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Computational Approach to Identifying Universal Macrophage Biomarkers
Macrophages engulf and digest microbes, cellular debris, and various disease-associated cells throughout the body. Understanding the dynamics of macrophage gene expression is crucial for studying human diseases. As both bulk RNAseq and single cell RNAseq datasets become more numerous and complex, id...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156600/ https://www.ncbi.nlm.nih.gov/pubmed/32322218 http://dx.doi.org/10.3389/fphys.2020.00275 |
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author | Dang, Dharanidhar Taheri, Sahar Das, Soumita Ghosh, Pradipta Prince, Lawrence S. Sahoo, Debashis |
author_facet | Dang, Dharanidhar Taheri, Sahar Das, Soumita Ghosh, Pradipta Prince, Lawrence S. Sahoo, Debashis |
author_sort | Dang, Dharanidhar |
collection | PubMed |
description | Macrophages engulf and digest microbes, cellular debris, and various disease-associated cells throughout the body. Understanding the dynamics of macrophage gene expression is crucial for studying human diseases. As both bulk RNAseq and single cell RNAseq datasets become more numerous and complex, identifying a universal and reliable marker of macrophage cell becomes paramount. Traditional approaches have relied upon tissue specific expression patterns. To identify universal biomarkers of macrophage, we used a previously published computational approach called BECC (Boolean Equivalent Correlated Clusters) that was originally used to identify conserved cell cycle genes. We performed BECC analysis using the known macrophage marker CD14 as a seed gene. The main idea behind BECC is that it uses massive database of public gene expression dataset to establish robust co-expression patterns identified using a combination of correlation, linear regression and Boolean equivalences. Our analysis identified and validated FCER1G and TYROBP as novel universal biomarkers for macrophages in human and mouse tissues. |
format | Online Article Text |
id | pubmed-7156600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71566002020-04-22 Computational Approach to Identifying Universal Macrophage Biomarkers Dang, Dharanidhar Taheri, Sahar Das, Soumita Ghosh, Pradipta Prince, Lawrence S. Sahoo, Debashis Front Physiol Physiology Macrophages engulf and digest microbes, cellular debris, and various disease-associated cells throughout the body. Understanding the dynamics of macrophage gene expression is crucial for studying human diseases. As both bulk RNAseq and single cell RNAseq datasets become more numerous and complex, identifying a universal and reliable marker of macrophage cell becomes paramount. Traditional approaches have relied upon tissue specific expression patterns. To identify universal biomarkers of macrophage, we used a previously published computational approach called BECC (Boolean Equivalent Correlated Clusters) that was originally used to identify conserved cell cycle genes. We performed BECC analysis using the known macrophage marker CD14 as a seed gene. The main idea behind BECC is that it uses massive database of public gene expression dataset to establish robust co-expression patterns identified using a combination of correlation, linear regression and Boolean equivalences. Our analysis identified and validated FCER1G and TYROBP as novel universal biomarkers for macrophages in human and mouse tissues. Frontiers Media S.A. 2020-04-08 /pmc/articles/PMC7156600/ /pubmed/32322218 http://dx.doi.org/10.3389/fphys.2020.00275 Text en Copyright © 2020 Dang, Taheri, Das, Ghosh, Prince and Sahoo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Dang, Dharanidhar Taheri, Sahar Das, Soumita Ghosh, Pradipta Prince, Lawrence S. Sahoo, Debashis Computational Approach to Identifying Universal Macrophage Biomarkers |
title | Computational Approach to Identifying Universal Macrophage Biomarkers |
title_full | Computational Approach to Identifying Universal Macrophage Biomarkers |
title_fullStr | Computational Approach to Identifying Universal Macrophage Biomarkers |
title_full_unstemmed | Computational Approach to Identifying Universal Macrophage Biomarkers |
title_short | Computational Approach to Identifying Universal Macrophage Biomarkers |
title_sort | computational approach to identifying universal macrophage biomarkers |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156600/ https://www.ncbi.nlm.nih.gov/pubmed/32322218 http://dx.doi.org/10.3389/fphys.2020.00275 |
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