Light-Scattering Techniques to Determine Stem Cell Fates
University of California System: University of California, Merced
posted on 06/09/2011
Determination of stem cell fates, including ascertaining the differentiation status and forecasting the outcome for a given stem cell or stem cell colony, is critical in regenerative medicine and tissue engineering. However, commonly employed procedures for making such determinations, such as immunofluorescence and flow cytometry, can involve time-consuming and costly sample preparation and often (especially for human stem cells) require the sacrifice of the cells during the assay process. It would be highly preferable to employ procedures that are faster, less intrusive, and less expensive.
The UCM system for assaying stem cell fates may help facilitate stem cell therapies that have been proposed for heart disease, spinal cord injuries, diabetes, and other significant health problems, and more generally should find widespread use in most stem cell protocols. In particular, this invention should improve quality control for any stem cell protocol that requires strict monitoring and control of cell growth and differentiation during cell incubation.
In contrast to existing stem cell fate assays, the UCM system:
- is non-intrusive;
- is low cost;
- requires only 3–4 minutes to analyze one plate, or 5–7 seconds per cell cluster/colony; and
- provides real-time measurement or forecasting of stem cell differentiation outcomes.
University of California, Merced (UCM) researchers have invented an improved assay of stem cell fates based on forward light scattering/diffraction images of the cells. This UCM system takes advantage of phenotype-dependent optical effects associated with cell morphology, dielectric parameters, and other cell properties affecting interactions with an incident laser light beam. Using appropriate methods for rapidly generating and capturing images, one can compare the results to a reference image database to quickly determine the degree of differentiation displayed by a cell. In experiments with human embryonic stem cell colonies with known differentiation statuses, the researchers achieved an accuracy of better than 90% with their system, and ultimately should be able to achieve better than 90% through the use of machine-learning methods for image classification.
File Number: 21760
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This innovation currently is not available for online licensing. Please contact David Cepoi at University of California System: University of California, Merced for more information.request more info
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