Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity

Takahiro Tsujikawa, Guillaume Thibault, Vahid Azimi, Sam Sivagnanam, Grace Banik, Casey Means, Rie Kawashima, Daniel Clayburgh, Joe Gray, Lisa Coussens, Young Hwan Chang

Research output: Contribution to journalArticle

Abstract

Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare TH17 cells, further enabling sub-population analysis into TH1-like and TH2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of TH2-like TH17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, TH2-like TH17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b+ granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration.

Original languageEnglish (US)
JournalCytometry Part A
DOIs
StatePublished - Jan 1 2019

Fingerprint

Image Cytometry
Th17 Cells
Papillomaviridae
Immunohistochemistry
Biomarkers
Cell Lineage
Hematoxylin
Eosine Yellowish-(YS)
Immunosuppressive Agents
Head and Neck Neoplasms
Granulocytes
Immunotherapy
Cluster Analysis
Neoplasms
Phenotype
Sensitivity and Specificity
Population

Keywords

  • cell segmentation
  • T17 cell phenotypes
  • tumor immune microenvironment

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Cite this

Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity. / Tsujikawa, Takahiro; Thibault, Guillaume; Azimi, Vahid; Sivagnanam, Sam; Banik, Grace; Means, Casey; Kawashima, Rie; Clayburgh, Daniel; Gray, Joe; Coussens, Lisa; Chang, Young Hwan.

In: Cytometry Part A, 01.01.2019.

Research output: Contribution to journalArticle

@article{142ba766a6e54f8f81b112deb89df696,
title = "Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity",
abstract = "Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare TH17 cells, further enabling sub-population analysis into TH1-like and TH2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of TH2-like TH17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, TH2-like TH17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b+ granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration.",
keywords = "cell segmentation, T17 cell phenotypes, tumor immune microenvironment",
author = "Takahiro Tsujikawa and Guillaume Thibault and Vahid Azimi and Sam Sivagnanam and Grace Banik and Casey Means and Rie Kawashima and Daniel Clayburgh and Joe Gray and Lisa Coussens and Chang, {Young Hwan}",
year = "2019",
month = "1",
day = "1",
doi = "10.1002/cyto.a.23726",
language = "English (US)",
journal = "Cytometry. Part A : the journal of the International Society for Analytical Cytology",
issn = "1552-4922",
publisher = "Wiley-Liss Inc.",

}

TY - JOUR

T1 - Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity

AU - Tsujikawa, Takahiro

AU - Thibault, Guillaume

AU - Azimi, Vahid

AU - Sivagnanam, Sam

AU - Banik, Grace

AU - Means, Casey

AU - Kawashima, Rie

AU - Clayburgh, Daniel

AU - Gray, Joe

AU - Coussens, Lisa

AU - Chang, Young Hwan

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare TH17 cells, further enabling sub-population analysis into TH1-like and TH2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of TH2-like TH17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, TH2-like TH17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b+ granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration.

AB - Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare TH17 cells, further enabling sub-population analysis into TH1-like and TH2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of TH2-like TH17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, TH2-like TH17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b+ granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration.

KW - cell segmentation

KW - T17 cell phenotypes

KW - tumor immune microenvironment

UR - http://www.scopus.com/inward/record.url?scp=85061026017&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061026017&partnerID=8YFLogxK

U2 - 10.1002/cyto.a.23726

DO - 10.1002/cyto.a.23726

M3 - Article

JO - Cytometry. Part A : the journal of the International Society for Analytical Cytology

JF - Cytometry. Part A : the journal of the International Society for Analytical Cytology

SN - 1552-4922

ER -