Label-Free Deep Profiling of the Tumor Microenvironment.
Animals
Computational Biology
/ methods
Endothelial Cells
/ metabolism
Erythrocytes
/ metabolism
Extracellular Vesicles
/ metabolism
Female
Fibroblasts
/ metabolism
Lymphocytes
/ metabolism
Mammary Neoplasms, Experimental
/ diagnostic imaging
Mice
Neural Networks, Computer
Nonlinear Optical Microscopy
/ methods
Optical Imaging
Rats, Inbred WF
Single-Cell Analysis
/ methods
Tumor Microenvironment
Journal
Cancer research
ISSN: 1538-7445
Titre abrégé: Cancer Res
Pays: United States
ID NLM: 2984705R
Informations de publication
Date de publication:
01 05 2021
01 05 2021
Historique:
received:
17
09
2020
revised:
12
01
2021
accepted:
18
03
2021
pubmed:
21
3
2021
medline:
18
9
2021
entrez:
20
3
2021
Statut:
ppublish
Résumé
Label-free nonlinear microscopy enables nonperturbative visualization of structural and metabolic contrast within living cells in their native tissue microenvironment. Here a computational pipeline was developed to provide a quantitative view of the microenvironmental architecture within cancerous tissue from label-free nonlinear microscopy images. To enable single-cell and single-extracellular vesicle (EV) analysis, individual cells, including tumor cells and various types of stromal cells, and EVs were segmented by a multiclass pixelwise segmentation neural network and subsequently analyzed for their metabolic status and molecular structure in the context of the local cellular neighborhood. By comparing cancer tissue with normal tissue, extensive tissue reorganization and formation of a patterned cell-EV neighborhood was observed in the tumor microenvironment. The proposed analytic pipeline is expected to be useful in a wide range of biomedical tasks that benefit from single-cell, single-EV, and cell-to-EV analysis. SIGNIFICANCE: The proposed computational framework allows label-free microscopic analysis that quantifies the complexity and heterogeneity of the tumor microenvironment and opens possibilities for better characterization and utilization of the evolving cancer landscape.
Identifiants
pubmed: 33741692
pii: 0008-5472.CAN-20-3124
doi: 10.1158/0008-5472.CAN-20-3124
pmc: PMC8137645
mid: NIHMS1688143
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2534-2544Subventions
Organisme : NCI NIH HHS
ID : R01 CA213149
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA241618
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB023232
Pays : United States
Informations de copyright
©2021 American Association for Cancer Research.
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