LoQANT: An ImageJ Plugin for Quantifying Nuclear Staining in Immunohistochemistry and Immunofluorescence.
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| Názov: | LoQANT: An ImageJ Plugin for Quantifying Nuclear Staining in Immunohistochemistry and Immunofluorescence. |
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| Autori: | Cizkova K; Department of Histology and Embryology, Faculty of Medicine and Dentistry, Palacky University, 77900 Olomouc, Czech Republic. |
| Zdroj: | International journal of molecular sciences [Int J Mol Sci] 2025 Nov 06; Vol. 26 (21). Date of Electronic Publication: 2025 Nov 06. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067 (Electronic) Linking ISSN: 14220067 NLM ISO Abbreviation: Int J Mol Sci Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI, [2000- |
| Výrazy zo slovníka MeSH: | Cell Nucleus*/metabolism , Immunohistochemistry*/methods , Fluorescent Antibody Technique*/methods , Software* , Image Processing, Computer-Assisted*/methods , Staining and Labeling*/methods, Humans |
| Abstrakt: | A large number of regulatory proteins are found in both the cytoplasm and the nucleus. Changes in their nuclear abundance are important for cellular signalling, biological activity, and disease mechanisms. Accurate quantification of nuclear staining is therefore essential in studies of cellular function, therapeutic targeting, drug design, and drug resistance. However, manual scoring is time-consuming, unsuitable for high-throughput applications, and introduces potential bias. As expected, manual scoring by six observers with varying levels of expertise led to highly variable results. Moreover, it was far from achieving good interobserver reliability. To overcome these limitations, LoQANT (Localisation and Quantification of Antigen Nuclear sTaining), an open, freely available ImageJ plugin, was developed for reliable and efficient quantification of nuclear signals. LoQANT is a single cell-based approach to assess the proportion of cells with a positive nuclear signal, independent of cytoplasmic staining, in both immunohistochemically and fluorescently stained samples across various sample types. It also provides semiquantitative and quantitative measurements of nuclear staining intensity. The script, its version for batch analysis, and complete user guide are available at GitHub. |
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| Contributed Indexing: | Keywords: immunofluorescence; immunohistochemistry; nuclear positivity; staining quantification |
| Entry Date(s): | Date Created: 20251113 Date Completed: 20251113 Latest Revision: 20251116 |
| Update Code: | 20251116 |
| PubMed Central ID: | PMC12610205 |
| DOI: | 10.3390/ijms262110799 |
| PMID: | 41226833 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | A large number of regulatory proteins are found in both the cytoplasm and the nucleus. Changes in their nuclear abundance are important for cellular signalling, biological activity, and disease mechanisms. Accurate quantification of nuclear staining is therefore essential in studies of cellular function, therapeutic targeting, drug design, and drug resistance. However, manual scoring is time-consuming, unsuitable for high-throughput applications, and introduces potential bias. As expected, manual scoring by six observers with varying levels of expertise led to highly variable results. Moreover, it was far from achieving good interobserver reliability. To overcome these limitations, LoQANT (Localisation and Quantification of Antigen Nuclear sTaining), an open, freely available ImageJ plugin, was developed for reliable and efficient quantification of nuclear signals. LoQANT is a single cell-based approach to assess the proportion of cells with a positive nuclear signal, independent of cytoplasmic staining, in both immunohistochemically and fluorescently stained samples across various sample types. It also provides semiquantitative and quantitative measurements of nuclear staining intensity. The script, its version for batch analysis, and complete user guide are available at GitHub. |
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| ISSN: | 1422-0067 |
| DOI: | 10.3390/ijms262110799 |