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scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis

INTRODUCTION: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcri...

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Autores principales: Nale, Valentina, Chiodi, Alice, Di Nanni, Noemi, Cifola, Ingrid, Moscatelli, Marco, Cocola, Cinzia, Gnocchi, Matteo, Piscitelli, Eleonora, Sula, Ada, Zucchi, Ileana, Reinbold, Rolland, Milanesi, Luciano, Mezzelani, Alessandra, Pelucchi, Paride, Mosca, Ettore
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680269/
https://www.ncbi.nlm.nih.gov/pubmed/38012590
http://dx.doi.org/10.1186/s12859-023-05563-y
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author Nale, Valentina
Chiodi, Alice
Di Nanni, Noemi
Cifola, Ingrid
Moscatelli, Marco
Cocola, Cinzia
Gnocchi, Matteo
Piscitelli, Eleonora
Sula, Ada
Zucchi, Ileana
Reinbold, Rolland
Milanesi, Luciano
Mezzelani, Alessandra
Pelucchi, Paride
Mosca, Ettore
author_facet Nale, Valentina
Chiodi, Alice
Di Nanni, Noemi
Cifola, Ingrid
Moscatelli, Marco
Cocola, Cinzia
Gnocchi, Matteo
Piscitelli, Eleonora
Sula, Ada
Zucchi, Ileana
Reinbold, Rolland
Milanesi, Luciano
Mezzelani, Alessandra
Pelucchi, Paride
Mosca, Ettore
author_sort Nale, Valentina
collection PubMed
description INTRODUCTION: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data. RESULTS: scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics. CONCLUSIONS: The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05563-y.
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spelling pubmed-106802692023-11-27 scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis Nale, Valentina Chiodi, Alice Di Nanni, Noemi Cifola, Ingrid Moscatelli, Marco Cocola, Cinzia Gnocchi, Matteo Piscitelli, Eleonora Sula, Ada Zucchi, Ileana Reinbold, Rolland Milanesi, Luciano Mezzelani, Alessandra Pelucchi, Paride Mosca, Ettore BMC Bioinformatics Software INTRODUCTION: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data. RESULTS: scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics. CONCLUSIONS: The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05563-y. BioMed Central 2023-11-27 /pmc/articles/PMC10680269/ /pubmed/38012590 http://dx.doi.org/10.1186/s12859-023-05563-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Software
Nale, Valentina
Chiodi, Alice
Di Nanni, Noemi
Cifola, Ingrid
Moscatelli, Marco
Cocola, Cinzia
Gnocchi, Matteo
Piscitelli, Eleonora
Sula, Ada
Zucchi, Ileana
Reinbold, Rolland
Milanesi, Luciano
Mezzelani, Alessandra
Pelucchi, Paride
Mosca, Ettore
scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis
title scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis
title_full scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis
title_fullStr scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis
title_full_unstemmed scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis
title_short scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis
title_sort scmuffin: an r package to disentangle solid tumor heterogeneity by single-cell gene expression analysis
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680269/
https://www.ncbi.nlm.nih.gov/pubmed/38012590
http://dx.doi.org/10.1186/s12859-023-05563-y
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