ArticleMolecular & cellular proteomics : MCP2026
Mass Spectrometry-Based De Novo Sequencing of N-Glycosylated Light Chains: Linking Urinary Proteoforms to In Situ Renal Amyloid Deposits.
Article in Molecular & cellular proteomics : MCP, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systemic immunoglobulin light-chain (AL) amyloidosis is characterized by clonal immunoglobulin-secreting cells that produce a monoclonal light chain prone to misfolding and amyloid fibril formation in tissues. Understanding its molecular basis requires accurate full-length sequencing of amyloidogenic light chains and linkage of circulating light chains to renal deposits. Achieving this is technically challenging because the low abundance and N-glycosylation of amyloidogenic light chains can complicate protein purification and peptide-level sequence analysis. To address these challenges, we implemented a robust analytical pipeline that integrates intact-mass measurement by Q-TOF MS before and after deglycosylation with multi-protease digestion and de novo peptide sequencing to systematically characterize urinary N-glycosylated light chains. Mass shifts observed before and after deglycosylation supported the presence of N-glycosylation, whereas deglycosylated intact masses were used to constrain full-length sequence assembly. The assembled urinary light-chain sequences were subsequently compared with matched renal amyloid proteomes isolated by laser microdissection and analyzed by bottom-up liquid chromatography-tandem mass spectrometry (LC-MS/MS). Enzymatic deglycosylation reduced glycan-induced spectral interference. Eight monoclonal light-chain sequences were assembled from urinary light chains, including five derived from the immunoglobulin kappa variable 1 (IGKV1), two from the immunoglobulin lambda variable 2 (IGLV2), and one from IGKV4. The close agreement between theoretical and experimental intact masses confirmed the accuracy and completeness of sequence assembly. In each renal amyloid proteome, the corresponding urine-derived sequence had the highest score among light-chain identifications of the patient's clinically determined isotype (κ or λ) and showed 87.6% to 100% variable-region peptide coverage. Together, these findings support urinary N-glycosylated monoclonal light chains as the precursor proteins of the corresponding renal amyloid fibrils. In conclusion, we have demonstrated a robust workflow that applies established de novo peptide sequencing to characterize urinary N-glycosylated light chains and trace the corresponding sequences in renal amyloid deposits, with potential applications across light chain-related diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.