What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows

O’Rourke, Matthew B.; Town, Stephanie; Dalla, Penelope V.; Bicknell, Fiona; Belic, Naomi Koh; Violi, Jake P.; Steele, Joel R.; Padula, Matthew P.

doi:10.3390/proteomes7030029

Cited by 29 publications

(29 citation statements)

References 96 publications

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“…3c,d ). As protein composition of nasopharyngeal samples may vary between individuals, cytokine measurements were normalized to total protein concentrations 19 . We assessed whether changes in total protein or mucus content might account for the observed differences in nasopharyngeal cytokines.…”

Section: Resultsmentioning

confidence: 99%

Distinct systemic and mucosal immune responses during acute SARS-CoV-2 infection

et al. 2021

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Coordinated local mucosal and systemic immune responses following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection either protect against coronavirus disease 2019 (COVID-19) pathologies or fail, leading to severe clinical outcomes. To understand this process, we performed an integrated analysis of SARS-CoV-2 spike-specific antibodies, cytokines, viral load and bacterial communities in paired nasopharyngeal swabs and plasma samples from a cohort of clinically distinct patients with COVID-19 during acute infection. Plasma viral load was associated with systemic inflammatory cytokines that were elevated in severe COVID-19, and also with spike-specific neutralizing antibodies. By contrast, nasopharyngeal viral load correlated with SARS-CoV-2 humoral responses but inversely with interferon responses, the latter associating with protective microbial communities. Potential pathogenic microorganisms, often implicated in secondary respiratory infections, were associated with mucosal inflammation and elevated in severe COVID-19. Our results demonstrate distinct tissue compartmentalization of SARS-CoV-2 immune responses and highlight a role for the nasopharyngeal microbiome in regulating local and systemic immunity that determines COVID-19 clinical outcomes.

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Section: Resultsmentioning

confidence: 99%

Distinct systemic and mucosal immune responses during acute SARS-CoV-2 infection

et al. 2021

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“…Before the analysis, the total protein concentration was quantified as 3.56 ± 0.24 mg/g wet weight (± SE) for dormant cells and 6.01 ± 0.14 mg/g wet weight (± SE) for active cells. An equal amount of the total proteins was used for further trypsinolysis and LC-MS/MS profiling (19). LC-MS profiling resulted in the detection of 1379 proteins (whose encoding genes Rv were further used as identifiers), covering 35% of the coding M. tuberculosis H37Rv genome.…”

Section: Dormant Mycobacterial Cells Subjected To the Proteomic Analysis And The Results Of Proteomic Profilingmentioning

confidence: 99%

“…Before the analysis, the total protein concentration was quantified and an equal amount of protein extracts was used for further trypsinolysis and profiling – pre-instrumental normalization on total protein concentration was carried out (19). To make the profiles comparable for the subsequent statistical analysis, post-instrumental pre-treatment methods were carried out: to deal with heteroscedasticity and to make skewed distributions symmetric, log 2 transformation was performed.…”

Section: Methodsmentioning

confidence: 99%

Shotgun proteomic profiling of dormant, ‘non-culturable’Mycobacterium tuberculosis

Nikitushkin

Shleeva

Loginov

et al. 2021

Preprint

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Dormant cells of Mycobacterium tuberculosis, in addition to low metabolic activity and a high level of drug resistance, are characterized by 'non-culturability' – a state of the inability of the cells to grow on solid media. In this study, applying LC-MS proteomic profiling, we report the analysis of proteins accumulated in dormant, 'non-culturable' M. tuberculosis cells in a model of self-acidification of mycobacteria in the post-stationary phase, simulating the in vivo persistence conditions. This approach revealed the accumulation of a significant number of proteins after 4 months of storage in dormancy; among them, 468 proteins were significantly different from those in the actively growing cells and bore a positive fold change. Differential analysis revealed the proteins of the pH-dependent regulatory system phoP and allowed the reconstruction of the reactions of central carbon/glycerol metabolism, as well as revealing the salvaged pathways of mycothiol and UMP biosynthesis, establishing the cohort of survival enzymes of dormancy. The annotated pathways mirror the adaptation of the mycobacterial metabolic machinery to life within lipid-rich macrophages, especially the involvement of the methyl citrate and glyoxylate pathways. Thus, the current model of M. tuberculosis reflects the biochemical adaptation of these bacteria to persistence in vivo. Comparative analysis with published proteins with antigenic properties makes it possible to distinguish immunoreactive proteins among the proteins bearing a positive FC, which may include specific antigens of latent tuberculosis. Additionally, the biotransformatory enzymes (oxidoreductases and hydrolases) capable of prodrug activation and stored in the dormant state were annotated.

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“…The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates. Although all these methods can reduce the systematic bias to some extent, each approach has its own advantages and disadvantages (33)(34)(35). Therefore, the selection of the normalization…”

Section: B Ms Parametersmentioning

confidence: 99%

An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma

Kumar

Ghantasala

et al. 2020

Front. Oncol.

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Blood plasma is one of the most widely used samples for cancer biomarker discovery research as well as clinical investigations for diagnostic and therapeutic purposes. However, the plasma proteome is extremely complex due to its wide dynamic range of protein concentrations and the presence of high-abundance proteins. Here we have described an optimized, integrated quantitative proteomics pipeline combining the label-free and multiplexed-labeling-based (iTRAQ and TMT) plasma proteome profiling methods for biomarker discovery, followed by the targeted approaches for validation of the identified potential marker proteins. In this workflow, the targeted quantitation of proteins is carried out by multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) mass spectrometry. Thus, our approach enables both unbiased screenings of biomarkers and their subsequent selective validation in human plasma. The overall procedure takes only ~2 days to complete, including the time for data acquisition (excluding database searching). This protocol is quick, flexible, and eliminates the need for a separate immunoassay-based validation workflow in blood cancer biomarker investigations. We anticipate that this plasma proteomics workflow will help to accelerate the cancer biomarker discovery program and provide a valuable resource to the cancer research community.

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What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows

Cited by 29 publications

References 96 publications

Distinct systemic and mucosal immune responses during acute SARS-CoV-2 infection

Distinct systemic and mucosal immune responses during acute SARS-CoV-2 infection

Shotgun proteomic profiling of dormant, ‘non-culturable’Mycobacterium tuberculosis

An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma

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