Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases

preprint · bioRxiv · 2024

preprint · bioRxiv · 2024. Maria Chernigovskaya et al. The circulating antibody repertoire is crucial for immune protection, holding significant immunological and…
Date 2024-11-12
Type preprint
Venue bioRxiv
Publisher openRxiv
Contribution benchmark
DOI 10.1101/2024.11.11.622451
Citations (OpenAlex) 2

Peer-reviewed version: Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2025-11-01, Cell Systems)

Abstract

The circulating antibody repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is the most widely used proteomics technique for profiling the sequence diversity of circulating antibodies (Ab-seq), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal antibodies with known protein sequences in 70 different combinations of concentration and oligoclonality, both with and without polyclonal serum IgG background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 LC-MS/MS runs. We quantified the dependence of MS-based Ab-seq identification on antibody sequence, concentration, protease, background signal diversity, and bioinformatics setups. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced antibody identification. De novo peptide sequencing showed similar performance to database-dependent methods for higher antibody concentrations, but de novo antibody reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based antibody profiling.

Authors

  1. Maria Chernigovskaya · Oslo University Hospital, University of Oslo
  2. Khang Lê Quý · Oslo University Hospital, University of Oslo
  3. Maria Stensland · Oslo University Hospital, University of Oslo
  4. Sachin Singh · Oslo University Hospital, University of Oslo
  5. Rowan Nelson · University of Washington
  6. Melih Yilmaz · University of Washington
  7. Konstantinos Kalogeropoulos · Delft University of Technology, Kavli Institute of Nanoscience, Technical University of Denmark
  8. Pavel Sinitcyn · Morgridge Institute for Research, Utrecht University
  9. Anand Patel · Abterra Biosciences (United States)
  10. Natalie Castellana · Abterra Biosciences (United States), Mapp Biopharmaceutical (United States), University of California San Diego
  11. Stefano Bonissone · Abterra Biosciences (United States)
  12. Stian Foss · Oslo University Hospital, University of Oslo
  13. Jan Terje Andersen · Oslo University Hospital, University of Oslo
  14. Geir Kjetil Sandve · University of Oslo
  15. Timothy P. Jenkins · Technical University of Denmark
  16. William Stafford Noble · University of Washington
  17. Tuula A. Nyman · Oslo University Hospital, University of Oslo
  18. Igor Snapkow · Norwegian Institute of Public Health
  19. Victor Greiff · Oslo University Hospital, University of Oslo

Methods and tools

  • Ab-seq benchmark (MS antibody sequencing): Benchmarks bottom-up MS antibody sequencing over 3,360 runs of six spiked monoclonals, comparing proteases and database versus de novo tools. De novo matched database methods at high concentration, but full de novo antibody reconstruction remained hard.

Methods it uses

  • ALPS: Assembles de novo sequenced peptides and their per-residue confidence scores into a de Bruijn graph to reconstruct complete monoclonal antibody heavy and light chains without a template.
  • Casanovo: First Transformer
  • PEAKS: Commercial DP-based de novo

Data deposited

  • Benchmarking of mass spectrometry-based antibody repertoire profiling reveals extensive systematic biases (as deposited) · PXD055846

Cites (22)

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