Immune Cell Subsets
Immune Cell Subsets - Related Videos
04:57
A Flow Cytometry Assay to Identify Immune Cell Subsets in Peripheral Blood Mononuclear Cells
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2025
This video demonstrates a flow cytometry-based technique using two fluorochromes to identify CD4+ and CD8+ T cells, γδ T cells, B cells, natural killer (NK) cells, and monocytes in human peripheral blood mononuclear cells (PBMCs). Upon incubating the cells with fluorochrome-conjugated antibodies against the surface markers, the cell subsets are identified using an optimal flow cytometric gating strategy.
10:58
Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry
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2019
Here, we present a flow cytometric protocol to identify CD4+ and CD8+ T cells, γδ T cells, B cells, NK cells and monocytes in human peripheral blood by using only two fluorochromes instead of seven. With this approach, five additional markers can be recorded on most flow cytometers.
14:06
Isolation of Precursor B-cell Subsets from Umbilical Cord Blood
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Cited by 2 •
2013
Here we describe a protocol for isolating subsets of precursor B-cells from umbilical cord blood. A sufficient quantity and quality of nucleic acids may be extracted from the cells and used in subsequent assays utilizing DNA or RNA.
05:07
Isolation, Characterization and Functional Examination of the Gingival Immune Cell Network
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Cited by 30 •
2016
We have established a technique for the isolation, phenotypic characterization and functional analysis of immune cells from murine gingiva.
07:12
Mouse Naïve CD4+ T Cell Isolation and In vitro Differentiation into T Cell Subsets
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Cited by 71 •
2015
Naïve CD4+ T cells polarize to various subsets depending on the environment at the time of activation. The differentiation of naïve CD4+ T cells to various effector subsets can be achieved in vitro through the addition of T cell receptor stimuli and specific cytokine signals.
FAQs
These signals help determine whether cells activate, differentiate, or interact with other immune populations. Cytokines provide regulatory cues, while antigen-receptor signaling supplies response-related signals. Examining these influences helps explain why related populations can display different functional states and how immune responses are coordinated during pathogen defense, tolerance, or inflammation.
Marker patterns provide a way to distinguish populations that may share developmental origins but differ in surface characteristics and roles. Using CD4, CD8, CD19, or CD56 in combination supports comparisons among T-cell, B-cell, and natural killer cell populations. The resulting distinctions help connect cellular identity with signaling profiles and specialized immune functions.
Different subsets contribute through distinct specialized functions and interactions rather than through identical responses. T cells, B cells, natural killer cells, and myeloid cells can therefore be examined as related but functionally differentiated parts of one system. Comparing their composition and signaling helps researchers interpret pathogen defense, immune tolerance, and inflammation as coordinated processes.
Researchers first select markers relevant to the populations under study, such as CD4, CD8, CD19, or CD56. Flow cytometry then supports identification and comparison of cells according to these surface features. Investigators can relate the resulting subset pattern to activation, differentiation, signaling, or cellular interactions, depending on the biological question.
Changes in composition can signal altered immune organization in settings such as infection, autoimmunity, cancer, or vaccination. Researchers compare which populations are present and how their signaling or function differs, rather than treating total immune-cell abundance as the only outcome. These comparisons can reveal disease mechanisms or indicate responses to treatment.
In cancer and immunotherapy research, investigators can track subset composition alongside signaling profiles and specialized functions. Such measurements help assess how immune populations relate to disease biology and whether treatment is associated with altered cellular patterns or responses. The approach connects measurable differences among T, B, natural killer, and myeloid cells with therapeutic questions.