Exploring functional protein covariation across single cells using nPOP

Leduc, Andrew; Huffman, R. Gray; Slavov, Nikolai

doi:10.1101/2021.04.24.441211

Cited by 36 publications

(50 citation statements)

References 89 publications

(233 reference statements)

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“…With a better understanding of today's limits, such as the maximum loading channels that should be used in amplificationbased experiments, further advances will continue to chip away at these limits [59]. One key limitation is the sample preparation, and two preprints that were posted in early 2021 have demonstrated nanoliter semi-automated workflows to address these limitations [26,60]. These are reaping obvious dividends, resulting in some of the most comprehensive singlecell proteomes described to date (Figure 6).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Orsburn

2021

Proteomes

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Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Orsburn

2021

Proteomes

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“…These methods aim to achieve similar objectives, such as efficient delivery of peptides from single cells to the MS instruments via miniaturized sample preparation ( 1 ), but differ in the approaches used for achieving these objectives. For example, sample preparation volumes can be reduced by using microfabricated wells ( 30 ) or by using droplets on the surface of a slide ( 31 ). All single-cell MS methods can be classified either as label free or as multiplexed, and these categories have associated advantages and disadvantages as previously reviewed ( 1 , 2 ).…”

Section: Trade-offs Between Single-cell Proteomics Methodsmentioning

confidence: 99%

“…Sample preparation throughput increased with the introduction of automated multiwell-plate methods, such as minimal ProteOmic sample Preparation (mPOP) ( 21 , 39 , 40 ) and automated preparation in one pot for trace samples (autoPOTS) ( 41 ). A further increase is afforded by a droplet sample preparation method (nano-ProteOmic sample Preparation [nPOP]) that enables the simultaneous and automated preparation of over 2000 single cells in droplets on a slide surface ( 31 ). In addition to increasing throughput, the simultaneous processing of thousands of cells reduces the batch effects associated with different sample preparation batches.…”

Section: Highly Parallel Sample Preparationmentioning

confidence: 99%

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Scaling Up Single-Cell Proteomics

Slavov

2022

Molecular & Cellular Proteomics

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Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Toward meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications.

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“…The isobaric reporter ion fragments corresponding to labels applied to single cells, if within the intrascan linear dynamic range of the instrument, can be used for both peptide identification and quantification. [21][22][23][24] Although other single cell technologies such as single cell RNAseq have been sorting and analyzing single cells for over a decade with increasingly sophisticated technology, relatively little of this can be applied to proteomics approaches. 25,26 While new technologies such as nanoPOTs 27 , nPOP 23 and ProteoChip 24 are improving these aspects, technical challenges in sample preparation may be the biggest limiter to entry into single cell proteomics.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Ion Data Analysis Reduction (DIDAR) allows rapid quality control analysis and filtering of multiplexed single cell proteomics data

Jenkins

Orsburn

2022

Preprint

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Recent advances in the sensitivity and speed of mass spectrometers utilized for proteomics and metabolomics workflows has led to a dramatic increase in data file size and density. For a field already challenged by data complexity due to a dependence on desktop PC architecture and the Windows operating systems, further compromises appear inevitable as data density scales. As one method to reduce data complexity, we present herein a light-weight python script that can rapidly filter and provide analysis metrics from tandem mass spectra based on the presence and number of diagnostic fragment ions determined by the end user. Diagnostic Ion Data Analysis Reduction (DIDAR) can be applied to any mass spectrometry dataset to create smaller output files containing only spectra likely to contain post-translational modifications or chemical labels of interest. In this study we describe the application DIDAR within the context of multiplexed single cell proteomics workflows. When applied in this manner using reporter fragment ions as diagnostic signatures, DIDAR can provide quality control metrics based on the presence of reporter ions derived from single human cells and simplified output files for search engine analysis. The simple output metric text files can be used to rapidly flag entire LCMS runs with technical issues and remove them from downstream analysis based on end user minimum requirements. Acquisition files that pass these criteria are further improved through the automatic removal of spectra where insufficient signal from single cells is observed. We describe the application of DIDAR to two recently described multiplexed single cell proteomics datasets.

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Exploring functional protein covariation across single cells using nPOP

Cited by 36 publications

References 89 publications

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Scaling Up Single-Cell Proteomics

Diagnostic Ion Data Analysis Reduction (DIDAR) allows rapid quality control analysis and filtering of multiplexed single cell proteomics data

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