ArticlePloS one2020
New label-free methods for protein relative quantification applied to the investigation of an animal model of Huntington Disease.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- A new versatile peroxidase with extremophilic traits over-produced in MicroTom cell cultures.Scientific reports · 2023Article
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- Salivary proteins offer insights into keratinocyte death during aphthous stomatitis. A case-crossover study.BMC oral health · 2023Article
- Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice.Proteomes · 2023Review
- Preliminary evaluation of the proteomic profiling in the hippocampus of aged grazing cattle.Frontiers in aging neuroscience · 2023Article
- Proteomic signature profiling in the cortex of dairy cattle unravels the physiology of brain aging.Frontiers in aging neuroscience · 2023Article
- Wheat Proteomics for Abiotic Stress Tolerance and Root System Architecture: Current Status and Future Prospects.Proteomes · 2022Review
- The endogenous HBZ interactome in ATL leukemic cells reveals an unprecedented complexity of host interacting partners involved in RNA splicing.Frontiers in immunology · 2022Article
- What Antarctic Plants Can Tell Us about Climate Changes: Temperature as a Driver for Metabolic Reprogramming.Biomolecules · 2021Article
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9 authors.
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Abstract
Spectral Counts approaches (SpCs) are largely employed for the comparison of protein expression profiles in label-free (LF) differential proteomics applications. Similarly, to other comparative methods, also SpCs based approaches require a normalization procedure before Fold Changes (FC) calculation. Here, we propose new Complexity Based Normalization (CBN) methods that introduced a variable adjustment factor (f), related to the complexity of the sample, both in terms of total number of identified proteins (CBN(P)) and as total number of spectral counts (CBN(S)). Both these new methods were compared with the Normalized Spectral Abundance Factor (NSAF) and the Spectral Counts log Ratio (Rsc), by using standard protein mixtures. Finally, to test the robustness and the effectiveness of the CBNs methods, they were employed for the comparative analysis of cortical protein extract from zQ175 mouse brains, model of Huntington Disease (HD), and control animals (raw data available via ProteomeXchange with identifier PXD017471). LF data were also validated by western blot and MRM based experiments. On standard mixtures, both CBN methods showed an excellent behavior in terms of reproducibility and coefficients of variation (CVs) in comparison to the other SpCs approaches. Overall, the CBN(P) method was demonstrated to be the most reliable and sensitive in detecting small differences in protein amounts when applied to biological samples.
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