Missing Value Monitoring Enhances the Robustness in Proteomics Quantitation

Matafora, Vittoria; Corno, Andrea; Ciliberto, Andrea; Bachi, Ángela

doi:10.1021/acs.jproteome.6b01056

Cited by 15 publications

(10 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mass spectrometer was operated in DDA mode as described previously (Matafora et al, 2017): dynamic exclusion enabled (exclusion duration = 15 s), MS1 resolution = 70,000, MS1 automatic gain control target = 3 × 106, MS1 maximum fill time = 60 ms, MS2 resolution = 17,500, MS2 automatic gain control target = 1 × 105, MS2 maximum fill time = 60 ms, and MS2 normalized collision energy = 25. For each cycle, one full MS1 scan range = 300-1,650 m/z was followed by 12 MS2 scans using an isolation window of 2.0 m/z.…”

Section: Secretome Analysismentioning

confidence: 99%

Amyloid aggregates accumulate in melanoma metastasis modulating YAP activity

et al. 2020

Self Cite

View full text Add to dashboard Cite

Melanoma progression is generally associated with increased transcriptional activity mediated by the Yes‐associated protein (YAP). Mechanical signals from the extracellular matrix are sensed by YAP, which then activates the expression of proliferative genes, promoting melanoma progression and drug resistance. Which extracellular signals induce mechanotransduction, and how this is mediated, is not completely understood. Here, using secretome analyses, we reveal the extracellular accumulation of amyloidogenic proteins, i.e. premelanosome protein (PMEL), in metastatic melanoma, together with proteins that assist amyloid maturation into fibrils. We also confirm the accumulation of amyloid‐like aggregates, similar to those detected in Alzheimer disease, in metastatic cell lines, as well as in human melanoma biopsies. Mechanistically, beta‐secretase 2 (BACE2) regulates the maturation of these aggregates, which in turn induce YAP activity. We also demonstrate that recombinant PMEL fibrils are sufficient to induce mechanotransduction, triggering YAP signaling. Finally, we demonstrate that BACE inhibition affects cell proliferation and increases drug sensitivity, highlighting the importance of amyloids for melanoma survival, and the use of beta‐secretase inhibitors as potential therapeutic approach for metastatic melanoma.

show abstract

Section: Secretome Analysismentioning

confidence: 99%

Amyloid aggregates accumulate in melanoma metastasis modulating YAP activity

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…As protein quantities are inferred from the quantities of measured peptides or even fragment ions, one needs to decide at which level to correct the data. Second, it is known that missing values can be associated with technical factors (Karpievitch et al , 2012 ; Matafora et al , 2017 ). Finally, when dealing with experiments with large sample numbers, typically in the order of hundreds, one needs to account for MS signal drift.…”

Section: Introductionmentioning

confidence: 99%

Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial

Čuklina

Lee

Williams

et al. 2021

Molecular Systems Biology

106

View full text Add to dashboard Cite

Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

show abstract

“…Differences in lipid IDs among each run, in both micro-and nano-flow, are due to the presence of missing values, which are a common problem in omics approaches. Indeed, missing values occur in datasets for several reasons: lipid amount below the limit of detection; stochastic nature of precursor selection in data dependent acquisition methods; missing peak picking by the software and biological variability across different samples [26]. In line with the total identified lipids, also core lipids across replicates are doubled in nano-flow compared the micro-flow datasets (Figure S7B).…”

Section: Lipidomics Analysis Of Mammalian Cells 221 Semi-targeted Lipidomics Analysismentioning

confidence: 90%

Opti-nQL: An Optimized, Versatile and Sensitive Nano-LC Method for MS-Based Lipidomics Analysis

Cattaneo¹,

Martano

Restuccia

et al. 2021

Metabolites

Self Cite

View full text Add to dashboard Cite

Lipidomics is the comprehensive analysis of lipids in a given biological system. This investigation is often limited by the low amount and high complexity of biological samples, therefore highly sensitive lipidomics methods are required. Nanoflow-LC/MS offers extremely high sensitivity; however, it is challenging as a more demanding maintenance is often needed compared to conventional microflow-LC approaches. Here, we developed a sensitive and reproducible lipidomics LC method, termed Opti-nQL, which can be applied to any biological system. Opti-nQL has been validated with cellular lipid extracts of human and mouse origin and with different lipid extraction methods. Among the resulting 4000 detected features, 700 and even more unique lipid molecular species have been identified covering 16 lipid sub-classes, while 400 lipids were uniquely structure defined by MS/MS. These results were obtained by analyzing an amount of lipids extract equivalent to 40 ng of proteins, being highly suitable for low abundant samples. MS analysis showed that theOpti-nQL method increases the number of identified lipids, which is evidenced by injecting 20 times less material than in microflow based chromatography, being more reproducible and accurate thus enhancing robustness of lipidomics analysis.

show abstract

Missing Value Monitoring Enhances the Robustness in Proteomics Quantitation

Cited by 15 publications

References 31 publications

Amyloid aggregates accumulate in melanoma metastasis modulating YAP activity

Amyloid aggregates accumulate in melanoma metastasis modulating YAP activity

Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial

Opti-nQL: An Optimized, Versatile and Sensitive Nano-LC Method for MS-Based Lipidomics Analysis

Contact Info

Product

Resources

About