A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections

Area of Science:

• Biomedical Engineering
• Computational Pathology
• Immunohistochemistry

Background:

• Accurate quantitation of fluorescent lymphocytes in tissue sections is crucial for understanding immune responses and disease.
• Manual cell counting by experts is often inconsistent and unreproducible, hindering high-throughput analysis.

Purpose of the Study:

• To develop and validate a neural cell detection system (NCDS) for automatic quantitation of fluorescent lymphocytes.
• To compare the performance of the NCDS with manual cell counting by biomedical experts.

Main Methods:

• The NCDS was trained on user-selected fluorescent lymphocyte image patches.
• System performance was validated using cross-validation on digitized tissue sections from human tonsil and skeletal muscle.
• NCDS detection results were compared against expert annotations.

Main Results:

• The NCDS achieved a minimum detection rate of 95% for fluorescent lymphocytes, even in noisy micrographs with variable cell morphology.
• Expert cell counts demonstrated significant inconsistency and poor reproducibility for approximately 80% of lymphocytes.
• The NCDS provided rapid (2 min per image) and highly reproducible quantitation of cell number, position, and phenotype.

Conclusions:

• The NCDS offers a rapid, accurate, and reproducible method for quantifying fluorescent lymphocytes in diverse tissue types.
• This automated system facilitates high-throughput topological screening, enabling quantitative analysis of tissue-specific cellular interactions.
• The NCDS provides a robust platform for advancing research in immunology, pathology, and diagnostics.