title: Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling.
abstract: Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP) that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells) were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples) for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the development of computer-based systems for automatically parsing relevant histological and cellular features from molecular imaging data of arbitrary human tissue samples, and can provide a framework and resource to spur the optimization of these technologies.
authors.affiliation: Department of Biochemistry, Stanford University School of Medicine, Stanford, California, United States of America; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, United States of America.
authors.forename: Daniel P
authors.initials: DP
authors.lastname: Riordan
authors.affiliation: Department of Pathology, Stanford University School of Medicine, Stanford, California, United States of America.
authors.forename: Sushama
authors.initials: S
authors.lastname: Varma
authors.affiliation: Department of Pathology, Stanford University School of Medicine, Stanford, California, United States of America.
authors.forename: Robert B
authors.initials: RB
authors.lastname: West
authors.affiliation: Department of Biochemistry, Stanford University School of Medicine, Stanford, California, United States of America; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, United States of America.
authors.forename: Patrick O
authors.initials: PO
authors.lastname: Brown
mesh_terms: D000465:Algorithms; D001331:Automation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D057054:Molecular Imaging / Q000379:methods*; D012984:Software; D046888:Tissue Array Analysis; D000069558:Unsupervised Machine Learning