Cargando…

Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration

Microarray-based studies of global gene expression (GE) have resulted in a large amount of data that can be mined for further insights into disease and physiology. Meta-analysis of these data is hampered by technical limitations due to many different platforms, gene annotations and probes used in di...

Descripción completa

Detalles Bibliográficos
Autores principales: Sohal, Davendra, Yeatts, Andrew, Ye, Kenny, Pellagatti, Andrea, Zhou, Li, Pahanish, Perry, Mo, Yongkai, Bhagat, Tushar, Mariadason, John, Boultwood, Jacqueline, Melnick, Ari, Greally, John, Verma, Amit
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2495035/
https://www.ncbi.nlm.nih.gov/pubmed/18698424
http://dx.doi.org/10.1371/journal.pone.0002965
_version_ 1782158261986787328
author Sohal, Davendra
Yeatts, Andrew
Ye, Kenny
Pellagatti, Andrea
Zhou, Li
Pahanish, Perry
Mo, Yongkai
Bhagat, Tushar
Mariadason, John
Boultwood, Jacqueline
Melnick, Ari
Greally, John
Verma, Amit
author_facet Sohal, Davendra
Yeatts, Andrew
Ye, Kenny
Pellagatti, Andrea
Zhou, Li
Pahanish, Perry
Mo, Yongkai
Bhagat, Tushar
Mariadason, John
Boultwood, Jacqueline
Melnick, Ari
Greally, John
Verma, Amit
author_sort Sohal, Davendra
collection PubMed
description Microarray-based studies of global gene expression (GE) have resulted in a large amount of data that can be mined for further insights into disease and physiology. Meta-analysis of these data is hampered by technical limitations due to many different platforms, gene annotations and probes used in different studies. We tested the feasibility of conducting a meta-analysis of GE studies to determine a transcriptional signature of hematopoietic progenitor and stem cells. Data from studies that used normal bone marrow-derived hematopoietic progenitors was integrated using both RefSeq and UniGene identifiers. We observed that in spite of variability introduced by experimental conditions and different microarray platforms, our meta-analytical approach can distinguish biologically distinct normal tissues by clustering them based on their cell of origin. When studied in terms of disease states, GE studies of leukemias and myelodysplasia progenitors tend to cluster with normal progenitors and remain distinct from other normal tissues, further validating the discriminatory power of this meta-analysis. Furthermore, analysis of 57 normal hematopoietic stem and progenitor cell GE samples was used to determine a gene expression signature characteristic of these cells. Genes that were most uniformly expressed in progenitors and at the same time differentially expressed when compared to other normal tissues were found to be involved in important biological processes such as cell cycle regulation and hematopoiesis. Validation studies using a different microarray platform demonstrated the enrichment of several genes such as SMARCE, Septin 6 and others not previously implicated in hematopoiesis. Most interestingly, alpha-integrin, the only common stemness gene discovered in a recent comparative murine analysis (Science 302(5644):393) was also enriched in our dataset, demonstrating the usefulness of this analytical approach.
format Text
id pubmed-2495035
institution National Center for Biotechnology Information
language English
publishDate 2008
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-24950352008-08-13 Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration Sohal, Davendra Yeatts, Andrew Ye, Kenny Pellagatti, Andrea Zhou, Li Pahanish, Perry Mo, Yongkai Bhagat, Tushar Mariadason, John Boultwood, Jacqueline Melnick, Ari Greally, John Verma, Amit PLoS One Research Article Microarray-based studies of global gene expression (GE) have resulted in a large amount of data that can be mined for further insights into disease and physiology. Meta-analysis of these data is hampered by technical limitations due to many different platforms, gene annotations and probes used in different studies. We tested the feasibility of conducting a meta-analysis of GE studies to determine a transcriptional signature of hematopoietic progenitor and stem cells. Data from studies that used normal bone marrow-derived hematopoietic progenitors was integrated using both RefSeq and UniGene identifiers. We observed that in spite of variability introduced by experimental conditions and different microarray platforms, our meta-analytical approach can distinguish biologically distinct normal tissues by clustering them based on their cell of origin. When studied in terms of disease states, GE studies of leukemias and myelodysplasia progenitors tend to cluster with normal progenitors and remain distinct from other normal tissues, further validating the discriminatory power of this meta-analysis. Furthermore, analysis of 57 normal hematopoietic stem and progenitor cell GE samples was used to determine a gene expression signature characteristic of these cells. Genes that were most uniformly expressed in progenitors and at the same time differentially expressed when compared to other normal tissues were found to be involved in important biological processes such as cell cycle regulation and hematopoiesis. Validation studies using a different microarray platform demonstrated the enrichment of several genes such as SMARCE, Septin 6 and others not previously implicated in hematopoiesis. Most interestingly, alpha-integrin, the only common stemness gene discovered in a recent comparative murine analysis (Science 302(5644):393) was also enriched in our dataset, demonstrating the usefulness of this analytical approach. Public Library of Science 2008-08-13 /pmc/articles/PMC2495035/ /pubmed/18698424 http://dx.doi.org/10.1371/journal.pone.0002965 Text en Sohal et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Sohal, Davendra
Yeatts, Andrew
Ye, Kenny
Pellagatti, Andrea
Zhou, Li
Pahanish, Perry
Mo, Yongkai
Bhagat, Tushar
Mariadason, John
Boultwood, Jacqueline
Melnick, Ari
Greally, John
Verma, Amit
Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration
title Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration
title_full Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration
title_fullStr Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration
title_full_unstemmed Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration
title_short Meta-Analysis of Microarray Studies Reveals a Novel Hematopoietic Progenitor Cell Signature and Demonstrates Feasibility of Inter-Platform Data Integration
title_sort meta-analysis of microarray studies reveals a novel hematopoietic progenitor cell signature and demonstrates feasibility of inter-platform data integration
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2495035/
https://www.ncbi.nlm.nih.gov/pubmed/18698424
http://dx.doi.org/10.1371/journal.pone.0002965
work_keys_str_mv AT sohaldavendra metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT yeattsandrew metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT yekenny metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT pellagattiandrea metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT zhouli metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT pahanishperry metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT moyongkai metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT bhagattushar metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT mariadasonjohn metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT boultwoodjacqueline metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT melnickari metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT greallyjohn metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration
AT vermaamit metaanalysisofmicroarraystudiesrevealsanovelhematopoieticprogenitorcellsignatureanddemonstratesfeasibilityofinterplatformdataintegration