From Nanoflow to Standard Flow LC/MS for Routine Quantitative Plasma Proteomics in Diabetic Kidney Disease Research

Applications | 2020 | Agilent TechnologiesInstrumentation
LC/MS, LC/MS/MS, LC/QQQ
Industries
Clinical Research
Manufacturer
Agilent Technologies

Summary

Importance of the Topic


Diabetic kidney disease is a major complication of diabetes and existing clinical tests have limited sensitivity for early prediction. Mass spectrometry based proteomic assays such as PromarkerD offer quantitative measurement of plasma protein biomarkers for early detection of DKD. Transitioning from nanoflow to standard flow LC/MS platforms can enhance robustness and throughput for routine clinical research assays.

Study Objectives and Overview


This study aimed to transfer a previously developed nanoflow LC/MRM method for PromarkerD protein biomarkers to an Agilent 1290 Infinity II LC coupled with a 6495 triple quadrupole mass spectrometer using standard flow chromatography. The objectives were to compare analytical performance between flow regimes and evaluate sensitivity in whole and immunodepleted plasma.

Methodology and Instrumentation


Sample Collection and Preparation
  • EDTA plasma from type 2 diabetes subjects in the Fremantle Diabetes Study was collected after overnight fasting and stored at -80 °C.
  • A standard reference plasma was prepared from healthy volunteers.
  • Immunodepletion of abundant proteins was performed using an Agilent Human 14 affinity column.

PromarkerD Assay Transfer
  • Synthetic peptide mix containing 21 endogenous and 17 isotopically labeled peptides spiked into reference serum.
  • Dynamic MRM method optimized in Skyline software.

Liquid Chromatography and Mass Spectrometry
  • Agilent 1290 Infinity II LC with ZORBAX Eclipse Plus Rapid Resolution C18 column (50 × 2.1 mm, 1.8 μm).
  • Mobile phases: 0.1% formic acid in water and in acetonitrile.
  • Gradient flow at 0.4 mL/min from 8% to 97% organic over 26 minutes.
  • Agilent 6495B Triple Quadrupole MS in positive AJS ESI mode with dynamic MRM.

Key Results and Discussion


Comparison of nanoflow and standard flow platforms across 12 plasma samples showed equivalent relative peak areas for eight peptides representing four proteins (APOA4, CD5L, FHR2, IBP3). Dynamic MRM dot product values exceeded 0.9 for most transitions indicating high specificity. Whole plasma and depleted plasma analyses on the 6495 LC/TQ yielded reproducible chromatograms with relative standard deviations below 20% over 17 hours, demonstrating robust performance and sufficient sensitivity without immunodepletion.

Benefits and Practical Applications


  • Standard flow LC/MS offers improved robustness and reproducibility over nanoflow for routine assays.
  • The 6495 LC/TQ system enables sensitive quantitation of low abundance plasma proteins in complex matrices.
  • Potential for clinical research and future diagnostic assay development targeting early DKD biomarkers.

Future Trends and Potential Applications


The integration of high-throughput standard flow proteomics with targeted MRM workflows is poised to accelerate biomarker validation studies and clinical trials. Emerging instrument technologies and multiplexed assays will further enhance sensitivity, reduce analysis time, and lower costs. Application of machine learning to MS data may streamline biomarker panel optimization and predictive modeling in diabetic kidney disease and other conditions.

Conclusion


The study demonstrates successful transfer of a nanoflow PromarkerD MRM method to a standard flow Agilent 1290 Infinity II/6495 LC/TQ platform without compromising sensitivity or reproducibility. Standard flow LC/MS provides a viable approach for routine quantitative plasma proteomics in diabetic kidney disease research and prospective clinical assay development.

References


  1. Dunkler D et al. Clin J Am Soc Nephrol 2015;10(8):1371–1379.
  2. Peters KE et al. Diabetes Care 2017;40(11):1548–1555.
  3. Peters KE et al. J Diabetes Complications 2019;27:107406.
  4. Bringans SD et al. EuPA Open Proteomics 2017;14:1–10.
  5. Percy AJ et al. Anal Bioanal Chem 2012;404(4):1089–1101.
  6. Davis TM et al. Int J Epidemiol 2013;42(2):412–421.

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