In recent years, technological advances in pathology and tissue diagnostics have occurred at unprecedented rates. Novel methods for fluorescence-based as well as non-optical multiplexed immunohistochemistry, in situ hybridization, microscopy, and machine learning can revolutionize tissue-based diagnosis and will be central to the realization of personalized medicine. For instance, new multiplexed immunohistochemistry methods (e.g. imaging mass cytometry, multiplex ion beam imaging, and Nanostring Digital Spatial Profiling) promise four logs of dynamic range compared to two from traditional immunohistochemistry as well as the ability to spatially characterize over 50 biomarkers simultaneously. Advances in microscopy have also enabled non-destructive, slide-free examination of tissue samples with three dimensional image construction preserving tissue for downstream assays. They potentiate emerging tools for comprehensive, computerized analysis of digital histology, which could markedly improve prognostic or diagnostic methods that previously relied on subjective interpretation of traditional, two dimensional transmitted light histology.

While promising, these technologies require significant technical experience and knowledge to implement. Furthermore, their evolution has outpaced the ability to generate consensus guidelines on their validation and use. As such, increased visibility of these methods, shared by experienced users, can help in advancing dialogue around their implementation, accelerating standardization, ultimately to improve patient care. This collection will highlight methods of “high dimensional” pathology that could be transformative for histologic interpretation, biomarker science, and image analysis and includes methods for optical and non-optical multiplexed immunohistochemistry, three dimensional microscopy, generation of standards for immunohistochemistry platform validation, and machine learning-based image analysis.