IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

Yu, Fengchao; Haynes, Sarah E.; Nesvizhskii, Alexey I.

doi:10.1101/2020.11.02.365437

Cited by 40 publications

(53 citation statements)

References 54 publications

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“…Label free quantitation was conducted with IonQuant (Yu et al, 2020) and Match-Between-Runs enabled (with default parameters) and using Top-3 quantitation. Feature detection tolerance was set to 10ppm and RT Window to 0.6 minutes with an IM Window of 0.05 1/k0.…”

Section: Primary Data Analysis (Identification and Quantitation)mentioning

confidence: 99%

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

Ahern

Ainsworth

et al. 2021

Preprint

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Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete understanding of potentially druggable immune mediators of disease. To advance this, we present a comprehensive multi-omic blood atlas in patients with varying COVID-19 severity and compare with influenza, sepsis and healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity revealed cells, their inflammatory mediators and networks as potential therapeutic targets, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Tensor and matrix decomposition of the overall dataset revealed feature groupings linked with disease severity and specificity. Our systems-based integrative approach and blood atlas will inform future drug development, clinical trial design and personalised medicine approaches for COVID-19.

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Section: Primary Data Analysis (Identification and Quantitation)mentioning

confidence: 99%

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

Ahern

Ainsworth

et al. 2021

Preprint

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“…We speculate that this drastic reduction in replicate overlap again is mostly caused by the stochastic selection of precursors in DDA strategies. Even though label-free measurements now allow for FDR-controlled match between runs based on MS1 features 25 , we are confident that the transition to DIA measurements will improve replicate overlap and quantification correlation. Further, we speculated that the fast duty cycles and the increased usage of the ion beam of PASEF on the timsTOF Pro will benefit our label-free single cell analysis.…”

Section: Label-free Single Cell Proteome Acquisition With the Proteochipmentioning

confidence: 96%

“…Label-free proteome analysis has several advantages over multiplexed sample workflows, like the direct MS1 based quantification, the possibility of highly confident feature matching between analytical runs and the reduced chemical noise introduced by the labeling. 25,26 We therefore evaluated the proteoCHIP protocol in the analysis of label-free single cell samples, using shorter gradients based on the vastly reduced sample input (i.e. 30 minutes compared to 60 minutes for TMT-labeled samples).…”

Section: Label-free Single Cell Proteome Acquisition With the Proteochipmentioning

confidence: 99%

An automated workflow for multiplexed single-cell proteomics sample preparation at unprecedented sensitivity

Hartlmayr

Ctortecka

Seth³

et al. 2021

Preprint

105

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The analysis of single cell proteomes has recently become a viable complement to transcript and genomics studies. Proteins are the main driver of cellular functionality and mRNA levels are often an unreliable proxy of such. Therefore, the global analysis of the proteome is essential to study cellular identities. Both multiplexed and label-free mass spectrometry-based approaches with single cell resolution have lately attributed surprising heterogeneity to believed homogenous cell populations. Even though specialized experimental designs and instrumentation have demonstrated remarkable advances, the efficient sample preparation of single cells still lacks behind. Here, we introduce the proteoCHIP, a universal option for single cell proteomics sample preparation at surprising sensitivity and throughput. The automated processing using a commercial system combining single cell isolation and picoliter dispensing, the cellenONE®, allows to reduce final sample volumes to low nanoliters submerged in a hexadecane layer simultaneously eliminating error prone manual sample handling and overcoming evaporation. With this specialized workflow we achieved around 1,000 protein groups per analytical run at remarkable reporter ion signal to noise while reducing or eliminating the carrier proteome. We identified close to 2,000 protein groups across 158 multiplexed single cells from two highly similar human cell types and clustered them based on their proteome. In-depth investigation of regulated proteins readily identified one of the main drivers for tumorigenicity in this cell type. Our workflow is compatible with all labeling reagents, can be easily adapted to custom workflows and is a viable option for label-free sample preparation. The specialized proteoCHIP design allows for the direct injection of label-free single cells via a standard autosampler resulting in the recovery of 30% more protein groups compared to samples transferred to PEG coated vials. We therefore are confident that our versatile, sensitive, and automated sample preparation workflow will be easily adoptable by non-specialized groups and will drive biological applications of single cell proteomics.

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“…IonQuant, for FDR controlled MBR in a re‐analysis of sparse single‐cell datasets resulted in less false positives when transferring identifications. This might become extremely effective for label‐free single‐cell proteomics studies to confidently transfer identifications from a higher input sample to single‐cell runs 90 . Careful quality control and stringent filtering of scarce data is important in MS‐based proteomics and becomes critical for the biological conclusions based on single‐cell measurements.…”

Section: Post‐processing and Data Analysismentioning

confidence: 99%

The rise of single‐cell proteomics

Ctortecka

Mechtler

2021

Analytical Science Advances

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Mass spectrometry‐based proteomics comprehensively defines proteome expression patterns in thousands of cells majorly contributing to our current understanding of many biological processes. More recently, single‐cell transcriptome and genome studies, however, have demonstrated overwhelming heterogeneity of tissues and cellular subpopulations. These studies have indicated different cellular functionality and identity, which are mainly driven by proteins and their posttranscriptional modifications. The rapidly emerging field of single‐cell proteomics aims at complementing transcriptome and genome data by generating comparative protein expression profiles from individual cells. Recent developments demonstrated tremendous improvements in sample preparation workflows and MS instrumentation, quantifying over 1000 proteins from a single cell. Efficient and reproducible sample processing in conjunction with sensitive MS acquisition strategies will allow to further increase the proteome coverage of tissues with single‐cell resolution. The required throughput and data reliability of such studies are still subject to further developments. Therefore, we herein discuss recent progress on specialized workflows and instrumentation next to advancements outside the field, which we expect to contribute to the development of comprehensive single‐cell proteomics.

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IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

Cited by 40 publications

References 54 publications

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

An automated workflow for multiplexed single-cell proteomics sample preparation at unprecedented sensitivity

The rise of single‐cell proteomics

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