An Automated Method to Quantify Microglia Morphology and Application to Monitor Activation State Longitudinally In Vivo

Microglia are specialized immune cells of the brain. Upon insult, microglia initiate a cascade of cellular responses including a characteristic change in cell morphology. To study the dynamics of microglia immune response in situ, we developed an automated image analysis method that enables the quantitative assessment of microglia activation state within tissue based solely on cell morphology. Per cell morphometric analysis of fluorescently labeled microglia is achieved through local iterative threshold segmentation, which reduces errors caused by signal-to-noise variation across large volumes. We demonstrate, utilizing systemic application of lipopolysaccharide as a model of immune challenge, that several morphological parameters, including cell perimeter length, cell roundness and soma size, quantitatively distinguish resting versus activated populations of microglia within tissue comparable to traditional immunohistochemistry methods. Furthermore, we provide proof-of-concept data that monitoring soma size enables the longitudinal assessment of microglia activation in the mouse neocortex imaged via 2-photon in vivo microscopy. The ability to quantify microglia activation automatically by shape alone allows unbiased and rapid analysis of both fixed and in vivo central nervous system tissue

Longitudinal assessment of microglia activation <i>in vivo</i> by tracking changes in soma size.

<p>(<b>A</b>) Quantification of soma size, IBA-1 and CD68 expression relative to control conditions 24 or 48 hours post LPS injection. Asterisks indicate significance for each condition compared to control (0 mg/kg LPS, 24 h), horizontal line represents statistical comparisons between indicated time points, n = 5, 6, 6, 6 and 6 animals/group, respectively. (<b>B</b> and <b>C</b>) Top: representative images of microglia (green) and blood vessels (red) imaged <i>in vivo</i> at indicated time points. Bottom: Outlines of segmented microglia from the corresponding image above color-coded to indicate soma size per cell under control (<b>B</b>) and LPS (2 mg/kg) (<b>C</b>) conditions; red corresponds to microglia with soma size >65 µm², thus activated. (<b>D</b>) Quantification of soma size measured <i>in vivo</i> as a function of time and LPS dose. Data from one control animal, and one LPS stimulated animal, are shown. Scale bar equals 50 µm.</p

Correlation between morphological parameters and IBA-1 expression.

<p>Correlation plots between IBA-1 expression and CD68 expression or the various morphometric parameters, under control and 4 mg/kg LPS conditions (n = 7 and 5 animals/group, respectively). Cell populations from individual mice are plotted in different symbols. The numbers indicates the mean linear correlation coefficient (R²) value per comparison derived from linear fits to each animal dataset within a condition.</p

An entirely automated method to score DSS-induced colitis in mice by digital image analysis of pathology slides

SUMMARY The DSS (dextran sulfate sodium) model of colitis is a mouse model of inflammatory bowel disease. Microscopic symptoms include loss of crypt cells from the gut lining and infiltration of inflammatory cells into the colon. An experienced pathologist requires several hours per study to score histological changes in selected regions of the mouse gut. In order to increase the efficiency of scoring, Definiens Developer software was used to devise an entirely automated method to quantify histological changes in the whole H&E slide. When the algorithm was applied to slides from historical drug-discovery studies, automated scores classified 88% of drug candidates in the same way as pathologists’ scores. In addition, another automated image analysis method was developed to quantify colon-infiltrating macrophages, neutrophils, B cells and T cells in immunohistochemical stains of serial sections of the H&E slides. The timing of neutrophil and macrophage infiltration had the highest correlation to pathological changes, whereas T and B cell infiltration occurred later. Thus, automated image analysis enables quantitative comparisons between tissue morphology changes and cell-infiltration dynamics

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. (c) 2019 The Authors. The Journal of Pathology published by John Wiley amp; Sons Ltd on behalf of Pathological Society of Great Britain and Ireland

High Heregulin Expression Is Associated with Activated HER3 and May Define an Actionable Biomarker in Patients with Squamous Cell Carcinomas of the Head and Neck

<div><p>Purpose</p><p>Tumors with oncogenic dependencies on the HER family of receptor tyrosine kinases (RTKs) often respond well to targeted inhibition. Our previous work suggested that many cell lines derived from squamous cell carcinomas of the head and neck (SCCHNs) depend on autocrine signaling driven by HER2/3 dimerization and high-level co-expression of HRG. Additionally, results from a Phase I trial of MEHD7495A, a dual-action antibody that blocks ligand binding to EGFR and HER3, suggest that high-level <i>HRG</i> expression was associated with clinical response in SCCHN patients. Here we explore the hypothesis that high-level <i>HRG</i> expression defines a subpopulation of SCCHNs with activated HER3.</p> <p>Experimental Design</p><p>qRT-PCR expression profiling was performed on >750 tumors of diverse origin, including >150 therapy-naïve, primary, and recurrent SCCHNs. Activated HER3, defined by immunoprecipitation of phospho-HER3, was compared to <i>HRG</i> expression in SCCHN samples. Paracrine versus autocrine expression was evaluated using RNA-in situ hybridization.</p> <p>Results</p><p>SCCHN tumors express the highest levels of <i>HRG</i> compared to a diverse collection of other tumor types. We show that high <i>HRG</i> expression is associated with activated HER3, whereas low <i>HRG</i> expression is associated with low HER3 activation in SCCHN tumors. Furthermore, <i>HRG</i> expression is higher in recurrent SCCHN compared to patient-matched therapy naïve specimens.</p> <p>Conclusions</p><p><i>HRG</i> expression levels define a biologically distinct subset of SCCHN patients. We propose that high-level expression of <i>HRG</i> is associated with constitutive activation of HER3 in SCCHN and thus defines an actionable biomarker for interventions targeting HER3.</p> </div

Detailed evaluation of <i>HRG</i> and <i>HER3</i> expression in matched therapy naïve and post-chemotherapy SCCHNs.

•<p>RxN corresponds to therapy naïve patients;</p>†<p>PC corresponds to Post-Chemo patients.</p><p>% Autocrine cells were based on tumor cell content only; qRTPCR was performed on macrodissected specimens as outlined in the methods section.</p