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Decoding breast cancer tissue–stroma interactions using species-specific sequencing
INTRODUCTION: Decoding transcriptional effects of experimental tissue–tissue or cell–cell interactions is important; for example, to better understand tumor–stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4534116/ https://www.ncbi.nlm.nih.gov/pubmed/26265142 http://dx.doi.org/10.1186/s13058-015-0616-x |
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author | Chivukula, Indira V. Ramsköld, Daniel Storvall, Helena Anderberg, Charlotte Jin, Shaobo Mamaeva, Veronika Sahlgren, Cecilia Pietras, Kristian Sandberg, Rickard Lendahl, Urban |
author_facet | Chivukula, Indira V. Ramsköld, Daniel Storvall, Helena Anderberg, Charlotte Jin, Shaobo Mamaeva, Veronika Sahlgren, Cecilia Pietras, Kristian Sandberg, Rickard Lendahl, Urban |
author_sort | Chivukula, Indira V. |
collection | PubMed |
description | INTRODUCTION: Decoding transcriptional effects of experimental tissue–tissue or cell–cell interactions is important; for example, to better understand tumor–stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells has, however, frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression. METHODS: To circumvent this problem, we here describe a bioinformatics-based, genome-wide transcriptome analysis technique, which allows the human and mouse transcriptomes to be decoded from a mixed mouse and human cell population. The technique is based on a bioinformatic separation of the mouse and human transcriptomes from the initial mixed-species transcriptome resulting from sequencing an excised tumor/stroma specimen without prior cell sorting. RESULTS: Under stringent separation criteria, i.e., with a read misassignment frequency of 0.2 %, we show that 99 % of the genes can successfully be assigned to be of mouse or human origin, both in silico, in cultured cells and in vivo. We use a new species-specific sequencing technology—referred to as S(3) (“S-cube”)—to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of two different breast cancer cell lines (luminal MCF7 and basal-type MDA-MB-231) into mammary fat pad tissue in mice of different immunological status. We find that MCF7 and MDA-MB-231 respond differently to fat pad xenografting and the stromal response to transplantation of MCF7 and MDA-MB-231 cells was also distinct. CONCLUSIONS: In conclusion, the data show that the S(3) technology allows for faithful recording of transcriptomic changes when human and mouse cells are intermixed and that it can be applied to address a broad spectrum of research questions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-015-0616-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4534116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45341162015-08-13 Decoding breast cancer tissue–stroma interactions using species-specific sequencing Chivukula, Indira V. Ramsköld, Daniel Storvall, Helena Anderberg, Charlotte Jin, Shaobo Mamaeva, Veronika Sahlgren, Cecilia Pietras, Kristian Sandberg, Rickard Lendahl, Urban Breast Cancer Res Research Article INTRODUCTION: Decoding transcriptional effects of experimental tissue–tissue or cell–cell interactions is important; for example, to better understand tumor–stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells has, however, frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression. METHODS: To circumvent this problem, we here describe a bioinformatics-based, genome-wide transcriptome analysis technique, which allows the human and mouse transcriptomes to be decoded from a mixed mouse and human cell population. The technique is based on a bioinformatic separation of the mouse and human transcriptomes from the initial mixed-species transcriptome resulting from sequencing an excised tumor/stroma specimen without prior cell sorting. RESULTS: Under stringent separation criteria, i.e., with a read misassignment frequency of 0.2 %, we show that 99 % of the genes can successfully be assigned to be of mouse or human origin, both in silico, in cultured cells and in vivo. We use a new species-specific sequencing technology—referred to as S(3) (“S-cube”)—to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of two different breast cancer cell lines (luminal MCF7 and basal-type MDA-MB-231) into mammary fat pad tissue in mice of different immunological status. We find that MCF7 and MDA-MB-231 respond differently to fat pad xenografting and the stromal response to transplantation of MCF7 and MDA-MB-231 cells was also distinct. CONCLUSIONS: In conclusion, the data show that the S(3) technology allows for faithful recording of transcriptomic changes when human and mouse cells are intermixed and that it can be applied to address a broad spectrum of research questions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-015-0616-x) contains supplementary material, which is available to authorized users. BioMed Central 2015-08-13 2015 /pmc/articles/PMC4534116/ /pubmed/26265142 http://dx.doi.org/10.1186/s13058-015-0616-x Text en © Chivukula et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Article Chivukula, Indira V. Ramsköld, Daniel Storvall, Helena Anderberg, Charlotte Jin, Shaobo Mamaeva, Veronika Sahlgren, Cecilia Pietras, Kristian Sandberg, Rickard Lendahl, Urban Decoding breast cancer tissue–stroma interactions using species-specific sequencing |
title | Decoding breast cancer tissue–stroma interactions using species-specific sequencing |
title_full | Decoding breast cancer tissue–stroma interactions using species-specific sequencing |
title_fullStr | Decoding breast cancer tissue–stroma interactions using species-specific sequencing |
title_full_unstemmed | Decoding breast cancer tissue–stroma interactions using species-specific sequencing |
title_short | Decoding breast cancer tissue–stroma interactions using species-specific sequencing |
title_sort | decoding breast cancer tissue–stroma interactions using species-specific sequencing |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4534116/ https://www.ncbi.nlm.nih.gov/pubmed/26265142 http://dx.doi.org/10.1186/s13058-015-0616-x |
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