A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination

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Hagood" }, { "@type": "Person", "name": "Raymond J Pickles" }, { "@type": "Person", "name": "Fei Zou" }, { "@type": "Person", "name": "Xiaojing Zheng" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Our understanding of the human immune system’s response to viral respiratory tract infections (VRTIs) and vaccines, including the molecular mechanisms and correlates of protection, remains incomplete. Extensive transcriptomic data from inoculation and vaccination studies have been deposited in publicly available databases. However, these studies are often separate and difficult to locate. Methods To bridge this research gap, we have systematically searched and reviewed publicly available datasets from NCBI Gene Expression Omnibus (GEO), Archive of Functional Genomics Data (ArrayExpress), Immunology Database and Analysis Portal (ImmPort), and Google using targeted queries. Inoculation study queries included terms related to humans, blood, PBMCs, nasal, respiratory challenges, and respiratory viral inoculations; while vaccination study queries focused on humans, blood, PBMCs, transcriptomes, and respiratory viral vaccines or vaccinations. Results This collection includes 18 datasets from inoculation of 5 respiratory viruses: H1N1, H3N2, RSV, HRV, and SARS-CoV-2 (14 from blood and 4 from nasal swabs) with 429 participants (ages 18 to 55 years) and 37 datasets from vaccination of influenza and COVID-19 with 2,084 participants (ages 0.5 to over 89 years). The duration and number of post-immunization time points range from 14 days before to 28 days after inoculation (1 to 20 time points) and from 28 days before to 360 days after vaccination (1 to 13 time points). Conclusion We provide a curated compendium of public gene expression data repositories for researchers to reanalyze transcriptomes from human whole blood, peripheral blood mononuclear cells (PBMCs), and nasal swab samples. This will facilitate studies of transcriptional responses to respiratory viral inoculation or vaccination. 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F1000Research 2025, 14 :493 ( https://doi.org/10.12688/f1000research.162267.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination [version 1; peer review: 1 approved with reservations] Zheng Lan https://orcid.org/0000-0002-7439-110X 1 , Tongli Zhang 2 , Yu Zhang 1 , [...] Xuejun Sun 1 , Chuwen Liu 1 , Pei Liu 3 , Muyao Tang 1 , Meng Fu 1 , James S. Hagood https://orcid.org/0000-0003-3938-0330 4,5 , Raymond J Pickles 5,6 , Fei Zou 1 , Xiaojing Zheng 1,7 Zheng Lan https://orcid.org/0000-0002-7439-110X 1 , Tongli Zhang 2 , [...] Yu Zhang 1 , Xuejun Sun 1 , Chuwen Liu 1 , Pei Liu 3 , Muyao Tang 1 , Meng Fu 1 , James S. Hagood https://orcid.org/0000-0003-3938-0330 4,5 , Raymond J Pickles 5,6 , Fei Zou 1 , Xiaojing Zheng 1,7 PUBLISHED 13 May 2025 Author details Author details 1 Department of Biostatistics, The University of North Carolina at Chapel Hill Department of Biostatistics, Chapel Hill, North Carolina, USA 2 Industrial Engineering & Operations Research Department, University of California Berkeley, Berkeley, California, USA 3 Department of Probability and Statistics, University of Science and Technology of China, Hefei, Anhui, China 4 Department of Pediatrics, Division of Pulmonology, UNC School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 5 Marsico Lung Institute, UNC School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 6 Department of Microbiology & Immunology, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 7 Department of Pediatrics, Division of Infectious Diseases, UNC School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA Zheng Lan Roles: Data Curation, Validation Tongli Zhang Roles: Data Curation, Writing – Original Draft Preparation Yu Zhang Roles: Resources Xuejun Sun Roles: Resources Chuwen Liu Roles: Resources Pei Liu Roles: Resources Muyao Tang Roles: Formal Analysis, Visualization Meng Fu Roles: Visualization James S. Hagood Roles: Resources Raymond J Pickles Roles: Resources Fei Zou Roles: Conceptualization, Investigation, Supervision Xiaojing Zheng Roles: Conceptualization, Investigation, Methodology, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Our understanding of the human immune system’s response to viral respiratory tract infections (VRTIs) and vaccines, including the molecular mechanisms and correlates of protection, remains incomplete. Extensive transcriptomic data from inoculation and vaccination studies have been deposited in publicly available databases. However, these studies are often separate and difficult to locate. Methods To bridge this research gap, we have systematically searched and reviewed publicly available datasets from NCBI Gene Expression Omnibus (GEO), Archive of Functional Genomics Data (ArrayExpress), Immunology Database and Analysis Portal (ImmPort), and Google using targeted queries. Inoculation study queries included terms related to humans, blood, PBMCs, nasal, respiratory challenges, and respiratory viral inoculations; while vaccination study queries focused on humans, blood, PBMCs, transcriptomes, and respiratory viral vaccines or vaccinations. Results This collection includes 18 datasets from inoculation of 5 respiratory viruses: H1N1, H3N2, RSV, HRV, and SARS-CoV-2 (14 from blood and 4 from nasal swabs) with 429 participants (ages 18 to 55 years) and 37 datasets from vaccination of influenza and COVID-19 with 2,084 participants (ages 0.5 to over 89 years). The duration and number of post-immunization time points range from 14 days before to 28 days after inoculation (1 to 20 time points) and from 28 days before to 360 days after vaccination (1 to 13 time points). Conclusion We provide a curated compendium of public gene expression data repositories for researchers to reanalyze transcriptomes from human whole blood, peripheral blood mononuclear cells (PBMCs), and nasal swab samples. This will facilitate studies of transcriptional responses to respiratory viral inoculation or vaccination. READ ALL READ LESS Keywords Transcriptomics, Bioinformatics, Vaccination, Inoculation, Respiratory viral infection, Influenza viruses, COVID, Respiratory syncytial viruses (RSV), Human rhinoviruses (HRV) Whole Blood, PBMC. Corresponding Author(s) Fei Zou ( [email protected] ) Xiaojing Zheng ( [email protected] ) Close Corresponding authors: Fei Zou, Xiaojing Zheng Competing interests: No competing interests were disclosed. Grant information: This work is supported by National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH) under grant [P01 AI178377]; National Heart Lung and Blood Institute(NHLBI) of the NIH under grant [R01 HL173044] Copyright: © 2025 Lan Z et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Lan Z, Zhang T, Zhang Y et al. A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :493 ( https://doi.org/10.12688/f1000research.162267.1 ) First published: 13 May 2025, 14 :493 ( https://doi.org/10.12688/f1000research.162267.1 ) Latest published: 13 May 2025, 14 :493 ( https://doi.org/10.12688/f1000research.162267.1 ) Introduction Viral respiratory tract infections (VRTIs) are widespread globally, causing significant illnesses and hospitalizations each year. 1 The importance of studying VRTIs has grown in light of the global COVID-19 pandemic. 2 VRTIs involve a range of viruses, including respiratory syncytial viruses (RSV), human rhinoviruses (HRV), and influenza and coronaviruses. 3 Most VRTIs lack both effective antiviral therapies and approved vaccines. 4 In-depth investigation of human immune responses to inoculation and vaccination is critically needed. To facilitate further research, we have compiled a curated dataset collection, which includes transcriptomic data from the blood and nasal swabs of volunteers who underwent inoculation and vaccination, specifically for influenza, COVID-19, RSV, and HRV. Our curated dataset collection is divided into two categories: inoculation studies and vaccination studies. The inoculation collection includes 18 datasets with 429 participants, while the vaccination collection comprises 37 datasets with 2,084 participants. All datasets were sourced from GEO, ImmPort Shared Data, and the ArrayExpress Collection at EMBL-EBI. 5 – 7 These datasets have been organized into a publicly accessible format for download and further analysis. Methods The datasets were gathered using specific search queries across GEO, ArrayExpress, ImmPort, and Google search. The queries for inoculation studies included terms related to humans, blood, PBMCs, nasal, respiratory challenges, and viral inoculations. The vaccination study queries followed similar criteria, focusing on humans, blood, PBMCs, transcriptomes, and vaccines or vaccinations. The search queries used for GEO, ArrayExpress, and Google search to find inoculation studies are as follows: GEO and ArrayExpress Inoculation Search Query (“humans”[MeSH Terms] OR “Homo sapiens”[Organism] OR Homo sapiens [All Fields]) AND ((“blood”[Subheading] OR “blood”[MeSH Terms] OR blood [All Fields]) OR PBMC [All Fields] OR PBMCs [All Fields]) AND respiratory [All Fields] AND (challenge [All Fields] OR “experimentally infected”[All Fields] OR inoculated [All Fields]) Google Inoculation Search Query Homo sapiens AND (blood OR PBMC OR PBMCs) AND transcriptome AND respiratory AND (inoculation OR challenge OR “experimentally infected” OR inoculated) The search queries used for GEO, ArrayExpress, and Google to find vaccination studies are as follows: GEO and ArrayExpress Vaccine Search Query (“humans”[MeSH Terms] OR “Homo sapiens”[Organism] OR Homo sapiens [All Fields]) AND ((“blood”[Subheading] OR “blood”[MeSH Terms] OR blood [All Fields]) OR PBMC [All Fields] OR PBMCs [All Fields]) AND respiratory [All Fields] AND ((“vaccination”[MeSH Terms] OR inoculation [All Fields]) OR (“vaccines”[MeSH Terms] OR vaccine [All Fields]) OR (“vaccines”[MeSH Terms] OR vaccines [All Fields]) OR (“vaccination”[MeSH Terms] OR vaccination [All Fields])) Google Vaccine Search Query Homo sapiens AND (blood OR PBMC OR PBMCs) AND transcriptome AND respiratory AND (vaccine OR vaccines OR vaccination) For ImmPort , vaccine studies were located using the following filters: • Species: Homo sapiens • Research Focus: Vaccine Response • Condition or Disease: COVID-19 AND Influenza Results The majority of datasets obtained were derived from human blood, PBMCs, and nasal swabs, generated using Illumina or Affymetrix platforms or through RNA sequencing. Every dataset identified through this search was carefully curated manually. This involved thoroughly reviewing the dataset descriptions, examining the study designs, and reading through the related original articles on PubMed. Ultimately, we only included studies that involved human whole blood, PBMCs, and nasal swabs linked to VRTI inoculation or vaccination for our dataset collection. According to these criteria, we retained 18 inoculation datasets and 37 vaccine-related datasets. The data selection process for inoculation is summarized in Figure 1a . Using GEO and ArrayExpress inoculation query, 108 studies were found with Homo sapiens as the primary organism. Google search returned 92 inoculation studies. The data selection process for vaccination is summarized in Figure 1b . GEO and ArrayExpress Vaccine query identified 82 studies where Homo sapiens is the top organism. Google search produced 89 vaccination studies. ImmPort search yielded 99 vaccination studies. Figure 1. Flowchart illustrating construction of inoculation (a) and vaccination (b) dataset compendium. Figure 2 illustrates the distribution of participants over time in 18 inoculation studies, involving 429 participants aged 18 to 55 years, across five respiratory viruses: H1N1, H3N2, RSV, HRV, and SARS-CoV-2. There is a notable peak on day 0. The duration and number of post-immunization time points range from 14 days before to 28 days after inoculation, spanning 1 to 20 time points. Notably, 56 participants from four studies had both blood and nasal samples, and they were counted separately. Subsequent time points show smaller but consistent sample collections, with varying contributions from different pathogens. H1N1, H3N2, and HRV are the most frequently sampled pathogens, while SARS-CoV-2 and RSV contribute fewer samples to the dataset. Figure 2. A collection of datasets covering transcriptional responses to inoculation over time across varying viruses. Histogram of the time points pre- and post-inoculation available in our compendium. Each virus is indicated by a different color. The height of the bars represents the number of participants with available gene expression data. Figure 3 summarizes the distribution of 2,084 participants, aged 0.5 to over 89 years, over time in 37 vaccination studies covering COVID-19 and influenza vaccines and their types. The peak occurred on day 0, with participant recruitment enriched during the first week. The duration and number of post-immunization time points range from 28 days before to 360 days after vaccination, spanning 1 to 13 time points. Figure 3. A collection of datasets covering transcriptional responses to vaccination over time across COVID-19 and influenza. Histogram of the time points pre- and post-vaccination is available in our compendium. Each vaccine and type are indicated by a different color. The height of the bars represents the number of participants with available gene expression data. The complete list of inoculation datasets included in our collection can be found in Table 1 . Table 1. Complete list of inoculation datasets. Pathogen Sample type Sample size Profiling platform Time point Accession number Citation HRV Peripheral blood 50 Microarray (Affymetrix) -0.88, 0, 0.21, 0.5, 0.88, 1.21, 1.5, 1.88, 2.21, 2.5, 2.88, 3.21, 3.5, 3.88, 4.21, 4.5, 4.92, 5.21, 5.92, 6.92 GSE73072 8 RSV 20 H3N2 38 H1N1 43 HRV Peripheral blood 20 Microarray (Affymetrix) 0 GSE17156 9 RSV 20 H3N2 17 H3N2 Peripheral blood 17 Microarray (Affymetrix) 0, 0.21, 0.50, 0.88, 1.21, 1.50, 1.88, 2.21, 2.50, 2.88, 3.21, 3.50, 3.88, 4.21, 4.50 GSE30550 10 H3N2 Whole blood 11 Microarray (Illumina) 0,0.50,1,2 GSE61754 11 SARS-CoV-2 Blood and nose swabs 36 RNA-seq 0, 0.25, 14, 28(blood) 0,1,3,5,7,10,14(nose swabs) E-MTAB-12993 12 H3N2 Blood and nose swabs 20 RNA-seq 0, 1, 2, 3, 7, 10, 14, and 28(blood) 0,1, 2,3,7,14(nose swabs) EGAD50000000956 HRV Nose swabs 17 Microarray (Affymetrix) -14,0.33, 2.00 GSE11348 13 H1N1 Whole blood 21 Microarray (Illumina) 0, 1, 2, 3, 4 GSE90732 14 RSV Nose swabs 58 RNA-seq 0,3 GSE155237 15 H1N1 PBMC 24 Microarray (Affymetrix) 0, 0.21, 0.50, 0.90, 1.21, 1.50, 1.90, 2.21, 2.50, 2.90, 3.21, 3.50, 3.90, 4.21, 4.50 GSE52428 16 H3N2 17 We have collected transcriptomes from 18 cohorts across 10 inoculation studies, totaling 429 participants. This collection covers inoculations of five respiratory viruses: H1N1, H3N2, RSV, HRV, and SARS-CoV-2. The time span ranges from -14 to 28 days before and post for inoculation. The sample source includes blood (n=14) and nasal swabs (n=4). The transcriptomic data types include microarrays from Illumina (n=2) and Affy (n=11); bulk RNA-seq (n=5) ( Figure 4 ). Figure 4. Distribution of inoculation datasets across different profiling platforms. The complete list of vaccination datasets included in our collection can be found in Table 2 . Table 2. Complete list of vaccine datasets. Pathogen Vaccine type Sample type Sample size Profiling platform Time point Accession number Citation Influenza TIV PBMC 172 Microarray (Illumina) 0,2,7,28 GSE107990 17 Influenza LAIV Whole blood 20 Microarray (Illumina) 0,1,7,30 GSE52005 18 Influenza TIV Whole blood 17 Microarray (Illumina) 0,1,7,30 Influenza LAIV PBMC 28 Microarray (Affymetrix) 0,3,7 GSE29619 19 Influenza TIV PBMC 28 Microarray (Affymetrix) 0,3,7 Influenza LAIV and inactivated Blood 44 RNA-seq 0,3,7,29,85,92,113 GSE217770 20 Influenza inactivated PBMC 50 RNA-seq 0,1,3,7,21,22,24,28 GSE102012 21 Influenza LAIV Nasal epithelium 40 RNA-seq 0,3 GSE230494 22 Influenza LAIV Nasal cells 55 RNA-seq 0,5,12 GSE117580 23 Influenza TIV Nasal cells 62 RNA-seq 0,5,12 Influenza vetor viral virus Whole blood 11 Microarray (Illumina) 0,0.5,1,2 GSE61754 11 Influenza Inactivated PBMC 14 RNA-seq -7,0,1,2,3,4,5,6,7,8,9,10,21 GSE45764 24 Influenza Inactivated Whole blood 18 Microarray (Illumina) -7,0,1,3,7,10,14,21,28 GSE30101/GSE48762 25 Influenza Inactivated PBMC 212 Microarray (Affymetrix) 0,3,7,14 GSE74817 26 Influenza Inactivated Whole blood 247 Microarray (Illumina) 0,1,3,14 GSE48024 Influenza Inactivated Whole blood 91 Microarray (Illumina) 0 GSE41080 Influenza Inactivated PBMC 5 RNA-seq 0,1,2,3,4,5,6,7,8,9,10 GSE45735 Influenza Inactivated PBMC 60 Microarray (Illumina) 0,2,4,7,28 GSE59743/GSE95584 Influenza Inactivated PBMC 27 Microarray (Affymetrix) 0,3,7 GSE29617/GSE29614 Influenza Inactivated PBMC 64 Microarray (Illumina) 0,2,4,7,28 GSE59654 Influenza Inactivated Whole blood 51 Microarray (Illumina) 0,2,7,28 GSE101709 Influenza Inactivated PBMC 42 Microarray (Illumina) 0,4,7,28 GSE59635 Influenza Inactivated Whole blood 44 Microarray (Illumina) 0,2,7,28 GSE101710 Influenza Inactivated PBMC 63 Microarray (Affymetrix) -7,0,1,7,70 GSE47353 Influenza LAIV PBMC 28 Microarray (Affymetrix) 0,3,7 GSE29615 influenza Inactivated Whole blood 123 Microarray (Affymetrix) −7,0,1,3,7,10,14,21,28 SDY311/SDY312/SDY314/SDY315/SDY112 27 Influenza TIV Whole blood 34 Microarray (Illumina) 0,7,14 SDY272/SDY648/SDY739/SDY819/SDY622 28 influenza TIV Whole blood 65 RNA-seq 0,2,7,28 SDY1393 29 Influenza TIV Whole blood 6 RNA-seq 1,3,60 SDY300 * COVID mRNA PBMC 16 scRNA 0,7,21,28 GSE247917 30 COVID mRNA Blood 23 RNA-seq 0,1,2,3,4,5,6,7,8,9 GSE190001 31 COVID mRNA PBMC 214 RNA-seq 0,22,90,180,360 GSE220682 32 COVID mRNA Blood 6 scRNA 0,1,2,7,21,22,28,42 GSE171964 33 COVID mRNA Blood 56 RNA-seq 0,1,7,21,22,28 GSE169159 COVID mRNA PBMC 8 scRNA 28,35,60,110,201 GSE195673 34 COVID mRNA PBMC 4 scRNA 0,7,30,90 GSE210229 35 COVID vetor viral virus Whole blood 36 RNA-seq 0,50,57 GSE228842 36 * This data is available at ImmPort ( https://immport.org/shared/home ) under study accession SDY300: Healthy Human DC and monocyte subsets transcriptional regulations in response to Fluzone 2010-2011 and pneumococcal vaccinations. We have gathered transcriptomes from 37 cohorts across 22 vaccine studies, totaling 2084 participants. This collection focuses on studying Influenza and COVID-19 vaccines. The time span ranges from -28 to 360 days. The data source includes blood (n=34) and nasal swabs (n=3). The transcriptomic data types include microarrays from Illumina (n=13) and Affy (n=7); bulk RNA-seq (n=13) and scRNA-seq (n=4) ( Figure 5 ). Vaccine types: TIV (n=22) and LAIV (n=6) were used for influenza vaccination, mRNA vaccine (n=7) was used for COVID-19 vaccination, and vector adenovirus (n=2) was used for both influenza and COVID-19, one of each. Figure 5. Distribution of vaccine datasets across different profiling platforms. Ethics and consent Ethical approval and consent were not required. Data availability All datasets in our curated collection are also accessible to the public on the NCBI GEO website at https://www.ncbi.nlm.nih.gov/gds/ ArrayExpress at https://www.ebi.ac.uk/biostudies/arrayexpress/studies ImmPort at https://www.immport.org/shared/home , and the European Genome-phenome Archive (EGA) 37 at https://ega-archive.org/datasets/ cited in the manuscript using its GEO number, ArrayExpress number, ImmPort study number or EGA study number. All these citation and accession numbers are listed in Table 1 . The dataset can be found by searching for the accession number on the corresponding websites listed above. Note that downloading from ImmPort requires registering a personal account on their website. Accession number NCBI: Expression Omnibus (GEO): Transcriptome datasets of the transcriptional response in blood samples following HRV, RSV, H3N2 and H1N1 inoculation. Accession Number: GSE73072; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73072 8 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets of the transcriptional response in blood samples following HRV, RSV and H3N2 inoculation. Accession Number: GSE17156; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17156 9 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets of the transcriptional response in blood samples following H3N2 inoculation. Accession Number: GSE30550; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30550 10 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets of the transcriptional response in blood samples following H3N2 inoculation. Accession Number: GSE61754; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61754 11 EMBL-EBI: ArrayExpress: Transcriptome datasets of the transcriptional response in blood and nasal samples following SARS-CoV-2 inoculation. Accession Number: E-MTAB-12993; https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-12993 12 European Genome-Phenome Archive (EGA): Transcriptome datasets to study the transcriptional response in blood samples following H3N2 inoculation. Accession Number: EGAD50000000956; https://ega-archive.org/datasets/EGAD50000000956 12 (Data available on request) NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in nasal samples following HRV inoculation. Accession Number: GSE11348; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11348 14 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following H1N1 inoculation. Accession Number: GSE90732; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90732 15 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in nasal samples following RSV inoculation. Accession Number: GSE155237; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE155237 15 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following H1N1 and H3N2 inoculation. Accession Number: GSE52428; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52428 16 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE107990; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE107990 17 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE52005; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52005 18 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE29619; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29619 19 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE217770; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE217770 20 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE102012; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102012 21 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in nasal samples following influenza vaccination. Accession Number: GSE230494; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE230494 22 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in nasal samples following influenza vaccination. Accession Number: GSE117580; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117580 23 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE61754; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61754 11 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE45764; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45764 24 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE30101 and GSE48762; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30101 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48762 25 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE74817; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74817 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE48024; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48024 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE41080; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41080 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE45735; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45735 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE59743 and GSE95584; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE59743 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95584 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE29617 and GSE29614; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29617 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29614 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE59654; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE59654 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE101709; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101709 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE59635; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE59635 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE101710; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101710 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE47353; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE47353 26 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: GSE29615; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29615 26 ImmPort: Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: SDY311, SDY312, SDY314, SDY315 and SDY112; https://www.immport.org/shared/study/SDY311 , https://www.immport.org/shared/study/SDY312 , https://www.immport.org/shared/study/SDY314 , https://www.immport.org/shared/study/SDY315 and https://www.immport.org/shared/study/SDY112 27 ImmPort: Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: SDY272, SDY648, SDY739, SDY819 and SDY622; https://www.immport.org/shared/study/SDY272 , https://www.immport.org/shared/study/SDY648 , https://www.immport.org/shared/study/SDY739 , https://www.immport.org/shared/study/SDY819 and https://www.immport.org/shared/study/SDY622 28 ImmPort: Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: SDY1393; https://www.immport.org/shared/study/SDY1393 29 ImmPort: Transcriptome datasets to study the transcriptional response in blood samples following influenza vaccination. Accession Number: SDY300; https://www.immport.org/shared/study/SDY300 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following COVID vaccination. Accession Number: GSE247917; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE247917 30 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following COVID vaccination. Accession Number: GSE190001; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190001 31 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following COVID vaccination. Accession Number: GSE220682; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE220682 32 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following COVID vaccination. Accession Number: GSE171964 and GSE169159; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171964 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169159 33 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following COVID vaccination. Accession Number: GSE195673; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195673 34 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following COVID vaccination. Accession Number: GSE210229; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE210229 35 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in blood samples following COVID vaccination. Accession Number: GSE228842; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE228842 36 NCBI: Gene Expression Omnibus (GEO): Transcriptome datasets to study the transcriptional response in nasal samples following HRV inoculation. Accession Number: GSE11348; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11348 13 Acknowledgment We extend our gratitude to all the researchers who have publicly shared their datasets by depositing them in GEO, ArrayExpress, ImmPort and EGA. References 1. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 May 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Biostatistics, The University of North Carolina at Chapel Hill Department of Biostatistics, Chapel Hill, North Carolina, USA 2 Industrial Engineering & Operations Research Department, University of California Berkeley, Berkeley, California, USA 3 Department of Probability and Statistics, University of Science and Technology of China, Hefei, Anhui, China 4 Department of Pediatrics, Division of Pulmonology, UNC School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 5 Marsico Lung Institute, UNC School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 6 Department of Microbiology & Immunology, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 7 Department of Pediatrics, Division of Infectious Diseases, UNC School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA Zheng Lan Roles: Data Curation, Validation Tongli Zhang Roles: Data Curation, Writing – Original Draft Preparation Yu Zhang Roles: Resources Xuejun Sun Roles: Resources Chuwen Liu Roles: Resources Pei Liu Roles: Resources Muyao Tang Roles: Formal Analysis, Visualization Meng Fu Roles: Visualization James S. Hagood Roles: Resources Raymond J Pickles Roles: Resources Fei Zou Roles: Conceptualization, Investigation, Supervision Xiaojing Zheng Roles: Conceptualization, Investigation, Methodology, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work is supported by National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH) under grant [P01 AI178377]; National Heart Lung and Blood Institute(NHLBI) of the NIH under grant [R01 HL173044] Article Versions (1) version 1 Published: 13 May 2025, 14:493 https://doi.org/10.12688/f1000research.162267.1 Copyright © 2025 Lan Z et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. 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Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 13 May 2025 Views 0 Cite How to cite this report: Gardinassi LG. Reviewer Report For: A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :493 ( https://doi.org/10.5256/f1000research.178441.r418093 ) The direct URL for this report is: https://f1000research.com/articles/14-493/v1#referee-response-418093 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 Oct 2025 Luiz Gustavo Gardinassi , University of Sao Paulo, Ribeirão Preto, State of São Paulo, Brazil Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.178441.r418093 The data compendium described in this manuscript can be a useful community resource. There are a few issues that need to be better explored. - Define inoculation studies as controlled human challenges and the advantages of such ... Continue reading READ ALL The data compendium described in this manuscript can be a useful community resource. There are a few issues that need to be better explored. - Define inoculation studies as controlled human challenges and the advantages of such experimental approach to understand host transcriptional responses in the introduction section. What are the main differences compared to natural infections? How are they more related to vaccine studies? - Although the compendium gathered the studies into the same framework, it is unclear how many studies are ready to be used in statistical and other analysis and those that need additional processing before moving forward to statistical and functional analysis, such as filtering and normalization. This can have a major influence depending on the computational resources from users. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Systems immunology, transcriptomics, metabolomics applied to the study of infectious and parasitic diseases. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Gardinassi LG. Reviewer Report For: A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :493 ( https://doi.org/10.5256/f1000research.178441.r418093 ) The direct URL for this report is: https://f1000research.com/articles/14-493/v1#referee-response-418093 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 May 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 13 May 25 read Luiz Gustavo Gardinassi , University of Sao Paulo, Ribeirão Preto, Brazil Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Gardinassi L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Oct 2025 | for Version 1 Luiz Gustavo Gardinassi , University of Sao Paulo, Ribeirão Preto, State of São Paulo, Brazil 0 Views copyright © 2025 Gardinassi L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The data compendium described in this manuscript can be a useful community resource. There are a few issues that need to be better explored. - Define inoculation studies as controlled human challenges and the advantages of such experimental approach to understand host transcriptional responses in the introduction section. What are the main differences compared to natural infections? How are they more related to vaccine studies? - Although the compendium gathered the studies into the same framework, it is unclear how many studies are ready to be used in statistical and other analysis and those that need additional processing before moving forward to statistical and functional analysis, such as filtering and normalization. This can have a major influence depending on the computational resources from users. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Systems immunology, transcriptomics, metabolomics applied to the study of infectious and parasitic diseases. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Gardinassi LG. Peer Review Report For: A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :493 ( https://doi.org/10.5256/f1000research.178441.r418093) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-493/v1#referee-response-418093 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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