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Proceedings Paper

Identification and characterization of neutrophil extracellular trap shapes in flow cytometry
Author(s): Brandon Ginley; Tiffany Emmons; Prabhu Sasankan; Constantin Urban; Brahm H. Segal; Pinaki Sarder
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Paper Abstract

Neutrophil extracellular trap (NET) formation is an alternate immunologic weapon used mainly by neutrophils. Chromatin backbones fused with proteins derived from granules are shot like projectiles onto foreign invaders. It is thought that this mechanism is highly anti-microbial, aids in preventing bacterial dissemination, is used to break down structures several sizes larger than neutrophils themselves, and may have several more uses yet unknown. NETs have been implied to be involved in a wide array of systemic host immune defenses, including sepsis, autoimmune diseases, and cancer. Existing methods used to visually quantify NETotic versus non-NETotic shapes are extremely time-consuming and subject to user bias. These limitations are obstacles to developing NETs as prognostic biomarkers and therapeutic targets. We propose an automated pipeline for quantitatively detecting neutrophil and NET shapes captured using a flow cytometry-imaging system. Our method uses contrast limited adaptive histogram equalization to improve signal intensity in dimly illuminated NETs. From the contrast improved image, fixed value thresholding is applied to convert the image to binary. Feature extraction is performed on the resulting binary image, by calculating region properties of the resulting foreground structures. Classification of the resulting features is performed using Support Vector Machine. Our method classifies NETs from neutrophils without traps at 0.97/0.96 sensitivity/specificity on n = 387 images, and is 1500X faster than manual classification, per sample. Our method can be extended to rapidly analyze whole-slide immunofluorescence tissue images for NET classification, and has potential to streamline the quantification of NETs for patients with diseases associated with cancer and autoimmunity.

Paper Details

Date Published: 1 March 2017
PDF: 6 pages
Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400D (1 March 2017); doi: 10.1117/12.2254680
Show Author Affiliations
Brandon Ginley, Univ. at Buffalo (United States)
Tiffany Emmons, Roswell Park Cancer Institute (United States)
Prabhu Sasankan, Roswell Park Cancer Institute (United States)
Constantin Urban, Umeå Univ. (Sweden)
Brahm H. Segal, Roswell Park Cancer Institute (United States)
Univ. at Buffalo (United States)
Pinaki Sarder, Univ. at Buffalo (United States)

Published in SPIE Proceedings Vol. 10140:
Medical Imaging 2017: Digital Pathology
Metin N. Gurcan; John E. Tomaszewski, Editor(s)

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