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El-Sabawi B, Huang S, Tanriverdi K, Perry AS, Amancherla K, Jackson N, Hulsey J, Freedman JE, Shah R, Lindman BR. Capillary blood self-collection for high-throughput proteomics. Proteomics 2024:e2300607. [PMID: 38783781 DOI: 10.1002/pmic.202300607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/09/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
In this study, we sought to compare protein concentrations obtained from a high-throughput proteomics platform (Olink) on samples collected using capillary blood self-collection (with the Tasso+ device) versus standard venipuncture (control). Blood collection was performed on 20 volunteers, including one sample obtained via venipuncture and two via capillary blood using the Tasso+ device. Tasso+ samples were stored at 2°C-8°C for 24-hs (Tasso-24) or 48-h (Tasso-48) prior to processing to simulate shipping times from a study participant's home. Proteomics were analyzed using Olink (384 Inflammatory Panel). Tasso+ blood collection was successful in 37/40 attempts. Of 230 proteins included in our analysis, Pearson correlations (r) and mean coefficient of variation (CV) between Tasso-24 or Tasso-48 versus venipuncture were variable. In the Tasso-24 analysis, 34 proteins (14.8%) had both a correlation r > 0.5 and CV < 0.20. In the Tasso-48 analysis, 68 proteins (29.6%) had a correlation r > 0.5 and CV < 0.20. Combining the Tasso-24 and Tasso-48 analyses, 26 (11.3%) proteins met these thresholds. We concluded that protein concentrations from Tasso+ samples processed 24-48 h after collection demonstrated wide technical variability and variable correlation with a venipuncture gold-standard. Use of home capillary blood self-collection for large-scale proteomics should be limited to select proteins with good agreement with venipuncture.
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Affiliation(s)
- Bassim El-Sabawi
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shi Huang
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Kahraman Tanriverdi
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Andrew S Perry
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kaushik Amancherla
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Natalie Jackson
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Structural Heart and Valve Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jenna Hulsey
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Structural Heart and Valve Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jane E Freedman
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ravi Shah
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Brian R Lindman
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Structural Heart and Valve Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Zhang J, Zhou Y, Lei J, Liu X, Zhang N, Wu L, Li Y. Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics. Analyst 2024; 149:515-527. [PMID: 38078496 DOI: 10.1039/d3an01735d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Dysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.
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Affiliation(s)
- Jiangang Zhang
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Yu Zhou
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Juan Lei
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Xudong Liu
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Nan Zhang
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Lei Wu
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Yongsheng Li
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China.
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