Antibody-based Quantification
Antibody-based Quantification - Related Videos
05:55
Quantification of Autoreactive Antibodies in Mice upon Experimental Autoimmune Encephalomyelitis
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Cited by 3 •
2023
Experimental autoimmune encephalomyelitis (EAE) is an animal model of multiple sclerosis (MS), which shares with the human disease a robust humoral autoimmune response. Here, we report a simple and flexible ELISA protocol to quantify autoantibodies in the serum of EAE immunized mice.
12:55
Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries
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Cited by 33 •
2015
A method is described with visual accompaniment for conducting scalable, high throughput selections from phage-displayed combinatorial synthetic antibody libraries against hundreds of antigens simultaneously. Using this parallel approach, we have isolated antibody fragments that exhibit high affinity and specificity for diverse antigens that are functional in standard immunoassays.
07:53
Quantification of Antibody-dependent Enhancement of the Zika Virus in Primary Human Cells
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Cited by 1 •
2019
We describe a method to evaluate the effect of pre-existing immunity against dengue virus on the Zika virus infection by using human serum, primary human cells, and infection quantification by quantitative real-time polymerase chain reaction.
08:26
The Isolation, Differentiation, and Quantification of Human Antibody-secreting B Cells from Blood: ELISpot as a Functional Readout of Humoral Immunity
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Cited by 10 •
2016
Human peripheral blood is commonly used for the assessment of the humoral immune response. Here, the methods for isolating human B cells from peripheral blood, differentiating human B cells into antibody (Ab)-secreting B cells (ASCs) in culture, and enumerating the total IgM- and IgG-ASCs via an ELISpot assay are described.
13:21
Antibody Generation: Producing Monoclonal Antibodies Using Hybridomas
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2023
Source: Frances V. Sjaastad1,2, Whitney Swanson2,3, and Thomas S. Griffith1,2,3,4 1 Microbiology, Immunology, and Cancer Biology Graduate Program, University of Minnesota, Minneapolis, MN 55455 2 Center for Immunology, University of Minnesota, Minneapolis, MN 55455 3 Department of Urology, University of Minnesota, Minneapolis, MN 55455 4 Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455 Polyclonal antibodies are defined as a collection of antibodies directed against...
FAQs
Selectivity comes from the antibody’s ability to recognize the intended molecule within a sample. This narrows the measurement toward the target rather than treating all sample constituents as equivalent. In biochemical analysis, this focused recognition can support measurement of proteins, hormones, drugs, or other antigens, including when direct measurement is difficult.
Known-concentration standards provide the reference needed to interpret an assay signal quantitatively. The unknown sample’s response is compared with these reference values, allowing its target abundance to be determined rather than reported only as detectable or undetectable. This calibration step also supports comparisons among samples analyzed with the same measurement approach.
An enzyme, fluorescent label, or other detectable marker converts the antibody-target interaction into a measurable signal. The choice described in the assay determines how the binding event becomes observable, but quantitative interpretation still depends on comparing that signal with known standards. Thus, labeling supplies detectability, whereas antibody recognition supplies target selectivity.
Specificity and sensitivity contribute different strengths to an antibody-based measurement. Specificity helps focus the assay on the intended antigen, while sensitivity supports detection when direct measurement is difficult. Their combination is especially useful in complex biochemical samples, where the target must be distinguished and compared across preparations.
A basic workflow begins with a sample containing the molecule of interest and an antibody selected for recognition. The target binds the antibody, a detectable marker generates the assay signal, and standards of known concentration provide the comparison scale. The measured sample response is then interpreted against those standards to estimate target abundance.
Applications span protein characterization, diagnostics, pharmacology, and cell biology. In biochemistry, the same measurement logic can be applied to proteins, hormones, drugs, and other antigens, depending on the antibody and assay design. Results can reveal target abundance or enable comparisons between samples, making the approach useful across research and applied settings.