Research Article: Automated fluorescent miscroscopic image analysis of PTBP1 expression in glioma

Date Published: March 10, 2017

Publisher: Public Library of Science

Author(s): Behiye Kaya, Evgin Goceri, Aline Becker, Brad Elder, Vinay Puduvalli, Jessica Winter, Metin Gurcan, José Javier Otero, Arie Horowitz.


Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study presents the implementation of a methodology capable of overcoming the visual challenges of fluorescent microscopy for diagnostic neuropathology by using automated digital image analysis, with long term goal of providing unbiased quantitative analyses of multiplexed biomarkers for solid tissue neuropathology. In this study, we validated PTBP1, a putative biomarker for glioma, and tested the extent to which immunofluorescent microscopy combined with automated and unbiased image analysis would permit the utility of PTBP1 as a biomarker to distinguish diagnostically challenging surgical biopsies. As a paradigm, we utilized second resections from patients diagnosed either with reactive brain changes (pseudoprogression) and recurrent glioblastoma (true progression). Our image analysis workflow was capable of removing background autofluorescence and permitted quantification of DAPI-PTBP1 positive cells. PTBP1-positive nuclei, and the mean intensity value of PTBP1 signal in cells. Traditional pathological interpretation was unable to distinguish between groups due to unacceptably high discordance rates amongst expert neuropathologists. Our data demonstrated that recurrent glioblastoma showed more DAPI-PTBP1 positive cells and a higher mean intensity value of PTBP1 signal compared to resections from second surgeries that showed only reactive gliosis. Our work demonstrates the potential of utilizing automated image analysis to overcome the challenges of implementing fluorescent microscopy in diagnostic neuropathology.

Partial Text

Translation of basic scientific findings to improved clinical decision-making for neuro-oncology will require implementing unbiased, multiplexed, and objective histopathology interpretation. The current status quo suffers from biased, mainly uniplexed, and subjective histopathology interpretation. To improve the status quo, we must overcome multiple technical barriers including tissue processing, image capture, and image analysis. First, inconsistencies in antibody generation and validation represent well-recognized problems in immunohistochemistry [1], and therefore rigorous validation of all antibodies are required. The College of American Pathologists (CAP) has issued guideline policies and recommendations for immunohistochemistry validation in clinical labs [2]. However, these guideline policies do not address issues regarding antibody generation by vendors nor antibody-biomarker interaction validation. With current CAP guidelines, an antibody generated from an unknown epitope (e.g., HELA cell nuclear extract) could be validated as a biomarker for a clinical lab, or a distributor could change production procedures without changing the reagent’s catalogue number and thus not trigger a new validation need in the clinical lab. Unfortunately, multiplexing, routinely performed in hematopathological flow cytometry assays, is fraught with caveats in solid tissue histology. In addition to frequent incompatibilities in antigen retrieval methods between different epitopes, formalin-fixed paraffin embedded (FFPE) brain tissues show significant autofluorescence that complicates visual interpretation of brain biopsies. These challenges include erythrocyte autofluorescence [3], autofluorescence due to FFPE processing [4], and autofluorescence in some brain cells [5]. In addition, epifluorescent image acquisition shows inconsistencies in emitted light intensities between image captures if using halogen bulbs due to the well-documented decrease in bulb-intensity as bulb-age increases, further introducing significant challenges in utilizing fluorescent microscopy in biomarker quantification.




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