Automated gating pipeline for enumeration and characterization of extracellular vesicles in next-generation biomarker studies

Presenter Type

UNO Graduate Student (Doctoral)

Major/Field of Study

Bioinformatics

Advisor Information

Hesham Ali

Location

MBSC Ballroom Poster # 1309 - G (Doctoral)

Presentation Type

Poster

Start Date

24-3-2023 2:30 PM

End Date

24-3-2023 3:45 PM

Abstract

Extracellular vesicles (EVs) are submicron particles (< 1 μm) released by all cell types including tumor cells in body fluids such as blood and urine. Flow cytometry is one of the most widely used methods for detection of EVs and allows single particle analysis with immunophenotyping in a high-throughput manner. Studies have shown that circulating EV levels may be the next-generation biomarkers for improving diagnosis and prognosis of cancer patients. However, the current challenge in analyzing the flow cytometry data is manual gating process. Manual gating is not only time-consuming and subjective but also error-prone. In this study, we propose a novel pipeline for automated gating of EV flow cytometry in order to quantify EV sub-populations. The pipeline utilizes machine learning methods to identify EV sub-population of interest from liquid biopsies of patient samples. The results show that the proposed automated gating pipeline accurately identifies the disease specific EV sub-population in an unbiased manner and produces comparable results to that of manual gating.

Scheduling

1-2:15 p.m., 2:30 -3:45 p.m.

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COinS
 
Mar 24th, 2:30 PM Mar 24th, 3:45 PM

Automated gating pipeline for enumeration and characterization of extracellular vesicles in next-generation biomarker studies

MBSC Ballroom Poster # 1309 - G (Doctoral)

Extracellular vesicles (EVs) are submicron particles (< 1 μm) released by all cell types including tumor cells in body fluids such as blood and urine. Flow cytometry is one of the most widely used methods for detection of EVs and allows single particle analysis with immunophenotyping in a high-throughput manner. Studies have shown that circulating EV levels may be the next-generation biomarkers for improving diagnosis and prognosis of cancer patients. However, the current challenge in analyzing the flow cytometry data is manual gating process. Manual gating is not only time-consuming and subjective but also error-prone. In this study, we propose a novel pipeline for automated gating of EV flow cytometry in order to quantify EV sub-populations. The pipeline utilizes machine learning methods to identify EV sub-population of interest from liquid biopsies of patient samples. The results show that the proposed automated gating pipeline accurately identifies the disease specific EV sub-population in an unbiased manner and produces comparable results to that of manual gating.