Facs Analysis Results
Facs Analysis Results - Related Videos
06:04
Murine Dermal Fibroblast Isolation by FACS
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Cited by 28 •
2016
Fibroblast behavior underlies a spectrum of clinical entities, but they remain poorly characterized, largely due to their inherent heterogeneity. Traditional fibroblast research relies upon in vitro manipulation, masking in vivo fibroblast behavior. We describe a FACS-based protocol for the isolation of mouse skin fibroblasts that does not require cell culture.
17:07
Flow Cytometry and Fluorescence-Activated Cell Sorting (FACS): Isolation of Splenic B Lymphocytes
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2023
Source: Perchet Thibaut1,2,3, Meunier Sylvain1,2,3, Sophie Novault4, Rachel Golub1,2,3 1 Unit for Lymphopoiesis, Department of Immunology, Pasteur Institute, Paris, France 2 INSERM U1223, Paris, France 3 Université Paris Diderot, Sorbonne Paris Cité, Cellule Pasteur, Paris, France 4 Flow Cytometry Platfrom, Cytometry and Biomarkers UtechS, Center for Translational Science, Pasteur Institute, Paris, France The overall function of the immune system is to defend the body against infectious...
10:10
An Efficient Protocol for CUT&RUN Analysis of FACS-Isolated Mouse Satellite Cells
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Cited by 1 •
2023
Presented here is an efficient protocol for the fluorescence-activated cell sorting (FACS) isolation of mouse limb muscle satellite cells adapted to the study of transcription regulation in muscle fibers by cleavage under targets and release using nuclease (CUT&RUN).
08:37
Fluorescence Activated Cell Sorting (FACS) and Gene Expression Analysis of Fos-expressing Neurons from Fresh and Frozen Rat Brain Tissue
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Cited by 34 •
2016
Here we present a Fluorescence Activated Cell Sorting (FACS) protocol to study molecular alterations in Fos-expressing neuronal ensembles from both fresh and frozen brain tissue. The use of frozen tissue allows FACS isolation of many brain areas over multiple sessions to maximize the use of valuable animal subjects.
11:04
Adoptive Cell Transfer: Introducing Donor Mouse Splenocytes to a Host Mouse and Assessing Success via FACS
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2023
Source: Meunier Sylvain1,2,3, Perchet Thibaut1,2,3, Sophie Novault4, Rachel Golub1,2,3 1 Unit for Lymphopoiesis, Department of Immunology, Pasteur Institute, Paris, France 2 INSERM U1223, Paris, France 3 Université Paris Diderot, Sorbonne Paris Cité, Cellule Pasteur, Paris, France 4 Flow Cytometry Platfrom, Cytometry and Biomarkers UtechS, Center for Translational Science, Pasteur Institute, Paris, France Adoptive cell transfer is a method for introducing cells into a patient or study organism...
FAQs
Two signal types provide complementary evidence. Light scatter reports physical properties of cells, whereas fluorescence indicates signals from fluorophore-labeled markers. Considering both measurements helps distinguish cell populations that may look similar in one measurement alone. This combination supports quantitative assessment of cellular composition and phenotype within samples.
Gating defines which regions or subsets of the measured data represent the cell populations of interest. Researchers can then evaluate marker expression within those selected groups rather than treating all recorded events as one population. Because the chosen gates directly affect population identification and comparisons, careful gating is central to reliable interpretation.
Plots help examine relationships between measured physical and fluorescent properties, while histograms show how a selected signal is distributed across cells or populations. Together, these displays help reveal distinct groups, compare marker expression, and evaluate whether an apparent difference reflects a population-level pattern rather than an isolated measurement.
Controls provide a reference for judging whether detected fluorescence or population differences represent biology rather than background fluorescence or instrument-related variation. They strengthen interpretation of marker signals and comparisons between samples. Without appropriate controls, researchers may misclassify populations or attribute technical effects to experimental conditions.
In biology, these results can describe cellular composition, characterize phenotypes, assess viability, and evaluate responses to experimental conditions. The same dataset may therefore support both population-level and marker-level comparisons. Their usefulness depends on linking the measured scatter and fluorescence patterns to the biological populations and conditions being investigated.
A typical analysis examines the recorded scatter and fluorescence measurements, applies gates to identify relevant cell populations, and uses plots or histograms to compare marker expression or population features. Researchers then assess differences between conditions while checking controls and potential technical variation, helping separate genuine biological responses from measurement-related changes.