Proteomic profiling of serum extracellular vesicles from uranium-exposed miners
Data files
Oct 06, 2025 version files 399.62 KB
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large_EVs_COMPLETE.csv
97.12 KB
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large_EVs_normalized.csv
98.54 KB
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Miner_Info.csv
657 B
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README.md
6.98 KB
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small_EVs_COMPLETE.csv
97.23 KB
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small_EVs_normalized.csv
99.09 KB
Abstract
This dataset contains quantitative proteomic data from serum-derived extracellular vesicles (EVs) isolated from 29 former uranium miners. Small and large EVs were separated by differential ultracentrifugation and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The dataset includes raw and normalized protein intensities for each EV subtype, as well as metadata on mining tenure and age. These data were used to examine associations between mining tenure and the molecular profiles of serum-derived EVs. EV proteomics may serve as a sensitive tool for assessing the long-term health effects of uranium exposure.
Dataset DOI: 10.5061/dryad.931zcrjxd
Description of the data and file structure
Serum-derived EVs Proteomics Analysis in Uranium Miners
This repository contains data and documentation for the proteomic analysis of serum-derived extracellular vesicles (EVs) from uranium miners, focusing on the relationship between mining tenure and EVs-derived protein expression profiles.
1. Project Overview
This study investigates how uranium mining exposure affects the protein content of serum-derived small and large EVs. Miners were classified into short tenure (ST) and long tenure (LT) groups based on their mining history, and proteomic differences were assessed accordingly.
2. Source Files
This dataset consists of 5 individual CSV files.
- File 1:
Miner_Info.csv(Participant metadata) - File 2:
small_EVs_COMPLETE.csv(Raw protein intensities for small EVs) - File 3:
small_EVs_normalized.csv(Normalized protein intensities for small EVs) - File 4:
large_EVs_COMPLETE.csv(Raw protein intensities for large EVs) - File 5:
large_EVs_normalized.csv(Normalized protein intensities for large EVs)
3. Sample Metadata (File: Miner Info.csv)
Contains miner-level metadata and phenotype classification used in downstream analysis.
| Column Name | Description |
|---|---|
| Sample ID | Unique identifier for each participant (e.g., OLC_10002) |
| Mining Tenure | Total years of uranium mining experience |
| Tenure Classification | Group label based on tenure: ST (1–9 years), LT (10–40 years) |
| Age | Age in years at time of sample collection |
4. Proteomics Data Files
Each file below contains quantified protein intensities per sample for either small or large EVs, in both raw and normalized formats.
4.1 File: small EVs_COMPLETE.csv
- Type: Raw protein intensity
- Rows: Each sample (by Sample ID)
- Columns: Proteins (e.g., Alpha-2-macroglobulin [OS=Homo sapiens], etc.)
4.2 File: small EVs_normalized.csv
- Type: Normalized intensities of small EV proteins
- Normalization method: Total peptide amount
4.3 File: large EVs_COMPLETE.csv
- Type: Raw protein intensity from large EVs
4.4 File: large EVs_normalized.csv
- Type: Normalized large EV protein intensities
Note: Protein names are annotated in UniProt format (e.g., "[OS=Homo sapiens]").
5. Data Processing Workflow
Software and Database
- Search Software: Thermo Proteome Discoverer v2.5
- Database: UniProt Homo sapiens (reviewed; downloaded Oct 26, 2023)
Search Parameters
- Enzyme: Trypsin (allowing 2 missed cleavages)
- Static Modifications: Carbamidomethyl (C)
- Variable Modifications: Oxidation (M), Acetyl (N-term)
- Precursor Tolerance: 10 ppm
- Fragment Tolerance: 0.02 Da
- FDR Threshold: 1%
6. Quantification and Normalization
- Quantification Method: Intensity-based precursor abundance
- Normalization: Total peptide amount normalization for intra-sample correction
7. Output Summary
| File Name | Description |
|---|---|
Miner Info.csv |
Sample ID and phenotype annotations (age, tenure) |
small EVs_COMPLETE.csv |
Raw intensities of small EV proteins |
small EVs_normalized.csv |
Normalized small EV protein data |
large EVs_COMPLETE.csv |
Raw intensities of large EV proteins |
large EVs_normalized.csv |
Normalized large EV protein data |
8. Contact
For questions, please contact:
- Name: Dr. Katherine Zychowski
- Affiliation: University of New Mexico
- Email: kzychowski@salud.unm.edu
9. Human Subjects Data
All human subjects' data included in this dataset have been de-identified in compliance with applicable legal and ethical standards. Explicit informed consent was obtained from all study participants for the public sharing of their de-identified data. Participant metadata includes only non-identifiable variables such as age (in years) and mining tenure (in years). The 'Race/Ethnicity' and 'Smoking History' columns were removed from the dataset to comply with Dryad's data anonymization policy.
10. Files and Variables
File 1: Miner_Info.csv
- Description: Participant metadata including anonymized demographic information (age, mining tenure) and tenure classification.
- Variables: Sample ID, Mining Tenure, Tenure Classification, Age.
File 2: small_EVs_COMPLETE.csv
- Description: Raw, un-normalized protein intensities from small extracellular vesicles (S-EVs) for each participant.
- Variables: Sample ID, Protein abundances by UniProt accession.
File 3: small_EVs_normalized.csv
- Description: Normalized protein intensities from small extracellular vesicles (S-EVs) for each participant.
- Variables: Sample ID, Normalized protein abundances by UniProt accession.
File 4: large_EVs_COMPLETE.csv
- Description: Raw, un-normalized protein intensities from large extracellular vesicles (L-EVs) for each participant.
- Variables: Sample ID, Protein abundances by UniProt accession.
File 5: large_EVs_normalized.csv
- Description: Normalized protein intensities from large extracellular vesicles (L-EVs) for each participant.
- Variables: Sample ID, Normalized protein abundances by UniProt accession.
Human subjects data
All human subjects data included in this dataset have been fully de-identified in compliance with applicable legal and ethical standards. Explicit informed consent was obtained from all study participants for the collection, analysis, and public sharing of their de-identified data. No personally identifiable information (PII), including names, contact information, or identifiable health data, is included in the dataset.
De-identification procedures involved removing all direct identifiers. To comply with Dryad's data anonymization policy and minimize the risk of re-identification, the 'Race/Ethnicity' and 'Smoking History' variables were removed from the public dataset. The remaining participant metadata includes only non-identifiable variables such as age (in years) and mining tenure (in years). All samples were assigned anonymized study ID codes with no link to personal identifiers.
The dataset is compliant with Dryad’s human subjects data policy and is appropriate for open-access publication under the CC0 license.
