Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics

Vanderaa, Christophe; Gatto, Laurent

doi:10.1021/acs.jproteome.3c00227

Cited by 20 publications

(11 citation statements)

References 52 publications

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“…However, many of the proteins were quantified in only a fraction of the cells, and we focused on a subset of 1,893 proteins that are quantified in over 30 single cells from the data set and over 5 single cells from each time point. Such levels of missing data are common in many data sets and limit the number of proteins that can be analyzed without imputing missing values …”

Section: Resultsmentioning

confidence: 99%

Dynamics of Single-Cell Protein Covariation during Epithelial–Mesenchymal Transition

Khan,

Conover,

Asthagiri

et al. 2024

J. Proteome Res.

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Physiological processes, such as the epithelial−mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein−protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein−protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions.

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Section: Resultsmentioning

confidence: 99%

Dynamics of Single-Cell Protein Covariation during Epithelial–Mesenchymal Transition

Khan,

Conover,

Asthagiri

et al. 2024

J. Proteome Res.

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“…The resulting raw data were searched initially using MaxQuant with no peptide or protein FDR filtering, and further processed using DART-ID 35 and the SCP R package 36 . We assessed the sensitivity and consistency of our data as recommended by Vanderaa and Gatto 42 (Figure 2). These assessments suggest our dataset is similar to previous single-cell studies in animal models, including the original SCoPE2 report 20 .…”

Section: Resultsmentioning

confidence: 99%

Single-cell proteomics differentiates Arabidopsis root cell types

Montes,

Zhang,

Nolan

et al. 2024

Preprint

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Recently, major advances have enabled the exploration of cellular heterogeneity using single-cell proteomics. Here we explore the feasibility of single-cell proteomics on plant samples. We focus on Arabidopsis thaliana, examining isolated single cells from the cortex and endodermis, which are two adjacent root cell-types derived from a common stem cell. From 756 cells we identify 3,751 proteins and 1,114 proteins/cell. Ultimately, we focus on 3,101 proteins quantified following stringent filtering. Of these, we identified 555 proteins whose expression is enriched in either the cortex or endodermis and are able to differentiate these closely related plant cell-types. Collectivity, our findings underscore the promise of single-cell proteomics to explore the heterogeneity of expression between individual plant cells.

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“…The slight fold-change compression may arise for various reasons, including interferences, or lowly abundant peptides below the limit of detection in single-cell measurements. The latter challenge may be mitigated by improving the handling of missing data 59 .…”

Section: Anticipated Resultsmentioning

confidence: 99%

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

Leduc,

Koury,

Cantlon

et al. 2023

Preprint

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Single-cell proteomics by mass spectrometry (MS) allows quantifying proteins with high specificity and sensitivity. To increase its throughput, we developed nPOP, a method for parallel preparation of thousands of single cells in nanoliter volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation with plexDIA demonstrates accurate quantification of about 3,000 - 3,700 proteins per human cell. The protocol is implemented on the CellenONE instrument and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 days to prepare over 3,000 single cells. We provide metrics and software for quality control that can support the robust scaling of nPOP to higher plex reagents for achieving reliable high-throughput single-cell protein analysis.

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Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics

Cited by 20 publications

References 52 publications

Dynamics of Single-Cell Protein Covariation during Epithelial–Mesenchymal Transition

Dynamics of Single-Cell Protein Covariation during Epithelial–Mesenchymal Transition

Single-cell proteomics differentiates Arabidopsis root cell types

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

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