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Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques

The scale and capability of single-cell and single-nucleus RNA-sequencing technologies are rapidly growing, enabling key discoveries and large-scale cell mapping operations. However, studies directly comparing technical differences between single-cell and single-nucleus RNA sequencing are still lack...

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Autores principales: Pavan, Ricardo R., Diniz, Fabiola, El-Dahr, Samir, Tortelote, Giovane G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132733/
https://www.ncbi.nlm.nih.gov/pubmed/37122996
http://dx.doi.org/10.3389/fbinf.2023.1144266
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author Pavan, Ricardo R.
Diniz, Fabiola
El-Dahr, Samir
Tortelote, Giovane G.
author_facet Pavan, Ricardo R.
Diniz, Fabiola
El-Dahr, Samir
Tortelote, Giovane G.
author_sort Pavan, Ricardo R.
collection PubMed
description The scale and capability of single-cell and single-nucleus RNA-sequencing technologies are rapidly growing, enabling key discoveries and large-scale cell mapping operations. However, studies directly comparing technical differences between single-cell and single-nucleus RNA sequencing are still lacking. Here, we compared three paired single-cell and single-nucleus transcriptomes from three different organs (Heart, Lung and Kidney). Differently from previous studies that focused on cell classification, we explored disparities in the transcriptome output of whole cells relative to the nucleus. We found that the major cell clusters could be recovered by either technique from matched samples, but at different proportions. In 2/3 datasets (kidney and lung) we detected clusters exclusively present with single-nucleus RNA sequencing. In all three organ groups, we found that genomic and gene structural characteristics such as gene length and exon content significantly differed between the two techniques. Genes recovered with the single-nucleus RNA sequencing technique had longer sequence lengths and larger exon counts, whereas single-cell RNA sequencing captured short genes at higher rates. Furthermore, we found that when compared to the whole host genome (mouse for kidney and lung datasets and human for the heart dataset), single transcriptomes obtained with either technique skewed from the expected proportions in several points: a) coding sequence length, b) transcript length and c) genomic span; and d) distribution of genes based on exons counts. Interestingly, the top-100 DEG between the two techniques returned distinctive GO terms. Hence, the type of single transcriptome technique used affected the outcome of downstream analysis. In summary, our data revealed both techniques present disparities in RNA capture. Moreover, the biased RNA capture affected the calculations of basic cellular parameters, raising pivotal points about the limitations and advantages of either single transcriptome techniques.
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spelling pubmed-101327332023-04-27 Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques Pavan, Ricardo R. Diniz, Fabiola El-Dahr, Samir Tortelote, Giovane G. Front Bioinform Bioinformatics The scale and capability of single-cell and single-nucleus RNA-sequencing technologies are rapidly growing, enabling key discoveries and large-scale cell mapping operations. However, studies directly comparing technical differences between single-cell and single-nucleus RNA sequencing are still lacking. Here, we compared three paired single-cell and single-nucleus transcriptomes from three different organs (Heart, Lung and Kidney). Differently from previous studies that focused on cell classification, we explored disparities in the transcriptome output of whole cells relative to the nucleus. We found that the major cell clusters could be recovered by either technique from matched samples, but at different proportions. In 2/3 datasets (kidney and lung) we detected clusters exclusively present with single-nucleus RNA sequencing. In all three organ groups, we found that genomic and gene structural characteristics such as gene length and exon content significantly differed between the two techniques. Genes recovered with the single-nucleus RNA sequencing technique had longer sequence lengths and larger exon counts, whereas single-cell RNA sequencing captured short genes at higher rates. Furthermore, we found that when compared to the whole host genome (mouse for kidney and lung datasets and human for the heart dataset), single transcriptomes obtained with either technique skewed from the expected proportions in several points: a) coding sequence length, b) transcript length and c) genomic span; and d) distribution of genes based on exons counts. Interestingly, the top-100 DEG between the two techniques returned distinctive GO terms. Hence, the type of single transcriptome technique used affected the outcome of downstream analysis. In summary, our data revealed both techniques present disparities in RNA capture. Moreover, the biased RNA capture affected the calculations of basic cellular parameters, raising pivotal points about the limitations and advantages of either single transcriptome techniques. Frontiers Media S.A. 2023-04-12 /pmc/articles/PMC10132733/ /pubmed/37122996 http://dx.doi.org/10.3389/fbinf.2023.1144266 Text en Copyright © 2023 Pavan, Diniz, El-Dahr and Tortelote. https://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 Bioinformatics
Pavan, Ricardo R.
Diniz, Fabiola
El-Dahr, Samir
Tortelote, Giovane G.
Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques
title Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques
title_full Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques
title_fullStr Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques
title_full_unstemmed Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques
title_short Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques
title_sort gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132733/
https://www.ncbi.nlm.nih.gov/pubmed/37122996
http://dx.doi.org/10.3389/fbinf.2023.1144266
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