Microsampling Capillary Electrophoresis Mass Spectrometry Enables Single-Cell Proteomics in Complex Tissues: Developing Cell Clones in Live <i>Xenopus laevis</i> and Zebrafish Embryos

Lombard‐Banek, Camille; Moody, Sally A.; Manzini, M. Chiara; Nemes, Péter

doi:10.1021/acs.analchem.9b00345

Cited by 107 publications

(120 citation statements)

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“…While the analysis of small, gel-based experiments represents a long-standing, routine task for proteomics laboratories, the analysis of single cells is an emerging field and an exciting future for proteomics technologies [34]. Currently, several methods are making progress toward performing proteomics experiments at single-cell resolution [24, 35–37]. Critically, all of these methods inherntly rely on acquiring tandem mass spectra from samples with low analyte abundance, which in turn results in fewer acquired mass spectra and many mass spectra of insufficient quality for identification; hence, we sought to determine if static Percolator models could improve peptide detection rates in single-cell proteomics experiments.…”

Section: Resultsmentioning

confidence: 99%

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

Fondrie

Noble

2019

Preprint

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1Machine learning methods have proven invaluable for increasing the sensitivity of peptide de-2 tection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, 3 use semi-supervised algorithms to learn models directly from the datasets that they analyze. 4 Although these methods are effective for many proteomics experiments, we suspected that they 5 may be suboptimal for experiments of smaller scale. In this work, we found that the power and 6 consistency of Percolator results was reduced as the size of the experiment was decreased. As 7 an alternative, we propose a different operating mode for Percolator: learn a model with Per-8 colator from a large dataset and use the learned model to evaluate the small-scale experiment. 9 We call this a "static modeling" approach, in contrast to Percolator's usual "dynamic model" 10 that is trained anew for each dataset. We applied this static modeling approach to two settings: 11 small, gel-based experiments and single-cell proteomics. In both cases, static models increased 12 the yield of detected peptides and eliminated the model-induced variability of the standard dy-13 namic approach. These results suggest that static models are a powerful tool for bringing the 14 full benefits of Percolator and other semi-supervised algorithms to small-scale experiments. 15 1 Introduction 16The assignment of peptide sequences to tandem mass spectra is a fundamental task in any pro-17 teomics experiment [1]. This task is most often performed by a database search algorithm, which 18 was first introduced with the SEQUEST search engine in 1994 [2]. Database search algorithms score 19 the theoretical mass spectra of peptides from a selected sequence database against the acquired 20 mass spectra from the experiment, yielding a set of peptide-spectrum matches (PSMs). Although 21 the score functions of individual search engines may differ greatly, the scores reported by each are 22 intended to reflect the quality of PSMs. 23 2 Machine learning strategies to re-score PSMs were first introduced due to their ability to inte-24 grate multiple, orthogonal scores and features from database search engines, thereby improving the 25 sensitivity of peptide detection. Two such methods were proposed simultaneously: one based on 26 linear discriminant analysis [3] and another that used support vector machine (SVM) models to a 27 similar end [4]. Critically, both methods were examples of supervised learning: they relied on fully 28 labeled datasets-where the correct and incorrect PSMs could be determined a priori -to train 29 their respective models. These trained models were then used to predict a new score for the PSMs 30 of a new dataset. We refer to the models used by these methods as static, meaning their parameters 31 do not change when the dataset is changed. Critically, the success of these static models depended 32 on the quality of the labeled dataset that was used for training and how well it reflected the datasets 33 that were subsequently analyzed. The origina...

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Section: Resultsmentioning

confidence: 99%

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

Fondrie

Noble

2019

Preprint

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“…CE has been used extensively for single-cell proteomics, but so far the cells analyzed have generally been oocytes and blastocysts, etc. (9,10,38,(58)(59)(60), all of which are much larger than typical mammalian cells. One current disadvantage for CE relative to LC is that a packed LC column naturally cleans up and preconcentrates samples into focused bands at the inlet of the column prior to gradient elution, whereas effective preconcentration prior to separation for CE is less straightforward.…”

Section: Progressmentioning

confidence: 99%

Single-cell Proteomics: Progress and Prospects

Kelly

2020

Molecular & Cellular Proteomics

257

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MS-based proteome profiling has become increasingly comprehensive and quantitative, yet a persistent shortcoming has been the relatively large samples required to achieve an in-depth measurement. Such bulk samples, typically comprising thousands of cells or more, provide a population average and obscure important cellular heterogeneity. Single-cell proteomics capabilities have the potential to transform biomedical research and enable understanding of biological systems with a new level of granularity. Recent advances in sample processing, separations and MS instrumentation now make it possible to quantify >1000 proteins from individual mammalian cells, a level of coverage that required an input of thousands of cells just a few years ago. This review discusses important factors and parameters that should be optimized across the workflow for single-cell and other low-input measurements. It also highlights recent developments that have advanced the field and opportunities for further development.

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“…The common idea in all the sample preparation approaches that were developed to enable single cell proteomic analysis is 1) downscaling of the reagent volumes and 2) limiting the sample handling by performing the preparation in a single container. Using this concept, Lombard and co-workers extended MS to probe the proteome of single cells, with subcellular resolution for embryonic cells from X. laevis and zebrafish embryos [111] and mouse neurons [112]. Using nanoliter-scale oil air droplet (OAD) chip, single HeLa cells and mouse oocytes were prepared for bottom-up analysis on an orbitrap instrument leading to the identification of~40 to~250 proteins on average, respectively [113].…”

Section: A New Venue For Ms-based Proteomics: Single-cell Analysismentioning

confidence: 99%

Mass Spectrometry Advances and Perspectives for the Characterization of Emerging Adoptive Cell Therapies

Lombard‐Banek

Schiel

2020

Molecules

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Adoptive cell therapy is an emerging anti-cancer modality, whereby the patient’s own immune cells are engineered to express T-cell receptor (TCR) or chimeric antigen receptor (CAR). CAR-T cell therapies have advanced the furthest, with recent approvals of two treatments by the Food and Drug Administration of Kymriah (trisagenlecleucel) and Yescarta (axicabtagene ciloleucel). Recent developments in proteomic analysis by mass spectrometry (MS) make this technology uniquely suited to enable the comprehensive identification and quantification of the relevant biochemical architecture of CAR-T cell therapies and fulfill current unmet needs for CAR-T product knowledge. These advances include improved sample preparation methods, enhanced separation technologies, and extension of MS-based proteomic to single cells. Innovative technologies such as proteomic analysis of raw material quality attributes (MQA) and final product quality attributes (PQA) may provide insights that could ultimately fuel development strategies and lead to broad implementation.

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Microsampling Capillary Electrophoresis Mass Spectrometry Enables Single-Cell Proteomics in Complex Tissues: Developing Cell Clones in Live Xenopus laevis and Zebrafish Embryos

Cited by 107 publications

References 54 publications

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

Single-cell Proteomics: Progress and Prospects

Mass Spectrometry Advances and Perspectives for the Characterization of Emerging Adoptive Cell Therapies

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