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

peer-reviewed · Cell Systems · 2025

peer-reviewed · Cell Systems · 2025. Maria Chernigovskaya et al. The circulating antibody (Ab) repertoire is crucial for immune protection, holding significant immunological…
Date 2025-11-01
Type peer-reviewed
Venue Cell Systems
Publisher Elsevier BV
Contribution benchmark
DOI 10.1016/j.cels.2025.101449
Citations (OpenAlex) 2
Venue 2-year citedness 6.21

Preprint version: Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2024-11-12, bioRxiv)

Abstract

The circulating antibody (Ab) repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is widely used for profiling the sequence diversity of circulating Abs (Ab repertoire sequencing [Ab-seq]), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal Ab spike-ins in 70 combinations of concentration and oligoclonality, with and without polyclonal serum immunoglobulin G (IgG) background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 liquid chromatography-tandem MS (LC-MS/MS) runs. We quantified the dependence of Ab-seq identification on Ab sequence, concentration, protease, presence of background IgGs, and bioinformatics methods. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced Ab identification. De novo sequencing performed similarly to database-dependent methods at higher Ab concentrations, but de novo Ab reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based Ab profiling. A record of this paper’s transparent peer review process is included in the supplemental information.

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 (27)

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