Statistical benchmark of Label-free DIA quantification using the generic integration algorithm with iSanXoT

iSanXoT(1) is a standalone application that processes relative abundances between MS signals and integrates them sequentially to upper levels using the Generic Integration Algorithm (GIA)(2,3), which has been benchmarked for label-based and label-free quantitative proteomics analysis using Data Dependent Acquisition (DDA). Lately, the proteomics community is pivoting towards the use of Data Independent Acquisition (DIA) for label-free quantification analysis due to its reproducibility and consistency. The performance of DIA for protein quantification depends on the quality of the MS-data and the software used for signal extraction, identification, quantification and integration of quantitative data. This is especially relevant for latest-generation mass spectrometers, such as the Thermo Orbitrap Astral, that offer unprecedented peptide and protein coverage using DIA. Therefore, determination of the quality of quantification is essential to determine which peptides actually provide meaningful information that can be used in quantitative experiments.

iSanXoT allows user-defined integrations of any quantification level (i.e. features, peptides, modified peptides, proteoforms, protein groups, functional categories) while performing differential analysis among experimental conditions defined in the workflow. The GIA algorithm implemented in iSanXoT accurately models the error structure of the data at any level of quantitative information, allowing its sequential integration using error propagation theory. GIA assigns a statistical weight to all the elements, obviating the need to apply quality filters and allowing a full control of quantitative errors. Hence, iSanXoT provides an optimum environment to test the performance of DIA-based quantification. Here, we have benchmarked the integration to protein level using iSanXot versus the integration to protein performed directly in Spectronaut (MaxLFQ or Quant2.0), and DIANN (legacy or quantUMS). For that purpose, we have used mixed-species mixture datasets from previous publications and different MS-platforms: Orbitrap-Exploris(4), TripleTOF 5600(5), timsTOF Pro(6) and Orbitrap-Astral(4).

Overall, this work highlights the advantages of iSanXot to process quantitative data obtained using DIA, independently on the MS-platform used.

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