Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity

Specht, Harrison; Emmott, Edward; Petelski, Aleksandra A.; Huffman, R. Gray; Perlman, David H.; Serra, Marco; Kharchenko, Peter V.; Koller, Antonius; Slavov, Nikolai

doi:10.1101/665307

Cited by 65 publications

(95 citation statements)

References 60 publications

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“…To reveal their relevance in the viral life-cycle, novel MS techniques may be advantageous, e.g. single-cell proteomics and in vivo cross-linking MS [41,42].…”

Section: Discussionmentioning

confidence: 99%

Processing of the SARS-CoV pp1a/ab nsp7–10 region

Krichel

Falke

Hilgenfeld

et al. 2020

Biochemical Journal

108

119

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Severe acute respiratory syndrome coronavirus is the causative agent of a respiratory disease with a high case fatality rate. During the formation of the coronaviral replication/ transcription complex, essential steps include processing of the conserved polyprotein nsp7-10 region by the main protease M pro and subsequent complex formation of the released nsp's. Here, we analyzed processing of the coronavirus nsp7-10 region using native mass spectrometry showing consumption of substrate, rise and fall of intermediate products and complexation. Importantly, there is a clear order of cleavage efficiencies, which is influenced by the polyprotein tertiary structure. Furthermore, the predominant product is an nsp7+8(2 : 2) hetero-tetramer with nsp8 scaffold. In conclusion, native MS, opposed to other methods, can expose the processing dynamics of viral polyproteins and the landscape of protein interactions in one set of experiments. Thereby, new insights into protein interactions, essential for generation of viral progeny, were provided, with relevance for development of antivirals.

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“…To reveal their relevance in the viral life-cycle, novel MS techniques may be advantageous, e.g. single-cell proteomics and in vivo cross-linking MS [41,42].…”

Section: Discussionmentioning

confidence: 99%

Processing of the SARS-CoV pp1a/ab nsp7–10 region

Krichel

Falke

Hilgenfeld

et al. 2020

Biochemical Journal

108

119

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“…We reasoned that an ideal method for cell type prioritization would make no assumptions about the distributions of features provided as input 29 , and more broadly, would be agnostic to the particular molecular features provided as input: that is, it would readily incorporate single-cell RNA-seq [30][31][32][33] , proteomics 34,35 , epigenomics 11,[36][37][38] , and imaging transcriptomics 15,17,39 datasets, among other modalities. Accordingly, Augur uses a random forest 40 classifier to predict sample labels for each cell type.…”

Section: Methodsmentioning

confidence: 99%

Cell type prioritization in single-cell data

Skinnider¹,

Squair²,

Kathe³

et al. 2019

Preprint

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jordan.squair@epfl.ch a, Schematic overview of Augur. b, AUCs of Augur and a naive random forest classifier without subsampling in simulated scRNA-seq datasets containing increasing numbers of cells. Cell type prioritizations are confounded by training dataset size for the naive classifier, but Augur abolishes this confounding factor. The mean and standard deviation of ten simulation replicates are shown. c, Pearson correlations between the AUC of each cell type, and the number of cells of that type sequenced, across a compendium of 22 scRNA-seq datasets, for Augur and a naive random forest classifier without subsampling. d, Augur AUCs scale monotonically with both the proportion of DE genes and the magnitude of DE in simulated cell populations. e, Relationship between number of DE genes detected by a representative test for single-cell differential gene expression (Wilcoxon rank-sum test), and the proportion of differentially expressed genes simulated between the two populations, for simulated populations of between 100 and 1,000 cells. f, Augur cell type prioritizations track with duration of LPS exposure in a cross-species scRNA-seq experiment 7 . Grey points show AUCs with sample labels randomly permuted. g-h, Cell type prioritization in matched single-cell 4 and bulk 8 transcriptomic profiles of PBMCs after interferon stimulation. g, Left, Augur cell type prioritizations mirror the number of DE genes in a microarray dataset of FACS-purified cells. Right, the number of DE genes detected in the scRNA-seq dataset by a Wilcoxon rank-sum test is uncorrelated with the FACS gold standard. h, Correlation coefficients between cell type prioritizations (AUC or number of DE genes) in the scRNA-seq dataset and the FACS gold standard. i-j, Cell type prioritization in the mouse ventromedial hypothalamus reflects induction of IEG transcription. i, Correlation between AUC and the difference in the first principal component of IEG expression (∆IEG eigengene) engaging in aggressive behavior. j, Pearson correlation coefficients between cell type-specific AUC and ∆IEG eigengene values for eleven behavioral stimuli 9 . k, Reproducibility of cell type prioritization in two independent scRNA-seq studies of Alzheimer's disease 5,10 . l, Augur cell type prioritizations in a scATAC-seq dataset 11 track with the number of DE genes in an RNA-seq dataset of FACS-purified cells.

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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%

“…We decided to analyze the single-cell proteomics datasets from the recent update to the SCoPE-MS method [24]. The SCoPE-MS method uses TMT to multiplex single cells for analysis, while reserving one or more TMT channels for a “carrier sample,” i.e.…”

Section: Resultsmentioning

confidence: 99%

“…The Specht et al single-cell proteomics dataset [24] consists of two distinct sets of experiments: a quality control dataset consisting of 76 mass spectrometry acquisitions and a macrophage differentiation dataset consisting of 69 mass spectrometry acquisitions, each acquired using a Q-Exactive mass spectrometer. In both cases, each acquisition corresponds to a single experiment analyzing multiple single cells using the tandem mass tag (TMT) 10-plex reagents as described in the original paper [24]. The raw data files were downloaded from MassIVE [25] (ID: MSV000083945) and converted to mzML format using ThermoRawFileParser, with vendor peak-picking enabled.…”

Section: Methodsmentioning

confidence: 99%

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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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Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity

Cited by 65 publications

References 60 publications

Processing of the SARS-CoV pp1a/ab nsp7–10 region

Processing of the SARS-CoV pp1a/ab nsp7–10 region

Cell type prioritization in single-cell data

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

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