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Updated: Aug 8, 2026
Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
Published on: September 23, 2013
Validation of computer-assisted, pixel-based analysis of multiple-colour immunofluorescence histology
C F Inman1, L E N Rees, E Barker
1Division of Veterinary Pathology, Infection and Immunity, School of Clinical Veterinary Science, Langford House, Langford, Bristol BS40 5DU, UK. C.F.Inman@bristol.ac.uk
Insights
Automated image analysis of immunohistology is a reliable method for tissue assessment. This faster technique reduces observer variation and is essential for complex multi-fluorochrome staining.
Area of Science:
- Immunohistology
- Digital image analysis
- Cellular biology
Background:
- Simultaneous immunohistochemical staining with multiple monoclonal antibodies and fluorochromes enables identification of multiple cell populations.
- Accurate and reproducible quantification of these stained cells is crucial for reliable analysis.
- Computer-assisted analysis of digitized images is increasingly relied upon, necessitating validation against manual counting.
Purpose of the Study:
- To validate automated, pixel-based image analysis against manual counting for quantifying simultaneous immunohistochemical staining.
- To assess the reliability and speed of automated counting for tissue analysis.
- To explore the applicability of automated counting for complex staining protocols involving more than three fluorochromes.
Main Methods:
- Digitization of tissue sections (normal porcine skin, inflamed skin, tonsil) stained with three monoclonal antibodies.
- Quantification of staining combinations using both manual counting and automated pixel-based area measurement.
- Statistical comparison of manual and automated measurements, focusing on individual image correlation and tissue-level concordance.
Main Results:
- Individual image correlation between automated and manual measurements was poor.
- However, good concordance was observed in the means and variances of tissue measurements between the two methods.
- Both techniques identified similar biological changes in inflamed versus normal tissues, and pixel-based counting allowed for co-localization analysis.
Conclusions:
- Automated counting is an acceptable, faster, and less observer-variable method for tissue assessment compared to manual counting.
- The technique is particularly valuable for complex immunohistochemical staining (e.g., >3 fluorochromes) where manual counting is impractical.
- Automated analysis provides robust data for identifying tissue changes and analyzing cell co-localization.
Abstract:
Developments in immunohistology allow the routine simultaneous use on tissue sections of three monoclonal antibodies, tagged with different fluorochromes. Such staining can identify seven different cell populations and the limiting factor is rapid, reliable and reproducible analysis. Future reliance on computer-assisted analysis of digitised images depends on validation against manual counting, often viewed as the 'gold standard'. In this study images were digitised from sections of normal porcine skin, inflamed skin and tonsil, simultaneously stained with three monoclonal antibodies. Combinations of staining were quantified by four manual counts and by pixel-based area measurement. On individual images, the correlation between automated and manual measurements was poor. Despite this, the concordance between manual and automated measurements in the means and variances of tissues was good, and both techniques identified the same changes in inflamed versus normal tissues. In addition, pixel-based counting permitted statistical analysis of co-localisation of cell types in tissue sections. We conclude that automated counting is acceptable for the assessment of tissues, is faster and provides less opportunity for observer variation than manual counting. We also demonstrate that the technique is applicable where more than three fluorochromes are used such that manual counting becomes essentially impossible.
