Published April 29, 2021 | Version v1
Photo Open

MATISSE: a method for improved single cell segmentation in imaging mass cytometry

  • 1. Molecular Cancer Research, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CX, Utrecht, The Netherlands
  • 2. Molecular Cancer Research, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CX, Utrecht, The Netherlands.
  • 3. Molecular Cancer Research, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CX, Utrecht, The Netherlands ; Department of Pathology, University Medical Center Utrecht, Utrecht University, 3584 CX, Utrecht, The Netherlands.
  • 4. Department of Pathology, University Medical Center Utrecht, Utrecht University, 3584 CX, Utrecht, The Netherlands
  • 5. Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht University, 3584 CX, Utrecht, The Netherlands

Description

Abstract

Background 

Visualizing and quantifying cellular heterogeneity is of central importance to study tissue complexity, development and physiology, and has a vital role in understanding pathologies. Mass spectrometry-based methods including imaging mass cytometry (IMC), have in recent years emerged as powerful approaches for assessing cellular heterogeneity in tissues. IMC is an innovative multiplex imaging method that combines imaging using up to 40 metal conjugated antibodies, and provides distributions of protein markers in tissues with a resolution of 1 Micrometer 2 area. However, resolving the output signals of individual cells within the tissue sample, i.e. single cell segmentation, remains challenging. To address this problem, we developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation), a method that combines high resolution fluorescence microscopy with the multiplex capability of IMC into a single workflow to achieve improved segmentation over the current state-of-the-art. 

Results 

MATISSE results in improved quality and quantity of segmented cells when compared to IMC-only segmentation in sections of heterogeneous tissues. Additionally, MATISSE enables more complete and accurate identification of epithelial cells, fibroblasts, and infiltrating immune cells in densely packed cellular areas in tissue sections. MATISSE has been designed based on commonly used open-access tools and regular fluorescence microscopy, allowing easy implementation by labs using multiplex IMC into their analysis methods.

Conclusion 

MATISSE allows segmentation of densely packed cellular areas and provides a qualitative and quantitative improvement when compared to IMC-based segmentation. We expect that implementing MATISSE into tissue section analysis pipelines will yield improved cell segmentation and enable more accurate analysis of the tissue microenvironment in epithelial tissue pathologies, such as autoimmunity and cancer.

Files

MATISSE_DATA.zip

Files (4.6 GB)

Name Size Download all
md5:29ea0f9dad89c1e1297c6b0cc7ea8509
4.6 GB Preview Download