Benchmarking quantitative label-free LC–MS data processing workflows using a complex spiked proteomic standard dataset

Ramus, Claire; Hovasse, Agnès; Marcellin, Marlène; Hesse, Anne-Marie; Mouton-Barbosa, Emmanuelle; Bouyssié, David; Vaca, Sebastian; Carapito, Christine; Chaoui, Karima; Bruley, Christophe; Garin, Jérôme; Cianférani, Sarah; Ferro, Myriam; Dorssaeler, Alain Van; Burlet‐Schiltz, Odile; Schaeffer, Christine; Couté, Yohann; Peredo, Anne Gonzalez de

doi:10.1016/j.jprot.2015.11.011

Cited by 75 publications

(90 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3). Results from both experiments were complementary and showed that LC-MS ion intensity protein abundance measurements outperformed spectral counting in terms of accuracy and precision, and supported recent studies comparing both label-free methods in the estimation of absolute protein abundances [5,7,[27][28][29]. Moreover, although both labelfree quantification methods responded linearly to increasing protein concentration, protein quantification measurements changed at almost the same rate for LC-MS ion intensity quantification but not spectral counting, suggesting that LC-MS ion intensity quantification is fairly robust to differences in physicochemical properties of peptides, number of peptides per proteins, and ionisation efficiencies of peptides.…”

Section: Discussionsupporting

confidence: 72%

Improved LC-MS chromatographic alignment increases the accuracy of label-free quantitative proteomics: Comparison of spectral counting versus ion intensity-based proteomic quantification strategies

Humphries²,

Selley³

et al. 2017

Preprint

View full text Add to dashboard Cite

The ability to provide an unbiased qualitative and quantitative description of the global changes to proteins in a cell or an organism would permit the systems-wide study of complex biological systems. Label-free quantitative shotgun proteomic strategies (including LC-MS ion intensity quantification and spectral counting) are attractive because of their relatively low cost, ease of implementation, and the lack of multiplexing restrictions when comparing multiple samples. Owing to improvements in the resolution and sensitivity of mass spectrometers, and the availability of analytical software packages, protein quantification by LC-MS ion intensity has increased in popularity. Here, we have addressed the importance of chromatographic alignment on protein quantification, and then assessed how spectral counting compares to ion intensity-based proteomic quantification. Using a spiked-in protein strategy, we analysed two situations that commonly arise in the application of proteomics to cell biology: (i) samples with a small number of proteins of differential abundance in a larger non-changing background, and (ii) samples with a larger number of proteins of differential abundance. To perform these assessments on biologically relevant samples, we used isolated integrin adhesion complexes (IACs). Technical replicate analysis of isolated IACs resulted in a range of alignment scores using the Progenesis QI software package and demonstrated that higher LC-MS chromatographic alignment scores increased the precision of protein quantification. Furthermore, implementation of a simple sample batch-running strategy enabled good chromatographic alignment for hundreds of samples over multiple batches.Finally, we applied the sample batch-running strategy and compared quantification by LC-MS ion intensity to spectral counting and found that quantification by LC-MS ion intensity was more accurate and precise. In summary, these results demonstrate that chromatographic alignment is important for precise and accurate protein quantification based on LC-MS ion intensity and accordingly we present a simple sample re-ordering strategy to facilitate improved alignment. These findings are not only relevant to labelfree quantification using Progenesis QI but may be useful to the wide range of MS-based quantification strategies that rely on chromatographic alignment.

show abstract

Section: Discussionsupporting

confidence: 72%

Improved LC-MS chromatographic alignment increases the accuracy of label-free quantitative proteomics: Comparison of spectral counting versus ion intensity-based proteomic quantification strategies

Humphries²,

Selley³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…However, we have provided a separate table (Additional file 2: Table S2) that filters differentially expressed proteins by both Welch’s t test as well as z -score (see the “Methods” section). This type of filtering approach has been shown to be less stringent than multiple testing correction methods (e.g., Bonferroni or Benjamini-Hochberg) while still maintaining adequate FDR and sensitivity [43]. Several differentially expressed proteins (out of 156 total differentially expressed proteins from the second filtering approach) related to microglial function are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Proteomic analysis of aged microglia: shifts in transcription, bioenergetics, and nutrient response

Flowers

Bell-Temin

Jalloh

et al. 2017

J Neuroinflammation

View full text Add to dashboard Cite

BackgroundAge is the primary risk factor for many diseases. As such, age is a critical co-factor for examination in order to understand the progression and potential intervention in disease progression. Studies examining both the phenotype and transcriptome of aged microglia demonstrated a propensity for the development of a pro-inflammatory phenotype. Less well studied is the concomitant blunting of anti-inflammatory aspects of microglial function with age which also impact plasticity and repair in the CNS.MethodsThis study utilizes mass spectrometry-based proteomics to compare primary microglia from young and aged animals.ResultsThis study revealed alterations in three clusters of inter-related proteins. The three pathways were inflammatory signaling, mitochondrial function, and cellular metabolism. Analysis of these clusters identified the protein rapamycin-insensitive companion of mTOR (RICTOR), a component of the mTORC2 complex, as a novel upstream regulator of several biological functions that are altered with age and potentially linked to phenotype development. A decrease in mTORC2-dependent AKT S473 phosphorylation, as assessed by insulin growth factor (IGF) treatment, was observed in aged microglia. This novel finding was confirmed by genetic manipulation of the microglial cell line. BV2 cells with diminished RICTOR displayed a phenotype that was strikingly similar to that of aged microglia. This finding is particularly relevant as the mTOR pathway already has a number of pharmacological modulators used clinically.ConclusionsThe results suggest that microglia from aged mice show changes in cellular metabolism and energy regulation that might underlie the alterations in inflammatory signaling. Modulation of one pathway identified in our bioinformatic analysis, RICTOR, may provide an avenue by which deleterious aspects of the aging microglia can be attenuated. If successful, this could mean potentially delaying or diminishing the progress of diseases for which progressive inflammation is involved.Electronic supplementary materialThe online version of this article (doi:10.1186/s12974-017-0840-7) contains supplementary material, which is available to authorized users.

show abstract

“…Meta-omic count data were assumed to be modeled by a beta binomial distribution based on previous studies performed on count data obtained through discovery mass spectrometry proteomics (Ramus et al, 2016) and nucleic acid sequencing techniques (Smith and Birtwistle, 2016). The p -value list provided by the ibb test was subsequently subjected to a multiple testing adjustment based on a sequential goodness of fit (SGoF) metatest (Carvajal-Rodriguez et al, 2009) using the SGoF+ software (v.3.8) with default parameters (Carvajal-Rodriguez and de Una-Alvarez, 2011).…”

Section: Methodsmentioning

confidence: 99%

Metaproteogenomics Reveals Taxonomic and Functional Changes between Cecal and Fecal Microbiota in Mouse

et al. 2017

View full text Add to dashboard Cite

Previous studies on mouse models report that cecal and fecal microbial communities may differ in the taxonomic structure, but little is known about their respective functional activities. Here, we employed a metaproteogenomic approach, including 16S rRNA gene sequencing, shotgun metagenomics and shotgun metaproteomics, to analyze the microbiota of paired mouse cecal contents (CCs) and feces, with the aim of identifying changes in taxon-specific functions. As a result, Gram-positive anaerobes were observed as considerably higher in CCs, while several key enzymes, involved in oxalate degradation, glutamate/glutamine metabolism, and redox homeostasis, and most actively expressed by Bacteroidetes, were clearly more represented in feces. On the whole, taxon and function abundance appeared to vary consistently with environmental changes expected to occur throughout the transit from the cecum to outside the intestine, especially when considering metaproteomic data. The results of this study indicate that functional and metabolic differences exist between CC and stool samples, paving the way to further metaproteogenomic investigations aimed at elucidating the functional dynamics of the intestinal microbiota.

show abstract

Benchmarking quantitative label-free LC–MS data processing workflows using a complex spiked proteomic standard dataset

Cited by 75 publications

References 41 publications

Improved LC-MS chromatographic alignment increases the accuracy of label-free quantitative proteomics: Comparison of spectral counting versus ion intensity-based proteomic quantification strategies

Improved LC-MS chromatographic alignment increases the accuracy of label-free quantitative proteomics: Comparison of spectral counting versus ion intensity-based proteomic quantification strategies

Proteomic analysis of aged microglia: shifts in transcription, bioenergetics, and nutrient response

Metaproteogenomics Reveals Taxonomic and Functional Changes between Cecal and Fecal Microbiota in Mouse

Contact Info

Product

Resources

About