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Automated fluorescent miscroscopic image analysis of PTBP1 expression in glioma
Author(s) -
Goksel Behiye,
Goceri Evgin,
Elder Brad,
Puduvalli Vinay,
Gurcan Metin,
Otero Jose Javier
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.980.1
Subject(s) - glioma , cancer research , neuropathology , biology , dapi , pathology , autofluorescence , microbiology and biotechnology , medicine , staining , fluorescence , physics , disease , quantum mechanics
Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study focuses on 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 reactive gliosis versus recurrent glioma in patients with prior resections. Our image analysis workflow was capable of removing background autofluorescence and permitted quantification of DAPI‐PTBP1 positive cells and the mean intensity value of PTBP1 signal in cells. 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.PTBP1 antibody validation:(A) siRNA knock‐down workflow for PTBP1 (B) WB of total cell lysates of the siRNA knockdown cells using PTBP1 antibody (C) GAPDH as control (D) Densitometric analysis of WB data pooled from three independent experiments. (E) IF analysis of PTBP1 in scrambled siRNA treated (E1–E3) and anti‐PTBP1 siRNA treated (F1–F3). (G) Work flow for cell plug formation to test PTBP1 in glioma. (H) IF stain of PTBP1 in glioma cell line. Cell lines used in B–D were LN229, E–H were U251Overcoming challenges to visual interpretation of IF images in diagnostic neuropathology: In (A), note that the PTBP1 signal present in nuclei (white arrows) is weaker than background autofluorescence. To underscore this, we photographed in (B) a section of the secondary control that showed hemorrhage. The fluorescent image coming from the red channel in (B3) represents erythrocytes. Also, note the lack of background signal in the DAPI channel (A1 and B1). (C) Image analysis workflow.T‐test results (p values) calculated with numbers of nuclei and mean intensity values from reactive gliosis and recurrent glioma data sets.Numbers of data sets Numbers of positive nuclei in DAPI_PTBP1 images Numbers of positive nuclei in images stained with anti‐PTBP1 antibody Mean intensities of positive nuclei in images stained with anti‐PTBP1 antibody Reactive Gliosis Recurrent Glioma Reactive Gliosis Recurrent Glioma Reactive Gliosis Recurrent Glioma1 6849 19138 513 2438 137.4 558.2 2 6850 17815 1052 2229 202.6 487.0 3 8207 12116 1336 2162 331.4 551.3 4 8691 12036 1429 1248 481.5 479.1P value 0.007 0.031 0.026