<i>iq</i>: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics

Pham, Thang V.; Henneman, Alex A.; Jiménez, Connie R.

doi:10.1093/bioinformatics/btz961

Cited by 78 publications

(80 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Batch correction was performed at the precursor level as described previously 7 , using linear regression for intra-batch correction and control samples for inter-batch correction. Protein quantification was subsequently carried out using the MaxLFQ algorithm 104,105 as implemented in the diann R package (https://github.com/vdemichev/diann-rpackage). The Generation Scotland cohort proteomics raw data, which we described previously 7 , have been reanalyzed using the updated software pipeline, to ensure comparability.…”

Section: Methodsmentioning

confidence: 99%

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome

Demichev¹,

Tober‐Lau²,

Nazarenko³

et al. 2020

Preprint

View full text Add to dashboard Cite

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.

show abstract

Section: Methodsmentioning

confidence: 99%

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome

Demichev¹,

Tober‐Lau²,

Nazarenko³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to further improve DIA-NN (version 1.7.10) for the high-throughput workflow presented here, we have implemented the MaxLFQ protein quantification algorithm (Cox et al, 2014) . Originally, it was designed for shotgun-MS studies, but was recently introduced to DIA proteomics (Pham et al, 2020) and increased the quantification precision for serum and plasma proteomes. The sample preparation workflow is designed for handling 384 samples/batch (four 96-well plates).…”

Section: A New Platform For High-throughput Large-scale Proteomicsmentioning

confidence: 99%

Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics

Messner

Demichev

Wendisch

et al. 2020

Preprint

View full text Add to dashboard Cite

The COVID-19 pandemic is an unprecedented global challenge. Highly variable in its presentation, spread and clinical outcome, novel point-of-care diagnostic classifiers are urgently required. Here, we describe a set of COVID-19 clinical classifiers discovered using a newly designed low-cost high-throughput mass spectrometry-based platform. Introducing a new sample preparation pipeline coupled with short-gradient high-flow liquid chromatography and mass spectrometry, our methodology facilitates clinical implementation and increases sample throughput and quantification precision. Providing a rapid assessment of serum or plasma samples at scale, we report 27 biomarkers that distinguish mild and severe forms of COVID-19, of which some may have potential as therapeutic targets. These proteins highlight the role of complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling upstream and downstream of Interleukin 6. Application of novel methodologies hence transforms proteomics from a research tool into a rapid-response, clinically actionable technology adaptable to infectious outbreaks. Highlights-A completely redesigned clinical proteomics platform increases throughput and precision while reducing costs.-27 biomarkers are differentially expressed between WHO severity grades for COVID-19.-The study highlights potential therapeutic targets that include complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling both upstream and downstream of interleukin 6.

show abstract

“…We performed an exploratory analysis on the different expression protein profile of eight PDAC and 11 benign platelets patients using our proteomics data and four additional datasets to validate our findings: (i) label-free quantification from DDA data of discovery cohort; (ii) label-free quantification from DIA data of discovery cohort; (iii) iq implementation of DIA data of discovery cohort [ 54 ]; (iv) published study on proteomics platelets using healthy controls and PDAC patients [ 9 ].…”

Section: Resultsmentioning

confidence: 99%

Omics Analysis of Educated Platelets in Cancer and Benign Disease of the Pancreas

Mantini

Meijer

Glogovitis

et al. 2020

Cancers

Self Cite

View full text Add to dashboard Cite

Pancreatic ductal adenocarcinoma (PDAC) is traditionally associated with thrombocytosis/hypercoagulation and novel insights on platelet-PDAC “dangerous liaisons” are warranted. Here we performed an integrative omics study investigating the biological processes of mRNAs and expressed miRNAs, as well as proteins in PDAC blood platelets, using benign disease as a reference for inflammatory noise. Gene ontology mining revealed enrichment of RNA splicing, mRNA processing and translation initiation in miRNAs and proteins but depletion in RNA transcripts. Remarkably, correlation analyses revealed a negative regulation on SPARC transcription by isomiRs involved in cancer signaling, suggesting a specific ”education” in PDAC platelets. Platelets of benign patients were enriched for non-templated additions of G nucleotides (#ntaG) miRNAs, while PDAC presented length variation on 3′ (lv3p) as the most frequent modification on miRNAs. Additionally, we provided an actionable repertoire of PDAC and benign platelet-ome to be exploited for future studies. In conclusion, our data show that platelets change their biological repertoire in patients with PDAC, through dysregulation of miRNAs and splicing factors, supporting the presence of de novo protein machinery that can “educate” the platelet. These novel findings could be further exploited for innovative liquid biopsies platforms as well as possible therapeutic targets.

show abstract

iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics

Cited by 78 publications

References 6 publications

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome

Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics

Omics Analysis of Educated Platelets in Cancer and Benign Disease of the Pancreas

Contact Info

Product

Resources

About