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

Publication date

2024-11-12

Authors

Chernigovskaya, Maria
Quý, Khang Lê
Stensland, Maria
Singh, Sachin
Nelson, Rowan
Yilmaz, Melih
Kalogeropoulos, Konstantinos
Sinitcyn, PavelORCID 0000-0002-2653-1702
Patel, Anand
Castellana, Natalie

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Document Type

/dk/atira/pure/researchoutput/researchoutputtypes/workingpaper/preprint
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cc_by_nc_nd

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.

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Citation

Chernigovskaya, M, Quý, K L, Stensland, M, Singh, S, Nelson, R, Yilmaz, M, Kalogeropoulos, K, Sinitcyn, P, Patel, A, Castellana, N, Bonissone, S, Foss, S, Andersen, J T, Sandve, G K, Jenkins, T P, Noble, W S, Nyman, T A, Snapkow, I & Greiff, V 2024 'Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases' bioRxiv. https://doi.org/10.1101/2024.11.11.622451