Novel approach for quantification of multiple immunofluorescent signals using histograms and 2D plot profiling of whole-section panoramic images
Abstract We describe a novel approach for quantification and colocalization of immunofluorescence (IF) signals of multiple markers on high-resolution panoramic images of serial histological sections utilizing standard staining techniques and readily available software for image processing and analys...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/c9cd6a6e8e1f471a89bf5dcbd1175b72 |
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| Sumario: | Abstract We describe a novel approach for quantification and colocalization of immunofluorescence (IF) signals of multiple markers on high-resolution panoramic images of serial histological sections utilizing standard staining techniques and readily available software for image processing and analysis. Human gingiva samples stained with primary antibodies against the common leukocyte antigen CD45 and factors related to heparan sulfate glycosaminoglycans (HS GAG) were used. Expression domains and spatial gradients of IF signals were quantified by histograms and 2D plot profiles, respectively. The importance of histomorphometric profiling of tissue samples and IF signal thresholding is elaborated. This approach to quantification of IF staining utilizes pixel (px) counts and comparison of px grey value (GV) or luminance. No cell counting is applied either to determine the cellular content of a given histological section nor the number of cells positive to the primary antibody of interest. There is no selection of multiple Regions-Of-Interest (ROIs) since the entire histological section is quantified. Although the standard IF staining protocol is applied, the data output enables colocalization of multiple markers (up to 30) from a given histological sample. This can serve as an alternative for colocalization of IF staining of multiple primary antibodies based on repeating cycles of staining of the same histological section since those techniques require non standard staining protocols and sophisticated equipment that can be out of reach for small laboratories in academic settings. Combined with the data from ontological bases, this approach to quantification of IF enables creation of in silico virtual disease models. |
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