Conventional histopathology is the gold standard for allograft monitoring, but its value proposition is increasingly questioned. “-Omics” analysis of tissues, peripheral blood and fluids and targeted serologic studies provide mechanistic insights into allograft injury not currently provided by conventional histology. Microscopic biopsy analysis, however, provides valuable and unique information: a) spatial-temporal relationships; b) rare events/cells; c) complex structural context; and d) integration into a “systems” model. Nevertheless, except for immunostaining, no transformative advancements have “modernized” routine microscopy in over 100 years. Pathologists now team with hardware and software engineers to exploit remarkable developments in digital imaging, nanoparticle multiplex staining, and computational image analysis software to bridge the traditional histology - global “–omic” analyses gap. Included are side-by-side comparisons, objective biopsy finding quantification, multiplexing, automated image analysis, and electronic data and resource sharing. Current utilization for teaching, quality assurance, conferencing, consultations, research and clinical trials is evolving toward implementation for low-volume, high-complexity clinical services like transplantation pathology. Cost, complexities of implementation, fluid/evolving standards, and unsettled medical/legal and regulatory issues remain as challenges. Regardless, challenges will be overcome and these technologies will enable transplant pathologists to increase information extraction from tissue specimens and contribute to cross-platform biomarker discovery for improved outcomes.

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Routine light microscopy identifies two distinct epithelial cell populations in normal human livers: hepatocytes and biliary epithelial cells (BEC). Considerable epithelial diversity, however, arises during disease states when a variety of hepatocyte-BEC hybrid cells appear. This has been attributed to activation and differentiation of putative hepatic progenitor cells (HPC) residing in the Canals of Hering and/or metaplasia of pre-existing mature epithelial cells. A novel analytic approach consisting of multiplex labeling, high resolution whole slide imaging (WSI), and automated image analysis was used to determine if more complex epithelial cell phenotypes pre-existed in normal adult human livers, which might provide an alternative explanation for disease-induced epithelial diversity. “Virtually digested” WSI enabled quantitative cytometric analyses of individual cells displayed in a variety of formats (e.g. scatter plots) while still tethered to the WSI and tissue structure. We employed biomarkers specifically-associated with mature epithelial forms (HNF4α for hepatocytes, CK19 and HNF1β for BEC) and explored for the presence of cells with hybrid biomarker phenotypes. Results showed abundant hybrid cells in portal bile duct BEC, canals of Hering, and immediate periportal hepatocytes. These bi-potential cells likely serve as a reservoir for the epithelial diversity of ductular reactions, appearance of hepatocytes in bile ducts, and the rapid and fluid transition of BEC to hepatocytes, and vice versa. Conclusion Novel imaging and computational tools enable increased information extraction from tissue samples and quantify the considerable pre-existent hybrid epithelial diversity in normal human liver. This computationally-enabled tissue analysis approach offers much broader potential beyond the results presented here.

Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples. We present a direct method that generates readouts for a comprehensive panel of biomarkers from serial whole-brain slices, characterizing all major brain cell types, at scales ranging from subcellular compartments, individual cells, local multi-cellular niches, to whole-brain regions from each slice. We use iterative cycles of optimized 10-plex immunostaining with 10-color epifluorescence imaging to accumulate highly enriched image datasets from individual whole-brain slices, from which seamless signal-corrected mosaics are reconstructed. Specific fluorescent signals of interest are isolated computationally, rejecting autofluorescence, imaging noise, cross-channel bleed-through, and cross-labeling. Reliable large-scale cell detection and segmentation are achieved using deep neural networks. Cell phenotyping is performed by analyzing unique biomarker combinations over appropriate subcellular compartments. This approach can accelerate pre-clinical drug evaluation and system-level brain histology studies by simultaneously profiling multiple biological processes in their native anatomical context.

Aims To investigate the use of a computer-assisted technology for objective, cell-based quantification of molecular biomarkers in specified cell types in histopathology specimens, with the aim of advancing current visual estimation or pixel-level (rather than cell-based) quantification methods. Methods and results Tissue specimens were multiplex-immunostained to reveal cell structures, cell type markers, and analytes, and imaged with multispectral microscopy. The image data were processed with novel software that automatically delineates and types each cell in the field, measures morphological features, and quantifies analytes in different subcellular compartments of specified cells. The methodology was validated with the use of cell blocks composed of differentially labelled cultured cells mixed in known proportions, and evaluated on human breast carcinoma specimens for quantifying human epidermal growth factor receptor 2, oestrogen receptor, progesterone receptor, Ki67, phospho-extracellular signal-related kinase, and phospho-S6. Automated cell-level analyses closely matched human assessments, but, predictably, differed from pixel-level analyses of the same images. Conclusions Our method reveals the type, distribution, morphology and biomarker state of each cell in the field, and allows multiple biomarkers to be quantified over specified cell types, regardless of abundance. It is ideal for studying specimens from patients in clinical trials of targeted therapeutic agents, for investigating minority stromal cell subpopulations, and for phenotypic characterization to personalize therapy and prognosis.