A single workflow for multi-species blood transcriptomics
Abstract Background Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-024-10208-2 |
| _version_ | 1827316210032705536 |
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| author | Elody Orcel Hayat Hage May Taha Noémie Boucher Emilie Chautard Virginie Courtois Adrien Saliou |
| author_facet | Elody Orcel Hayat Hage May Taha Noémie Boucher Emilie Chautard Virginie Courtois Adrien Saliou |
| author_sort | Elody Orcel |
| collection | DOAJ |
| description | Abstract Background Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons. Results Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated. Conclusions Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy. |
| first_indexed | 2024-04-24T23:10:54Z |
| format | Article |
| id | doaj.art-d5c4a753fb8249b19f6ee4c60a79d693 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-24T23:10:54Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-d5c4a753fb8249b19f6ee4c60a79d6932024-03-17T12:16:25ZengBMCBMC Genomics1471-21642024-03-0125111610.1186/s12864-024-10208-2A single workflow for multi-species blood transcriptomicsElody Orcel0Hayat Hage1May Taha2Noémie Boucher3Emilie Chautard4Virginie Courtois5Adrien Saliou6BIOASTERBIOASTERBIOASTERBIOASTERSANOFISANOFIBIOASTERAbstract Background Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons. Results Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated. Conclusions Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.https://doi.org/10.1186/s12864-024-10208-2Preclinical modelsClinical modelsBlood samplesRNA extractionLibrary preparationTotal RNA sequencing |
| spellingShingle | Elody Orcel Hayat Hage May Taha Noémie Boucher Emilie Chautard Virginie Courtois Adrien Saliou A single workflow for multi-species blood transcriptomics BMC Genomics Preclinical models Clinical models Blood samples RNA extraction Library preparation Total RNA sequencing |
| title | A single workflow for multi-species blood transcriptomics |
| title_full | A single workflow for multi-species blood transcriptomics |
| title_fullStr | A single workflow for multi-species blood transcriptomics |
| title_full_unstemmed | A single workflow for multi-species blood transcriptomics |
| title_short | A single workflow for multi-species blood transcriptomics |
| title_sort | single workflow for multi species blood transcriptomics |
| topic | Preclinical models Clinical models Blood samples RNA extraction Library preparation Total RNA sequencing |
| url | https://doi.org/10.1186/s12864-024-10208-2 |
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