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Alkaline phosphatase and peroxidase for double immunoenzymatic labelling of cellular constituents.
Open accessAbstract
The use of alkaline phosphatase in an immunoenzymatic procedure for the localisation of antigens in paraffin sections or cell smears is described. The results of this method, when applied to the detection of immunoglobulins, lysozyme, or lactoferrin, were comparable in intensity and clarity to those obtained with the PAP immunoperoxidase procedure. Furthermore, double immunoenzymatic labelling (with alkaline phosphatase and peroxidase) of two cellular constituents in a tissue section is possible, the brown peroxidase reaction product contrasting well with the blue alkaline phosphatase product. Since the two antibody 'sandwiches' are applied simultaneously rather than sequentially the total duration of this double immunostaining procedure is only a few minutes longer than that required for detection of a single antigen. It was also found that the unlabelled antibody immunohistological procedure (whether used in conjunction with alkaline phosphatase or peroxidase) can be shortened without loss of sensitivity by carrying out two of the incubation steps simultaneously.
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Vandesande, F., & Dierickx, K. (1975) · Cell and Tissue Research · doi:10.1007/bf00218970 · Cited by 496
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Metadata
- Authors
- Mason, D. Y., & Sammons, R.
- Venue
- Journal of Clinical Pathology
- Publisher
- BMJ
- Published
- 1978-05
- Volume
- 31
- Issue
- 5
- Pages
- 454-460
- ISSN
- 0021-9746 (ISSN-L)
- 1472-4146
- OpenAlex
- W1989596114
- Cited by
- 405
- References
- 1
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Mason, D. Y., & Sammons, R. (1978). Alkaline phosphatase and peroxidase for double immunoenzymatic labelling of cellular constituents. Journal of Clinical Pathology. https://doi.org/10.1136/jcp.31.5.454
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
