Three-dimensional histological electrophoresis enables fast automatic distinguishment of cancer margins and lymph node metastases

Author:

Zhang Feiran12ORCID,Xu Jiajun12,Yue Ying1,Wang Yajun12,Sun Jianing3ORCID,Song Dong4,Zhang Chengbin5ORCID,Qu Limei5,Zhu Shoujun12ORCID,Zhang Junhu12ORCID,Yang Bai12

Affiliation:

1. State Key Laboratory of Supramolecular Structure and Materials, Center for Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China.

2. Joint Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China.

3. School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, P. R. China.

4. Department of Breast Surgery, The First Hospital of Jilin University, Changchun 130021, P. R. China.

5. Department of Pathology, The First Hospital of Jilin University, Changchun 130021, P. R. China.

Abstract

Tissue diagnosis is important during surgical excision of solid tumors for margin evaluation. Conventional histopathologic methods rely heavily on image-based visual diagnosis by specialized pathologists, which can be time-consuming and subjective. We report a three-dimensional (3D) histological electrophoresis system for rapid labeling and separation of the proteins within tissue sections, providing a more precise assessment of tumor-positive margin in surgically resected tissues. The 3D histological electrophoresis system uses a tumor-seeking dye labeling strategy to visualize the distribution of tumor-specific proteins within sections and a tumor finder that automatically predicts the tumor contour. We successfully demonstrated the system’s capability to predict the tumor contours from five murine xenograft models and distinguish the tumor-invaded region of sentinel lymph nodes. Specifically, we used the system to accurately assess tumor-positive margins from 14 patients with cancer. Our 3D histological electrophoresis system serves as an intraoperative tissue assessment technology for more accurate and automatic pathologic diagnosis.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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