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| 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
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| 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.
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| Overall design |
Measurement of small RNA in 103 plasma samples from healthy individuals.
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| Web link |
https://doi.org/10.18632/aging.204787
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| Contributor(s) |
Salignon J, Faridani OR, Miliotis T, Janssens GE, Chen P, Zarrouki B, Sandberg R, Davidsson P, Riedel CG |
| Citation(s) |
37341993 |
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| Submission date |
Aug 23, 2021 |
| Last update date |
Jun 30, 2023 |
| Contact name |
Christian Günter Riedel |
| E-mail(s) |
christian.riedel@ki.se
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| Organization name |
Karolinska Institute
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| Department |
Biosciences and Nutrition
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| Lab |
Riedel
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| Street address |
Blickagången 16 (Neo Building, 6th floor)
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| City |
Huddinge |
| State/province |
Stockholm |
| ZIP/Postal code |
SE-141 57 |
| Country |
Sweden |
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| Platforms (1) |
| GPL21290 |
Illumina HiSeq 3000 (Homo sapiens) |
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| Samples (103)
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| Relations |
| BioProject |
PRJNA756992 |
| SRA |
SRP333726 |
| 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 |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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