Astrocyte Markers
Astrocyte Markers - Related Videos
06:41
Astrocyte Isolation: A Method to Obtain Pure Preparation of Mouse Cortical Astrocytes
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2023
In this video, we describe the method to obtain a pure preparation of astrocytes from newly born mice.
11:25
Isolation and Culture of Mouse Cortical Astrocytes
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Cited by 460 •
2013
Astrocytes have been recognized to be versatile cells participating in fundamental biological processes that are essential for normal brain development and function, and central nervous system repair. Here we present a rapid procedure to obtain pure mouse astrocyte cultures to study the biology of this major class of central nervous system cells.
02:18
Differentiating Induced Neural Progenitor Cells into Astrocytes
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2025
This video demonstrates the process for differentiating induced neural progenitor cells (iNPCs) into neurons and astrocytes using specialized media.
08:32
Sex Differences in Mouse Hippocampal Astrocytes after In-Vitro Ischemia
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Cited by 9 •
2016
Astrocytes are one of the most important key players in the central nervous system (CNS). Here, we are reporting a practical method of sexed hippocampal astrocyte culture protocol in order to study the mechanisms underlying the astrocyte function in male and female neonate pups after in-vitro ischemia.
15:08
Monitoring Astrocyte Reactivity and Proliferation in Vitro Under Ischemic-Like Conditions
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Cited by 13 •
2017
Ischemic stroke is a complex event in which the specific contribution of astrocytes to the affected brain region exposed to oxygen glucose deprivation (OGD) is difficult to study. This article introduces a methodology to obtain isolated astrocytes and study their reactivity and proliferation under OGD conditions.
FAQs
GFAP alone may not reliably capture every astrocyte population or state. Astrocyte-marker expression varies across brain regions, developmental stages, and reactive conditions, so GFAP staining can produce an incomplete or context-dependent picture. Pairing it with S100β, ALDH1L1, AQP4, or GLT-1 helps researchers evaluate astrocyte identity across differing biological contexts.
Using several astrocyte markers improves classification because no single signal necessarily represents astrocytes identically across all experimental contexts. A panel can combine GFAP, S100β, ALDH1L1, AQP4, and GLT-1, allowing researchers to compare overlapping evidence rather than relying on one feature. This approach is useful when mapping cells or interpreting changes linked to injury, inflammation, or disease.
Immunostaining detects marker-associated proteins, whereas gene-expression analysis examines marker expression at the gene level. Fluorescent labeling provides another way to visualize labeled cellular features. These approaches can supply complementary evidence by connecting molecular measurements with cellular distribution, strengthening interpretation of astrocyte identity and changes in cellular state.
A study can begin by selecting one or more markers suited to its biological question, then detecting them through immunostaining, gene-expression analysis, or fluorescent labeling. Researchers can compare marker distribution or expression across brain regions, developmental stages, or experimental conditions. The resulting patterns support cell classification and assessment of changes after injury, inflammation, or disease.
Beyond distinguishing astrocytes from other neural cells, these markers help map astrocyte distribution and examine cellular patterns within neural tissue. They support investigations of neurodevelopment and neural circuits, while also providing information relevant to gliopathology. Their value comes from connecting localization or expression patterns with the biological context under study.
Researchers can compare astrocyte-marker distribution or expression across conditions to assess disease- or injury-associated changes. Because reactive state can alter marker expression, a changed signal requires biological context and should not automatically be interpreted as a change in astrocyte number. Using complementary markers helps distinguish broader classification patterns from state-dependent variation.