Tissue microarray (TMA) use in post mortem neuropathology

Background: Tissue microarrays (TMAs), where each block (and thus section) contains multiple tissue cores from multiple blocks potentially allow more efficient use of tissue, reagents and time in neuropathology. New method: The relationship between data from TMA cores and whole sections was investigated using 'virtual' TMA cores. This involved quantitative assessments of microglial pathology in white matter lesions and motor neuron disease, alongside qualitative TDP-43 inclusion status in motor neuron disease cases. Following this, a protocol was developed for TMA construction. Results: For microglial pathology we found good concordance between virtual cores and whole sections for volume density using one 1.75 mm core (equivalent to a 2 mm core after accounting for peripheral tissue loss). More sophisticated microglial cell size and measures required two cores. Qualitative results of pTDP-43 pathology showed use of one 1.75 mm core gave a 100 % sensitivity and specificity within grey matter, and 88.3 % sensitivity and 100 % specificity within white matter. A method of producing the TMAs was suitable for immunohistochemistry both manually and by autostainer, with the minimal core loss from the microscope slide. Comparison with existing methods: TMAs have been used infrequently in post mortem neuropathology research. However, we believe TMAs give comparable tissue assessment results and can be constructed, sectioned and stained with relative ease. Conclusions: We found TMAs could be used to assess both quantitative (microglial pathology) and qualitative pathology (TDP-43 proteinopathy) with greatly reduced quantities of tissue, time and reagents. These could be used for further work to improve data acquisition efficiency.

Automated summary

The study explored the potential benefits of using TMAs in neuropathology, demonstrating the effectiveness for evaluating microglial pathology and TDP-43 proteinopathy with reduced tissue, time, and reagent usage. The results indicate that TMAs could be a valuable tool for improving data acquisition efficiency in neuropathological research.