Florence Roux-Dalvai   Florence Roux-Dalvai is senior scientist at the CHU de Québec - Université Laval (Québec, Canada) in the proteomics and computational biology laboratory of Pr. Arnaud Droit. She received her M.Sc. in Structural Biochemistry at the University of Strasbourg (France) and was introduced to mass spectrometry for protein analysis under the supervision of Dr. Jérôme Garin at the CEA (Grenoble, France). She then worked at the Novartis Friedrich Miescher Institute (Basel, Switzerland) studying ErbB receptor signaling pathways in breast cancer using proteomics before to join the CNRS (Toulouse, France) where she developed new strategies for quantitative analysis of deep proteomes on Orbitrap instruments. Since 2014, at Université Laval, she coordinates development projects for the large-scale analysis of clinical samples and for the detection of microorganisms in biological fluids by combining the latest analysis strategies in proteomics (Data Independent Acquisition) and in artificial intelligence (Machine Learning).

Comparative analysis of library-based and library-free DIA strategies using Skyline software

Data independent acquisition (DIA) analysis has become a strategy of choice for the analysis of complex proteomes and a plethora of methods are now available in the literature. However, there is no consensus on the best acquisition parameters to use, whether a spectral library is needed, and which processing software is most efficient. In the most comprehensive comparative study of DIA pipelines ever published (Gotti et al. J.Proteome Res., 2021), we used a complex proteomic standard (E.Coli background + UPS1 Sigma) with 4 DIA acquisition methods on an Orbitrap Fusion instrument to benchmark 6 different processing tools. Read More
For each of them, we reported the number of protein and peptide identifications, linearity and reproducibility of quantification, and sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations.We extended our work using Skyline with a newly implemented library-free functionality. In this option, DIA-Umpire is used to generate a pseudo-MGF file that can be searched with MS Amanda or MSFragger database search engines, all these steps being fully integrated in Skyline. Thus, our complex proteomic standard was used to compare library-based and library-free DIA analysis in Skyline. Finally, we applied the Skyline library-free DIA pipeline to a large-scale study of cerebrospinal fluid with the objective to define new biomarkers that could improve the diagnosis and prognosis of Alzheimer’s disease.