Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases
peer-reviewed · Cell Systems · 2025
| Date | 2025-11-01 |
| Type | peer-reviewed |
| Venue | Cell Systems |
| Publisher | Elsevier BV |
| Contribution | benchmark |
| DOI | 10.1016/j.cels.2025.101449 |
| Citations (OpenAlex) | 1 |
| 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.
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.