FAIMS-GPF XL-MS: crosslinking-mass spectrometry based on gas-phase fractionation

Abstract

Protein-protein interactions (PPIs) underpin nearly all cellular processes; therefore, mapping PPI and protein-protein association networks is critical for understanding how biological systems function in health and disease. However, many functionally relevant PPIs are transient and mediated by weak affinity interactions, making them challenging to detect. Using chemical cross-linking together with mass spectrometry (XL-MS) has emerged as an important category of methods for mapping PPIs, including those that are more dynamic and transient. However, XL-MS workflows face limitations which hinder their routine use, especially in proteomic platforms. Here, to address these limitations, we develop a XL-MS workflow based on gas-phase fractionation (GPF) using high-field asymmetric waveform ion mobility spectrometry (FAIMS). Our optimized FAIMS-GPF XL-MS protocols exhibit improved performance when handling both low-complexity samples, such as purified Cdk7-Activating Kinase (CAK) heterotrimer, and high-complexity cross-linked HeLa lysates. In HeLa lysate, we identify 1269 cross-links accounting for 213 PPIs without any in-solution fractionation, starting from less than 20 µg of sample. The method also proves effective for analyzing low-abundance, high-complexity samples such as spliceosomes. Here we show that FAIMS-GPF enhances the detection of cross-linked peptides and improves PPI coverage, thus offering a scalable, high-sensitivity solution for XL-MS integration with structural biology and functional proteomics applications.

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Cite

BibTeX RIS

BibTeX

@article{gizardinfredon2026faimsgpf,
  title = {{FAIMS-GPF XL-MS: crosslinking-mass spectrometry based on gas-phase fractionation}},
  author = {Hugo Gizardin-Fredon and Salvatore Terrosu and Simon Pichard and Zinedine Loukili and Dursun Korkut and Cathy Braun and Arnaud Poterszman and Clément Charenton and Sarah Cianférani},
  journal = {Nature Communications},
  year = {2026},
  month = jul,
  doi = {10.1038/s41467-026-75736-9},
  url = {https://hal.science/hal-05781826v1},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - FAIMS-GPF XL-MS: crosslinking-mass spectrometry based on gas-phase fractionation
AU  - Hugo Gizardin-Fredon
AU  - Salvatore Terrosu
AU  - Simon Pichard
AU  - Zinedine Loukili
AU  - Dursun Korkut
AU  - Cathy Braun
AU  - Arnaud Poterszman
AU  - Clément Charenton
AU  - Sarah Cianférani
PY  - 2026
DA  - 2026/07/23
JO  - Nature Communications
DO  - 10.1038/s41467-026-75736-9
UR  - https://hal.science/hal-05781826v1
AB  - Protein-protein interactions (PPIs) underpin nearly all cellular processes; therefore, mapping PPI and protein-protein association networks is critical for understanding how biological systems function in health and disease. However, many functionally relevant PPIs are transient and mediated by weak affinity interactions, making them challenging to detect. Using chemical cross-linking together with mass spectrometry (XL-MS) has emerged as an important category of methods for mapping PPIs, including those that are more dynamic and transient. However, XL-MS workflows face limitations which hinder their routine use, especially in proteomic platforms. Here, to address these limitations, we develop a XL-MS workflow based on gas-phase fractionation (GPF) using high-field asymmetric waveform ion mobility spectrometry (FAIMS). Our optimized FAIMS-GPF XL-MS protocols exhibit improved performance when handling both low-complexity samples, such as purified Cdk7-Activating Kinase (CAK) heterotrimer, and high-complexity cross-linked HeLa lysates. In HeLa lysate, we identify 1269 cross-links accounting for 213 PPIs without any in-solution fractionation, starting from less than 20 µg of sample. The method also proves effective for analyzing low-abundance, high-complexity samples such as spliceosomes. Here we show that FAIMS-GPF enhances the detection of cross-linked peptides and improves PPI coverage, thus offering a scalable, high-sensitivity solution for XL-MS integration with structural biology and functional proteomics applications.
SN  - 2041-1723
SN  - 2041-1723
ER  -

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