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Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis
BACKGROUND: Single rare cell characterization represents a new scientific front in personalized therapy. Imaging mass cytometry (IMC) may be able to address all these questions by combining the power of MS-CyTOF and microscopy. METHODS: We have investigated this IMC method using < 100 to up to 10...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395380/ https://www.ncbi.nlm.nih.gov/pubmed/32736533 http://dx.doi.org/10.1186/s12885-020-07203-7 |
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author | Batth, Izhar S. Meng, Qing Wang, Qi Torres, Keila E. Burks, Jared Wang, Jing Gorlick, Richard Li, Shulin |
author_facet | Batth, Izhar S. Meng, Qing Wang, Qi Torres, Keila E. Burks, Jared Wang, Jing Gorlick, Richard Li, Shulin |
author_sort | Batth, Izhar S. |
collection | PubMed |
description | BACKGROUND: Single rare cell characterization represents a new scientific front in personalized therapy. Imaging mass cytometry (IMC) may be able to address all these questions by combining the power of MS-CyTOF and microscopy. METHODS: We have investigated this IMC method using < 100 to up to 1000 cells from human sarcoma tumor cell lines by incorporating bioinformatics-based t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis of highly multiplexed IMC imaging data. We tested this process on osteosarcoma cell lines TC71, OHS as well as osteosarcoma patient-derived xenograft (PDX) cell lines M31, M36, and M60. We also validated our analysis using sarcoma patient-derived CTCs. RESULTS: We successfully identified heterogeneity within individual tumor cell lines, the same PDX cells, and the CTCs from the same patient by detecting multiple protein targets and protein localization. Overall, these data reveal that our t-SNE-based approach can not only identify rare cells within the same cell line or cell population, but also discriminate amongst varied groups to detect similarities and differences. CONCLUSIONS: This method helps us make greater inroads towards generating patient-specific CTC fingerprinting that could provide an accurate tumor status from a minimally-invasive liquid biopsy. |
format | Online Article Text |
id | pubmed-7395380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73953802020-08-05 Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis Batth, Izhar S. Meng, Qing Wang, Qi Torres, Keila E. Burks, Jared Wang, Jing Gorlick, Richard Li, Shulin BMC Cancer Research Article BACKGROUND: Single rare cell characterization represents a new scientific front in personalized therapy. Imaging mass cytometry (IMC) may be able to address all these questions by combining the power of MS-CyTOF and microscopy. METHODS: We have investigated this IMC method using < 100 to up to 1000 cells from human sarcoma tumor cell lines by incorporating bioinformatics-based t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis of highly multiplexed IMC imaging data. We tested this process on osteosarcoma cell lines TC71, OHS as well as osteosarcoma patient-derived xenograft (PDX) cell lines M31, M36, and M60. We also validated our analysis using sarcoma patient-derived CTCs. RESULTS: We successfully identified heterogeneity within individual tumor cell lines, the same PDX cells, and the CTCs from the same patient by detecting multiple protein targets and protein localization. Overall, these data reveal that our t-SNE-based approach can not only identify rare cells within the same cell line or cell population, but also discriminate amongst varied groups to detect similarities and differences. CONCLUSIONS: This method helps us make greater inroads towards generating patient-specific CTC fingerprinting that could provide an accurate tumor status from a minimally-invasive liquid biopsy. BioMed Central 2020-07-31 /pmc/articles/PMC7395380/ /pubmed/32736533 http://dx.doi.org/10.1186/s12885-020-07203-7 Text en © The Author(s). 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Batth, Izhar S. Meng, Qing Wang, Qi Torres, Keila E. Burks, Jared Wang, Jing Gorlick, Richard Li, Shulin Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis |
title | Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis |
title_full | Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis |
title_fullStr | Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis |
title_full_unstemmed | Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis |
title_short | Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis |
title_sort | rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395380/ https://www.ncbi.nlm.nih.gov/pubmed/32736533 http://dx.doi.org/10.1186/s12885-020-07203-7 |
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