). We have also developed modeling tools for using the temporal information to model the stochastic and deterministic components of gene expression (Sisan PNAS 2012; Lund Phys Chem B 2014). 

We are now applying these live cell imaging tools to the study of  stem cell pluripotency and differentiation (Bhadriraju Stem Cell Research 2016). Induced pluripotent stem cell technologies are a powerful new tool for biomedical research and have the potential to revolutionize medicine.

 

 

We use optical imaging to provide ‘high content’ information about the size, shape, edge character and internal structure of the cells and cell colonies under study in our laboratory. Here are shown images of H9 pluripotent stem cells in Zernike phase contrast (left) and fluorescence (right). The fluorescence is generated by a fluorescence protein reporter protein produced by a cell line that has been genetically engineered. The expression level of the fluorescent reporter provides information about the activity of a biochemical pathway. Our goal is to identify critical attributes to measure during iPS processing schedules that can facilitate reaching the desired cellular product. Imaging is essential because cells are dynamic, heterogeneous, and spatial context is important.  Optical microscopy is the only technique that allows us to directly measure dynamic, spatially resolved information at the single cell level.

Live cell imaging
Cells are complex and spatial context is important. Images provide ‘high content’ information that can be used to identify critical attributes to measure during iPS processing schedules. It is not possible to get this type of information from flow cytometry, microarray, or sequencing data.

NRC Postdoctoral Opportunities in these research areas can be found here.

Created September 26, 2016, Updated December 21, 2018