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Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
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Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.

Jason D Hipp1,2, Donald J Johann3, Yun Chen1,4

  • 1Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.

Journal of Pathology Informatics
|January 10, 2019
PubMed
Summary
This summary is machine-generated.

Computer-aided laser dissection (CALD) enhances cell procurement from histological sections by automating cell identification and isolation. This improves efficiency for molecular diagnostics and large-scale specimen analysis.

Keywords:
Image analysisSIVQmicrodissectionprobabilistic pairwise Markov model

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Area of Science:

  • Biotechnology
  • Molecular Diagnostics
  • Histology

Background:

  • Next-generation sequencing and proteomics require efficient cell isolation from histological sections.
  • Traditional laser microdissection is time-consuming for large cell numbers or multiple specimens.

Purpose of the Study:

  • To develop an automated workflow for cell identification and microdissection.
  • To improve the efficiency of procuring target cells from tissue specimens.

Main Methods:

  • Developed computer-aided laser dissection (CALD) workflow using open-source image analysis algorithms.
  • Integrated CALD with probabilistic pairwise Markov model, ImageJ, SIVQ, and eSeg.
  • Applied CALD to ThermoFisher Scientific ArcturusXT and Leica LMD7000 microdissection platforms.

Main Results:

  • Demonstrated CALD's ability to automate cell identification and drive the microdissection process.
  • Showcased integration of image analysis tools with microdissection platforms through various use cases.

Conclusions:

  • CALD methodology significantly enhances microdissection efficiency.
  • This approach has potential impact on clinical and life science applications, particularly in molecular diagnostics.