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Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response

Single-cell RNA-seq is a powerful tool for revealing heterogeneity in cancer cells. However, each of the current single-cell RNA-seq platforms has inherent advantages and disadvantages. Here, we show that combining the different single-cell RNA-seq platforms can be an effective approach to obtaining...

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Autores principales: Kashima, Yukie, Suzuki, Ayako, Liu, Ying, Hosokawa, Masahito, Matsunaga, Hiroko, Shirai, Masataka, Arikawa, Kohji, Sugano, Sumio, Kohno, Takashi, Takeyama, Haruko, Tsuchihara, Katsuya, Suzuki, Yutaka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823859/
https://www.ncbi.nlm.nih.gov/pubmed/29472726
http://dx.doi.org/10.1038/s41598-018-21161-y
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author Kashima, Yukie
Suzuki, Ayako
Liu, Ying
Hosokawa, Masahito
Matsunaga, Hiroko
Shirai, Masataka
Arikawa, Kohji
Sugano, Sumio
Kohno, Takashi
Takeyama, Haruko
Tsuchihara, Katsuya
Suzuki, Yutaka
author_facet Kashima, Yukie
Suzuki, Ayako
Liu, Ying
Hosokawa, Masahito
Matsunaga, Hiroko
Shirai, Masataka
Arikawa, Kohji
Sugano, Sumio
Kohno, Takashi
Takeyama, Haruko
Tsuchihara, Katsuya
Suzuki, Yutaka
author_sort Kashima, Yukie
collection PubMed
description Single-cell RNA-seq is a powerful tool for revealing heterogeneity in cancer cells. However, each of the current single-cell RNA-seq platforms has inherent advantages and disadvantages. Here, we show that combining the different single-cell RNA-seq platforms can be an effective approach to obtaining complete information about expression differences and a sufficient cellular population to understand transcriptional heterogeneity in cancers. We demonstrate that it is possible to estimate missing expression information. We further demonstrate that even in the cases where precise information for an individual gene cannot be inferred, the activity of given transcriptional modules can be analyzed. Interestingly, we found that two distinct transcriptional modules, one associated with the Aurora kinase gene and the other with the DUSP gene, are aberrantly regulated in a minor population of cells and may thus contribute to the possible emergence of dormancy or eventual drug resistance within the population.
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spelling pubmed-58238592018-02-26 Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response Kashima, Yukie Suzuki, Ayako Liu, Ying Hosokawa, Masahito Matsunaga, Hiroko Shirai, Masataka Arikawa, Kohji Sugano, Sumio Kohno, Takashi Takeyama, Haruko Tsuchihara, Katsuya Suzuki, Yutaka Sci Rep Article Single-cell RNA-seq is a powerful tool for revealing heterogeneity in cancer cells. However, each of the current single-cell RNA-seq platforms has inherent advantages and disadvantages. Here, we show that combining the different single-cell RNA-seq platforms can be an effective approach to obtaining complete information about expression differences and a sufficient cellular population to understand transcriptional heterogeneity in cancers. We demonstrate that it is possible to estimate missing expression information. We further demonstrate that even in the cases where precise information for an individual gene cannot be inferred, the activity of given transcriptional modules can be analyzed. Interestingly, we found that two distinct transcriptional modules, one associated with the Aurora kinase gene and the other with the DUSP gene, are aberrantly regulated in a minor population of cells and may thus contribute to the possible emergence of dormancy or eventual drug resistance within the population. Nature Publishing Group UK 2018-02-22 /pmc/articles/PMC5823859/ /pubmed/29472726 http://dx.doi.org/10.1038/s41598-018-21161-y Text en © The Author(s) 2018 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kashima, Yukie
Suzuki, Ayako
Liu, Ying
Hosokawa, Masahito
Matsunaga, Hiroko
Shirai, Masataka
Arikawa, Kohji
Sugano, Sumio
Kohno, Takashi
Takeyama, Haruko
Tsuchihara, Katsuya
Suzuki, Yutaka
Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response
title Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response
title_full Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response
title_fullStr Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response
title_full_unstemmed Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response
title_short Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response
title_sort combinatory use of distinct single-cell rna-seq analytical platforms reveals the heterogeneous transcriptome response
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823859/
https://www.ncbi.nlm.nih.gov/pubmed/29472726
http://dx.doi.org/10.1038/s41598-018-21161-y
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