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whereRU: Aresty Poster 100 - Detection of Variability in Mesenchymal Stem Cells by Aresty Posters 2009

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Aresty Posters 2009 Aresty Posters 2009
Explore score
1
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0.06 Gigapixels
Views
1610
Date added
May 29, 2009
Date taken
May 28, 2009
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Author: Simon Gordonov - Biomedical Engineering Department, Rutgers University, Piscataway, NJ

Abstract:

The recent study of human mesenchymal stem cell (hMSC) differentiation on polymeric biomaterials using various soluble cues has shown that quantification of early cytoskeletal and whole-cell morphology provides a valuable paradigm for predicting long term stem cell fates. We investigated the extent to which early cellular morphology is capable of predicting long term differentiation of hMSCs to either the osteoblastic (bone) or adipocytic (fat) cell phenotypes. We utilized cytoskeletal fluoro-reporters to image early cell morphology via confocal microscopy and quantified morphological parameters, or descriptors, of the stem cells using image processing software. The application of a data dimensionality reduction technique, Principal Component Analysis (PCA), on the descriptor data set, has enabled us to identify variability in cellular morphological parameters that may be indicative of heterogeneous stem cell priming to varying lineages. Consequently, PCA enabled us to tease out a subset of descriptors that contribute most to the variability in the morphological parameter data set, features that may be indicative of early stem cell commitment to becoming osteoblasts or adipocytes. Finally, we have shown that immunofluorescent staining for lineage-specific transcription factors is a valuable technique for simultaneously quantifying cytoskeletal properties and identifying early markers of cellular differentiation to particular lineages on a single-cell basis. This methodology can be used to evaluate the predictive value of morphological feature extraction on long term stem cell differentiation, which together can function as a tissue engineering toolbox that enables high throughput, quantitative screening and identification of heterogeneous stem cell populations.

Contact info
email: simong@eden.rutgers.edu


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