Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Daily transcriptomic profiling of immune response in COVID-19 cases and healthy controls using the NanoString Human Immunology Panel

ABSTRACT: Daily transcriptomic profiling was conducted on whole blood collected from COVID-19 cases. Whole blood was collected in Tempus Blood RNA tubes, and RNA was extracted from whole blood using the Tempus Spin RNA Isolation Kit. Healthy controls consisted of participants of a measles, mumps and rubella re-vaccination study. Pre-vaccination whole blood was collected and processed and analyzed as above.

ORGANISM(S): Homo sapiens

SUBMITTER: Eugenia Ong

PROVIDER: E-MTAB-9719 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:The DALIA-1 trial was designed to evaluate the safety and immunogenicity of a DC-based vaccine generated by culturing blood monocytes with GM-CSF and IFN-a. DCs were pulsed for 24 hrs with the ANRS LIPO5 peptide pool and activated for the last 6 hrs with LPS. In total, 19 asymptomatic HAART-treated patients were included in the study. Microarray samples were collected in both the treatment phase and follow-up phase of the study. Whole blood RNA samples were collected at two time points prior to the first vaccination (Weeks -8 and -4) and served as a pre-vaccination baseline. Additional whole blood RNA samples were collected at the time of vaccination (Weeks 0, 4, 8, 12) and post-vaccination at weeks 16, 22, and 24. At week 24, an analytical treatment interruption (ATI) was initiated during which HAART was suspended. During this post-ATI follow-up phase, whole blood RNA samples were collected at weeks 25, 26, 27, 28, 32, 36, 40, , 44, and 48). Additionally, at weeks -4, 16, and 48 patients were apheresed. 18 samples are constituents of both the pre-ATI and post-ATI datasets, which were normalized separately. The normalised data underwent the following preprocessing: First a normal-exponential convolution model was applied (Xie et al, Bioinformatics, 2009; Shi et al, Nucleid Acids Research, 2010) to correct for the background noise. An offset of 16 (default value) was then added to the data (to improve precision and avoid a compression of the fold changes) before a quantile-quantile normalization was performed. Finally, data were log2 transformed. Only probes whose pval-detection (provided by Illumina Genome-Studio software) was below 0.01 in at least one sample were kept further on. At last, the ComBat method (Johnson et al, Biostatistics, 2007) was applied to those data in order to correct for batch effects (namely a small hybridization chamber effect, that was identified through Principal Variance Component Analysis - Li et al, chap. 12, Batch Effects and Noise in Microarray Experiments: Sources and Solutions, Wiley, 2009).

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Daily transcriptomic profiling of immune response in COVID-19 cases and healthy controls using the NanoString Human Immunology Panel

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