Clinical chemistry最新文献

Background: Serum free light chains (FLCs) are an essential clinical biomarker for the diagnosis and monitoring of patients with plasma cell neoplasms. The current widely used immunoassay methods quantify total serum FLCs, which include monoclonal FLCs as well as FLCs in the polyclonal background. Patients with chronic diseases, inflammatory disorders, or renal dysfunction can have elevated total FLCs that lead to ambiguous results. These patients may benefit from a direct measurement of monoclonal FLCs. The purpose of this study was to develop a method that couples on-probe extraction (OPEX) with liquid chromatography-high-resolution mass spectrometry (LC-HR-MS), abbreviated to OPEX-MS, to directly determine the clonality of FLCs.

Methods: OPEX immunocapture was performed using microprobes loaded with anti-kappa or anti-lambda light chain antibodies. Captured proteins were separated by reversed-phase LC and analyzed by HR-MS.

Results: Four cohorts of samples from unique patients were tested based on immunoassay FLC results. The LC-HR-MS analysis in the OPEX-MS method provides both a unique retention time along with deconvoluted masses of FLC monomers and dimers for each clone. The study found that 16 out of 49 (33%) kappa FLC elevated samples as well as 83 out of 100 (83%) dual kappa and lambda FLC elevated samples did not have monoclonal FLCs, which is consistent with the knowledge that there is often no clonal population in samples with mildly elevated FLC immunoassay results.

Conclusions: The OPEX-MS method can serve as a complementary approach to directly determine clonality in patients with difficult-to-interpret FLC immunoassay results.

Background: Untargeted metabolomics has shown promise in expanding screening and diagnostic capabilities for inborn errors of metabolism (IEMs). However, inter-batch variability remains a major barrier to its implementation in the clinical laboratory, despite attempts to address this through normalization techniques. We have developed a sustainable, matrix-matched reference material (RM) using the iterative batch averaging method (IBAT) to correct inter-batch variability in liquid chromatography-high-resolution mass spectrometry-based untargeted metabolomics for IEM screening.

Methods: The RM was created using pooled batches of remnant plasma specimens. The batch size, number of batch iterations per RM, and stability compared to a conventional pool of specimens were determined. The effectiveness of the RM for correcting inter-batch variability in routine screening was evaluated using plasma collected from a cohort of phenylketonuria (PKU) patients.

Results: The RM exhibited lower metabolite variability between iterations over time compared to metabolites from individual batches or individual specimens used for its creation. In addition, the mean variation across amino acid (n = 19) concentrations over 12 weeks was lower for the RM (CVtotal = 8.8%; range 4.7%-25.3%) compared to the specimen pool (CVtotal = 24.6%; range 9.0%-108.3%). When utilized in IEM screening, RM normalization minimized unwanted inter-batch variation and enabled the correct classification of 30 PKU patients analyzed 1 month apart from 146 non-PKU controls.

Conclusions: Our RM minimizes inter-batch variability in untargeted metabolomics and demonstrated its potential for routine IEM screening in a cohort of PKU patients. It provides a practical and sustainable solution for data normalization in untargeted metabolomics for clinical laboratories.