Here is a list of selected publications by the labs involved in the Proteomics French Infrastructure. You can also find publications of each lab on their web site :,400-.html


  The three members of ProFI contributed to

Vandenbrouck Y, Lane L, Carapito C, Duek P, Rondel K, Bruley C, Macron C, Gonzalez de Peredo A, Couté Y, Chaoui K, Com E, Gateau A, Hesse AM, Marcellin M, Méar L, Mouton-Barbosa E, Robin T, Burlet-Schiltz O, Cianferani S, Ferro M, Fréour T, Lindskog C, Garin J, Pineau C. Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update. J Proteome Res. 2016 Aug 23. PubMed PMID: 27444420.


In the context of the Chromosome-Centric Human Proteome Project (C-HPP), ProFI in collaboration with the C-HPP Swiss team and Pineau’s group performed an in-depth proteomics analysis of the human sperm proteome to identify testis-enriched missing proteins. Using protein extraction procedures and LC–MS/MS analysis, we detected 235 proteins (PE2–PE4) for which no previous evidence of protein expression was annotated (a.k.a. “missing proteins”). Through LC–MS/MS and LC–PRM analysis, data mining, and immunohistochemistry, we confirmed the expression of 206 missing proteins (PE2–PE4) in line with current HPP guidelines (version 2.0). Parallel reaction monitoring acquisition and synthetic heavy labeled peptides targeted “one-hit wonder” candidates also allowed to validate 24 with additional predicted and specifically targeted peptides. Evidence was found for 16 more missing proteins using immunohistochemistry on human testis sections. The expression pattern for some of these proteins was specific to the testis, and they could possibly be valuable markers with fertility assessment applications. 


 The three members of ProFI contributed to

Ramus C, Hovasse A, Marcellin M, Hesse AM, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, Garin J, Cianférani S, Ferro M, Van Dorssaeler A, Burlet-Schiltz O, Schaeffer C, Couté Y, Gonzalez de Peredo A. Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset. J Proteomics. 2016 Jan 30;132:51-62. PMID: 26585461



We provide here a controlled standard dataset and used it to evaluate the performances of several label-free bioinformatics tools (including MaxQuant, Skyline, MFPaQ, IRMa-hEIDI and Scaffold) in different workflows, for detection of variant proteins with different absolute expression levels and fold change values. This proteomic standard used is composed of an equimolar mixture of 48 human proteins (Sigma UPS1) spiked at different concentrations into a background of yeast cell lysate. The dataset we generated can be useful for tuning software tool parameters, and also testing new algorithms for label-free quantitative analysis, or for evaluation of downstream statistical methods.


Giai Gianetto Q, Combes F, Ramus C, Bruley C, Couté Y, Burger T. Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments. Proteomics. 2016 Jan;16(1):29-32. PMID: 26572953.


 In MS-based quantitative proteomics, the FDR control (i.e. the limitation of the number of proteins that are wrongly claimed as differentially abundant between several conditions) is a major postanalysis step. It is classically achieved thanks to a specific statistical procedure that computes the adjusted p-values of the putative differentially abundant proteins. Unfortunately, such adjustment is conservative only if the p-values are well-calibrated; the false discovery control being spuriously underestimated otherwise. However, well-calibration is a property that can be violated in some practical cases. To overcome this limitation, we propose a graphical method straightforwardly and visually assess the p-value well-calibration, as well as the R codes to embed it in any pipeline.



Plumel, M., M. Benhaim-Delarbre, M. Rompais, D. Thiersé, G. Sorci, A. van Dorsselaer, F. Criscuolo and F. Bertile (2015). “Differential proteomics reveals age-dependent liver oxidative costs of innate immune activation in mice.” J Proteomics. pii: S1874-3919(15)30126-3. : pii: S1874-3919(1815)30126-30123.

This paper illustrates the added value of using proteomics to answer evolutionary biology questions, and opens a promising way to study the interspecific variability in the rates of immune senescence. We explored liver protein profiles using 2D-DIGE-MS/MS in old vs. young mice of which the innate immunity was challenged using bacterial lipopolysaccharide. It was found that oxidative stress constitutes a cost of innate immune response in old mice, possibly contributing to senescence.





Lazar C, Gatto L, Ferro M, Bruley C, Burger T. Accounting for the multiple natures of missing values in label-free quantitative proteomics datasets to compare imputation strategies. J Proteome Res. 2016 Feb 23. PubMed PMID: 26906401.

Missing values are a genuine issue in label-free quantitative proteomics. With respect to this issue we believe that the question at stake is not to find the most accurate imputation method in general, but instead, the most appropriate one. This lead us to formulate few practical guidelines, regarding the choice and the application of an imputation method in a proteomics context.



Discovery and targeted proteomics on cutaneous biopsies infected by Borrelia to investigate Lyme disease. Schnell, G., Bœuf, A., Westermann, B., Jaulhac, B., Carapito, C., Boulanger, N., Ehret-Sabatier, L. Mol. Cell. Proteomics (2015) 14, 1254-1264.



Lyme disease is the most important vector-borne disease in the Northern hemisphere. The causative agents are bacteria belonging the Borrelia burgdorferi sensu lato group, transmitted by hard ticks Ixodes spp. Lyme disease represents a major public health challenge with insufficient means of reliable diagnosis. In this study we first set up a discovery approach on a murine model to identify Borrelia protein targets. Then we developed a selected reaction monitoring (SRM) assay and we demonstrated the feasibility of Borrelia detection in human skin samples. This paper shows that a targeted SRM approach is a promising tool for the early direct diagnosis of Lyme disease with high sensitivity.



The three members of ProFI contributed to

the Chromosome-Centric Human Proteome Project (C-HPP). In collaboration with Swiss group (Calipho Group, University of Geneva), we designed and applied a step-by-step strategy combining bioinformatics and MS-based experiments to identify and validate missing proteins based on database search results (85,326 .dat files) from a compendium of MS/MS datasets generated using 40 human cell line/tissue type/body fluid samples.


More details in:

Carapito C, Lane L, Benama M, Opsomer A, Mouton-Barbosa E, Garrigues L,Gonzalez de Peredo A, Burel A, Bruley C, Gateau A, Bouyssié D, Jaquinod M, Cianférani S, Burlet-Schiltz O, Van Dorsselaer A, Garin J, Vandenbrouck Y. Computational and mass spectrometry-based workflow for the discovery and validation of missing human proteins: application to chromosomes 2 and 14. J Proteome Res. 2015 July 1. PMID: 26132440.



proteasomeFabre B., Lambour T., Garrigues L, Amalric F., Vigneron N., Menneteau T., Stella A., Monsarrat B., Van den Eynde B., Burlet-Schiltz O.*, Bousquet-Dubouch M.P*. Deciphering preferential interactions within supramolecular protein complexes: the proteasome case. Mol. Syst. Biol. 2015, Jan 5; 11:771. In this paper, using a new method we developed based on the combination of affinity purification and protein correlation profiling associated with high resolution mass spectrometry, we comprehensively characterized proteasome heterogeneity and identified previously unknown preferential associations within proteasome sub-complexes. In particular, we showed for the first time that the two main proteasome sub-types, standard proteasome and immunoproteasome, interact with a different subset of important regulators. This method constitutes an innovative and powerful strategy that could be widened to unravel the dynamic and heterogeneous nature of many other biologically relevant molecular systems.



Juste C, Kreil DP, Beauvallet C, Guillot A, Vaca S, Carapito C, Mondot S, Sycacek P, Sokol H, Blon F, Lepercq F, Valot B, Carré W, Loux V, Pons N, David O, Schaeffer B, Lapage P, Martin P, Monnet V, Seksik P, Beaugerie L, Ehrlich SD, Gibrat JF, Van Dorsselaer A, Doré J. Bacterial protein signals are associated with Crohn’s disease, Gut. 2014 Oct; 63(10):1566-77. In this study, we demonstrated the potential of targeted selected reaction monitoring mass spectrometry (SRM-MS) to quantify and validate bacterial protein signals associated with Crohn’s disease in unfractionated gut microbiota, a sample of extreme complexity.



Tomizioli M, Lazar C, Brugière S, Burger T, Salvi D, Gatto L, Moyet L, Breckels LM, Hesse AM, Lilley KS, Seigneurin-Berny D, Finazzi G, Rolland N, Ferro M. Deciphering thylakoid sub-compartments using a mass spectrometry-based approach. Mol Cell Proteomics. 2014 Aug;13(8):2147-67. In this study, using quantitative proteomics, we performed a complete survey of the protein composition of thylakoid sub-compartments (grana and stroma-lamellae) using thylakoid membrane fractionations. About 300 thylakoid (or potentially thylakoid) proteins were shown to be enriched in either the BBY or the stroma-lamellae fractions. The originality of the present proteomic relies in the identification of photosynthetic proteins whose differential distribution in the thylakoid sub-compartments might explain already observed phenomenon such as LHCII docking.




urineLacroix C, Caubet C, Gonzalez-de-Peredo A, Breuil B, Bouyssie D, Stella A, Garrigues L, Le Gall C, Raevel A, Massoubre A, Klein J, Decramer S, Sabourdy F, Bandin F, Burlet-Schiltz O, Monsarrat B*, Schanstra JP*, Bascands JL*. Label-free quantitative urinary proteomics identifies the arginase pathway as a new player in congenital obstructive nephropathy. Mol Cell Proteomics. 2014, Dec; 13(12):3421-34. We have analyzed the urinary proteome of newborns (n=5/group) with obstructive nephropathy using label free quantitative nanoLC-MS/MS allowing the identification and quantification of 970 urinary proteins. We next focused on proteins exclusively regulated in severe obstructive nephropathy and identified Arginase 1 as a potential candidate molecule involved in the development of obstructive nephropathy, located at the crossroad of pro- and anti-fibrotic pathways. The reduced urinary abundance of Arginase 1 in obstructive nephropathy was verified in independent clinical samples using both Western blot and MRM analysis. The present study demonstrates the relevance of such a quantitative urinary proteomics approach with clinical samples for a better understanding of the pathophysiology and for the discovery of potential therapeutic targets.


Wasselin T, Zahn S, Mayo YL, Van Dorsselaer A, Raclot T, Bertile F. Exacerbated oxidative stress in the fasting liver according to fuel partitioning, Proteomics. 2014Aug; 14(16):1905-21. In this study, combination of transcriptomics and quantitative proteomics data was used to establish the hepatic metabolic network that responds to prolonged food deprivation in the Rat, and biochemical assays validated the omics-driven hypotheses.


VirusLegendre M, Bartoli J, Shmakova L, Jeudy S, Labadie K, Adrait A, Lescot M, Poirot O, Bertaux L, Bruley C, Couté Y, Rivkina E, Abergel C, Claverie JM. Thirty-thousand-year-old distant relative of giant icosahedral DNA viruses with a pandoravirus morphology. Proc Natl Acad Sci U S A. 2014 Mar 18;111(11):4274-9. In the present paper, we report the discovery of a third type of giant virus combining a large pandoravirus-like particle 1.5 μm in length with a surprisingly smaller 600 kb AT-rich genome, suggesting that pandoravirus-like particles may be associated with a variety of virus families more diverse than previously envisioned. This giant virus, named Pithovirus sibericum, was isolated from a >30,000-y-old radiocarbon-dated sample of the virome of Siberian permafrost. In this context proteomic analyses confirmed the occurrence of 159 proteins, two-thirds of them corresponding to unknown functions.