DART-ID increases single-cell proteome coverage

Chen, Albert T.; Franks, Alexander; Slavov, Nikolai

doi:10.1101/399121

Cited by 9 publications

(15 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…incorporating additional features, such as retention time. 46,47 This example is consistent with previous observations that longer accumulation times can increase the number of confident peptide identifications for lowly abundant samples 17,40 . Furthermore, this example underscores that fill times can strongly influence apex targeting.…”

Section: Such Curves Offer Insight Into Low-confidence Peptide Identisupporting

confidence: 91%

See 1 more Smart Citation

DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

Huffman¹,

Specht²,

Chen³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

The performance of ultrasensitive LC-MS/MS methods, such as Single-Cell Proteomics by Mass Spectrometry (SCoPE-MS), depends on multiple interdependent parameters. This interdependence makes it challenging to specifically pinpoint bottlenecks in the LC-MS/MS methods and approaches for resolving them. For example, low signal at MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such bottlenecks by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many search engines, such as MaxQuant, already provide such data, and we developed an open source platform for their interactive visualization and analysis: Data-driven Optimization of MS (DO-MS). We found that in many cases DO-MS not only specifically diagnosed bottlenecks but also enabled us to rationally optimize them. For example, we used DO-MS to diagnose poor sampling of the elution peak apex and to optimize it, which increased the efficiency of delivering ions for MS2 analysis by 370%. DO-MS is easy to install and use, and its GUI allows for interactive data subsetting and high-quality figure generation. The modular design of DO-MS facilitates customization and expansion. DO-MS is available for download from GitHub: github.com/SlavovLab/DO-MS

show abstract

Section: Such Curves Offer Insight Into Low-confidence Peptide Identisupporting

confidence: 91%

“…DO-MS's diagnostic plots have been organized into the following five categories: Chromatography, Instrument Performance, Contamination, Peptide Identifications, SCoPE-MS Diagnostics, and DART-ID 47 . Data are visualized as full distributions using vertically oriented histograms to avoid kernel-smoothing issues.…”

Section: Visualizationmentioning

confidence: 99%

DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

Huffman¹,

Specht²,

Chen³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…If the RT can be predicted with high accuracy for all peptides, the predicted RT of the peptide in each PSM can be compared with the observed RT associated with the spectrum to determine the quality of the PSMs. Although several studies have shown the value of integrating RT information into the proteomics data analysis workflow 26,[32][33][34][35] , RT is typically not used in peptide identification and is independent of the FDR estimation. Therefore, the difference between predicted and observed RTs can serve as an effective and unbiased quantitative metric for evaluating the quality of PSMs reported by different variant peptide identification methods.…”

mentioning

confidence: 99%

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

et al. 2020

View full text Add to dashboard Cite

Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

show abstract

“…2b. The number of confidently identified proteins can be increased up to 50% by applying DART-ID, a data-driven Bayesian framework that uses retention time evidence to enhance peptide sequence identification 13 . Taken together, these results suggest that mPOP supports the preparation of SCoPE-MS sets from low-input samples.…”

Section: Scope-ms Sample With Two Carrier Channels (126c -Jurkat Cellmentioning

confidence: 99%

Automated sample preparation for high-throughput single-cell proteomics

Specht

Harmange

et al. 2018

Preprint

Self Cite

137

View full text Add to dashboard Cite

A major limitation to applying quantitative LC-MS/MS proteomics to small samples, such as single cells, are the losses incured during sample cleanup. To relieve this limitation, we developed a Minimal ProteOmic sample Preparation (mPOP) method for culture-grown mammalian cells. mPOP obviates cleanup and thus eliminates cleanup-related losses while expediting sample preparation and simplifying its automation. Bulk SILAC samples processed by mPOP or by conventional urea-based methods indicated that mPOP results in complete cell lysis and accurate relative quantification. We integrated mPOP lysis with the Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) sample preparation, and benchmarked the quantification of such samples on a Q-exactive instrument. The results demonstrate low noise and high technical reproducibility. Then, we FACS sorted single U-937, HEK-293, and mouse ES cells into 96-well plates and analyzed them by automated mPOP and SCoPE-MS. The quantified proteins enabled separating the single cells by cell-type and cell-division-cycle phase.discussions and constructive comments.

show abstract

DART-ID increases single-cell proteome coverage

Abstract: 9Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can iden- 24. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Cited by 9 publications

References 61 publications

DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Automated sample preparation for high-throughput single-cell proteomics

Contact Info

Product

Resources

About