Stem Cell Analysis
Stem Cell Analysis - Related Videos
13:11
Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth
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Cited by 17 •
2010
Video bioinformatics is the automated processing, analysis, understanding, and data mining of biological spatio-temporal data extracted from microscopic videos. The purpose of this article is to demonstrate a method for measuring human embryonic stem cell colony growth using a video bioinformatics method.
22:06
Derivation of Hematopoietic Stem Cells from Murine Embryonic Stem Cells
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Cited by 8 •
2007
This protocol details the derivation of transplantable hematopoietic stem cells from mouse embryonic stem cells (ESC) and their subsequent injection into lethally irradiated recipient mice. Briefly, ESC are differentiated as embryoid bodies, which are then infected with retroviral HoxB4 and co-cultured with OP9 stromal cells and hematopoietic cytokines.
07:36
Intravital Microscopy of the Microcirculation in the Mouse Cremaster Muscle for the Analysis of Peripheral Stem Cell Migration
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Cited by 4 •
2013
Intravital microscopy of the mouse M. cremaster microcirculation offers a unique and well-standardized in vivo model for the analysis of peripheral bone marrow stem cell migration.
14:42
Isolation and Analysis of Hematopoietic Stem Cells from the Placenta
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Cited by 11 •
2008
We have identified the placenta as a major hematopoietic organ during development. We found that hematopoietic stem cells (HSCs) are both generated and expanded in the placenta in unique microenvironmental niches. Here, we describe experimental techniques required for isolation and visualization of HSCs in the mouse placenta.
11:13
Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes (hPSC-CMs) Using Multi-electrode Arrays (MEAs)
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Cited by 44 •
2017
Electrophysiological characterization of cardiomyocytes derived from human Pluripotent Stem Cells (hPSC-CMs) is crucial for cardiac disease modeling and for determining drug responses. This protocol provides the necessary information to dissociate and plate hPSC-CMs on multi-electrode arrays, measure their field potential, and a method for analyzing QT and RR intervals.
FAQs
Researchers compare several types of evidence rather than relying on a single measurement. Cell-surface marker detection indicates which characteristic populations are present, while microscopy shows cellular features and gene-expression measurements reveal molecular activity. Together, these readouts help distinguish stem cell populations and assess whether their observed state matches the needs of a biological experiment.
Functional differentiation assays test what cells can do, not only which markers they display. Researchers evaluate whether a population can produce specialized cell types, providing evidence about differentiation capacity and cell fate. This functional information complements marker, imaging, and gene-expression data, making conclusions about stem cell properties more informative than measurements based on identity alone.
The analysis distinguishes these categories by comparing their measured characteristics and functional behavior. Marker profiles, microscopy, gene-expression patterns, and differentiation results provide evidence that helps separate pluripotent, adult, and induced stem cell populations. This comparison is important because the categories can differ in biological state and potential, even when researchers examine them within related experimental systems.
Changes in gene-expression measurements and differentiation outcomes can show how biological signals influence cell fate. If a signal is associated with a different specialized-cell outcome, the combined molecular and functional data help connect that signal with changes in developmental state. This information supports studies of development and explains how stem cells acquire distinct identities.
A typical workflow begins by characterizing the sample with cell-surface markers, microscopy, or gene-expression measurements. Researchers then use functional differentiation assays to evaluate developmental potential and may quantify populations with flow cytometry or immunostaining. Interpreting these results together provides a broader assessment of identity, state, self-renewal capacity, and differentiation behavior.
Both approaches can help quantify cell populations, but they contribute through different forms of measurement. Flow cytometry provides population-level analysis of detected characteristics, whereas immunostaining combines detection with microscopy-based visualization. Using either method, alongside gene-expression and functional data, helps researchers evaluate whether the sample contains the populations expected for the experiment.
Stem Cell Analysis supports research on development, disease modeling, tissue engineering, and regenerative medicine. In these settings, researchers use characterization and functional results to examine cell states, differentiation behavior, and responses relevant to specialized tissues or disease-related models. The resulting evidence helps connect stem cell properties with the biological question being investigated.
Systematic characterization provides evidence that experimental stem cell populations have the expected identity, state, and functional behavior. Researchers can compare marker measurements, imaging, gene-expression results, and differentiation outcomes across experiments. This supports evaluation of sample quality and reproducibility, while also contributing information relevant to safety when stem cells are studied for tissue engineering or regenerative medicine.