High-Quality Immunohistochemical Stains through Computational Assay Parameter Optimization
Immunohistochemistry has been an invaluable analytical method in the field of cancer diagnosis. Optimization of assay parameters governing the quality of immunostaining requires of exhaustive exploration of the parameter space, but such optimization is infeasible due to the limited availability of tissue samples. Thus, suboptimal images are being used for diagnoses. This work analyzes immunohistochemistry staining quality through staining quality indicators and proposes an innovative local staining method using the microfluidic probe technology. Consequently, the tissue is processed with parameters that result in improved signal-to-background stains. This methodology will contribute to standardize immunostaining across diagnostic laboratories and to reduce errors in diagnosis.