Portable cerebral blood flow monitor to detect large vessel occlusion in patients with suspected stroke.

BACKGROUND: Early detection of large vessel occlusion (LVO) facilitates triage to an appropriate stroke center to reduce treatment times and improve outcomes. Prehospital stroke scales are not sufficiently sensitive, so we investigated the ability of the portable Openwater optical blood flow monitor to detect LVO. METHODS: Patients were prospectively enrolled at two comprehensive stroke centers during stroke alert evaluation within 24 hours of onset with National Institutes of Health Stroke Scale (NIHSS) score ≥2. A 70 s bedside optical blood flow scan generated cerebral blood flow waveforms based on relative changes in speckle contrast. Anterior circulation LVO was determined by CT angiography. A deep learning model trained on all patient data using fivefold cross-validation and learned discriminative representations from the raw speckle contrast waveform data. Receiver operating characteristic (ROC) analysis compared the Openwater diagnostic performance (ie, LVO detection) with prehospital stroke scales. RESULTS: Among 135 patients, 52 (39%) had an anterior circulation LVO. The median NIHSS score was 8 (IQR 4-14). The Openwater instrument had 79% sensitivity and 84% specificity for the detection of LVO. The rapid arterial occlusion evaluation (RACE) scale had 60% sensitivity and 81% specificity and the Los Angeles motor scale (LAMS) had 50% sensitivity and 81% specificity. The binary Openwater classification (high-likelihood vs low-likelihood) had an area under the ROC (AUROC) of 0.82 (95% CI 0.75 to 0.88), which outperformed RACE (AUC 0.70; 95% CI 0.62 to 0.78; P=0.04) and LAMS (AUC 0.65; 95% CI 0.57 to 0.73; P=0.002). CONCLUSIONS: The Openwater optical blood flow monitor outperformed prehospital stroke scales for the detection of LVO in patients undergoing acute stroke evaluation in the emergency department. These encouraging findings need to be validated in an independent test set and the prehospital environment.

Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks.

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.

Preexisting epithelial diversity in normal human livers: a tissue-tethered cytometric analysis in portal/periportal epithelial cells.

UNLABELLED: Routine light microscopy identifies two distinct epithelial cell populations in normal human livers: hepatocytes and biliary epithelial cells (BECs). 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 preexisting 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 preexist 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., scatterplots) while still tethered to the WSI and tissue structure. We employed biomarkers specifically associated with mature epithelial forms (HNF4α for hepatocytes, CK19 and HNF1ß for BEC) and explored for the presence of cells with hybrid biomarker phenotypes. The results showed abundant hybrid cells in portal bile duct BEC, canals of Hering, and immediate periportal hepatocytes. These bipotential 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 preexistent hybrid epithelial diversity in normal human liver. This computationally enabled tissue analysis approach offers much broader potential beyond the results presented here.

Cell-based quantification of molecular biomarkers in histopathology specimens.

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 and 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, estrogen 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 their 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.

Adding value to liver (and allograft) biopsy evaluation using a combination of multiplex quantum dot immunostaining, high-resolution whole-slide digital imaging, and automated image analysis.

Various technologies including nucleic acid, protein, and metabolic array analyses of blood, liver tissue, and bile are emerging as powerful tools in the study of hepatic pathophysiology. The entire lexicon of liver disease, however, has been written using classical hematoxylin-eosin staining and light microscopic examination. The authors' goal is to develop new tools to enhance histopathologic examination of liver tissue that would enrich the information gained from liver biopsy analysis, enable quantitative analysis, and bridge the gap between various "-omics" tools and interpretation of routine liver biopsy results. This article describes the progress achieved during the past 2 years in developing multiplex quantum dot (nanoparticle) staining and combining it with high-resolution whole-slide imaging using a slide scanner equipped with filters to capture 9 distinct fluorescent signals for multiple antigens. The authors first focused on precise characterization of leukocyte subsets, but soon realized that the data generated were beyond the practical limits that could be properly evaluated, analyzed, and interpreted visually by a pathologist. Therefore, the authors collaborated with the open source FARSIGHT image analysis project (http://www.farsight-toolkit.org). FARSIGHT's goal is to develop and disseminate the next-generation toolkit of automated image analysis methods to enable quantification of molecular biomarkers on a cell-by-cell basis from multiparameter images. The resulting data can be used for histocytometric studies of the complex and dynamic tissue microenvironments that are of biomedical interest. The authors envisage that these tools will eventually be incorporated into the routine practice of surgical pathology and precipitate a revolution in the specialty.