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Series GSE182598 Query DataSets for GSE182598
Status Public on Jun 30, 2023
Title Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis
Organism Homo sapiens
Experiment type Non-coding RNA profiling by high throughput sequencing
Summary Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.
Overall design Measurement of small RNA in 103 plasma samples from healthy individuals.
Web link
Contributor(s) Salignon J, Faridani OR, Miliotis T, Janssens GE, Chen P, Zarrouki B, Sandberg R, Davidsson P, Riedel CG
Citation(s) 37341993
Submission date Aug 23, 2021
Last update date Jun 30, 2023
Contact name Christian Günter Riedel
Organization name Karolinska Institute
Department Biosciences and Nutrition
Lab Riedel
Street address Blickagången 16 (Neo Building, 6th floor)
City Huddinge
State/province Stockholm
ZIP/Postal code SE-141 57
Country Sweden
Platforms (1)
GPL21290 Illumina HiSeq 3000 (Homo sapiens)
Samples (103)
GSM5531984 S1
GSM5531985 S2
GSM5531986 S3
BioProject PRJNA756992
SRA SRP333726

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SOFT formatted family file(s) SOFTHelp
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Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE182598_DESeq2_normalized_counts.xlsx 463.4 Kb (ftp)(http) XLSX
GSE182598_DESeq2_small_RNA_association_with_age.xlsx 58.7 Kb (ftp)(http) XLSX
GSE182598_raw_counts_MINTmap_tRF.txt.gz 68.1 Kb (ftp)(http) TXT
GSE182598_raw_counts_Smallseq_Ensembl.txt.gz 5.1 Mb (ftp)(http) TXT
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Raw data are available in SRA
Processed data are available on Series record

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