|
| Status |
Public on Dec 01, 2023 |
| Title |
plasma, subject 399596aaT1 |
| Sample type |
protein |
| |
|
| Source name |
serum
|
| Organism |
Homo sapiens |
| Characteristics |
subject id: Subj_399596aa
blood sample id: Subj_399596aaT1
Sex: Female
age: 50
days since first sample: 0
patient classification at first sample: SARS-CoV-2_Positive_Ab_Positive
covid-19 status: Positive
in_covid_aki_dataset: No
in_covid_only_dataset: Yes
instrument model: Somascan discovery platform v4
|
| Extracted molecule |
protein |
| Extraction protocol |
Blood samples were collected in Serum Separation Tubes (SST) with a polymer gel for serum separation as previously described in Del Valle DM, Kim-Schulze S, Huang HH, et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat Med. 2020. Samples were centrifuged at 1200 g for 10 minutes at 20°C. After centrifugation, serum was pipetted to a 15 mL conical tube. Serum was then aliquoted into cryovials and stored at -80°C.
|
| Label |
NA
|
| Label protocol |
As described in https://www.somalogic.com/wp-content/uploads/2016/08/SSM-002-Rev-3-SOMAscan-Technical-White-Paper.pdf
|
| |
|
| Hybridization protocol |
As described in https://www.somalogic.com/wp-content/uploads/2016/08/SSM-002-Rev-3-SOMAscan-Technical-White-Paper.pdf
|
| Scan protocol |
As described in https://www.somalogic.com/wp-content/uploads/2016/08/SSM-002-Rev-3-SOMAscan-Technical-White-Paper.pdf
|
| Description |
See https://www.synapse.org/#!Synapse:syn35874390/ for full clinical data and associated metadata including detailed description for process workflow.
|
| Data processing |
As described in Su, C.-Y. et al. Circulating proteins to predict adverse COVID-19 outcomes. medRxiv, 2021.2010.2004.21264015, doi:10.1101/2021.10.04.21264015 (2021).
Preliminary normalization (no log2 transformation) procedure as per Somalogic guidelines. The code workflow creates the normalizations prior to running the linear model. More details, see Data-Preprocessing section as described in Su, C.-Y. et al. Circulating proteins to predict adverse COVID-19 outcomes. medRxiv, 2021.2010.2004.21264015, doi:10.1101/2021.10.04.21264015 (2021).
Script used to analyze the data is available at:
https://github.com/Nadkarni-Lab/aki_covid_proteomics
|
| |
|
| Submission date |
Feb 15, 2023 |
| Last update date |
Dec 01, 2023 |
| Contact name |
Pushkala Jayaraman |
| E-mail(s) |
pushkala.jayaraman@cahn.mssm.edu
|
| Organization name |
Icahn School of Medicine at Mount Sinai
|
| Department |
Charles Bronfman Institute of Personalized Medicine
|
| Lab |
AIMS Lab
|
| Street address |
1 Gustave Levy Pl
|
| City |
New York |
| State/province |
New York |
| ZIP/Postal code |
10029 |
| Country |
USA |
| |
|
| Platform ID |
GPL33128 |
| Series (1) |
| GSE225349 |
Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection |
|
