Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single Cell Proteomics and the Carrier Proteome Effect

ABSTRACT: Single cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry) comprise technical limitations with the potential to effect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500x single cell proteomes, is used to enable peptide identifications. Here, we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy as it relates to mass analyzer dynamic range, multiplexing level, and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single Cell Proteomics Companion a program that enables rapid evaluation of single cell proteomics data and recommends instrument and data analysis parameters for improved data quality.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Christopher Rose

PROVIDER: MSV000085959 | MassIVE | Sun Aug 16 16:23:00 BST 2020

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Single Cell Proteomics and the Carrier Proteome Effect

Project description:Single cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method that utilizes isobaric labels to quantify multiplexed single cell proteomes. While this technique has generated great excitement, the technologies underlying SCoPE-MS - isobaric labels and mass spectrometry - comprise technical limitations with the potential to unfavorably impact data quality and biological interpretation. These limitations are due to the carrier proteome, a sample added at 25-500x single cell proteomes to enable peptide identifications. Here, we perform SCoPE-MS experiments with increasing amounts of carrier proteome and evaluate quantitative accuracy as it relates to mass analyzer dynamic range, multiplexing level, and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy – we term this the carrier proteome effect. Based on our findings, we provide guidance on experimental design, data collection, and data analysis to limit the impact of the carrier proteome effect within SCoPE-MS measurements.

2020-12-16 | PXD016573 | Pride

Quantitative accuracy and precision in multiplexed single-cell proteomics.

Project description:Multiplexed quantitative mass spectrometry-based proteomics is shaped by numerous opposing propositions. With the emergence of multiplexed single-cell proteomics, studies increasingly present single cell measurements in conjunction with an abundant congruent carrier to improve precursor selection and enhance identifications. While these extreme carrier spikes are often >100-times more abundant than the investigated samples, undoubtedly the total ion current increases but quantitative accuracy possibly is affected. We here focus on narrowly titrated carrier spikes (i.e. <20x) and evaluate the elimination of such for comparable sensitivity at superior accuracy. We find that subtle changes in the carrier ratio can severely impact measurement variability and describe alternative multiplexing strategies to evaluate data quality. Lastly, we demonstrate elevated replicate overlap, while preserving acquisition throughput at improved quantitative accuracy with DIA-TMT and discuss optimized experimental designs for multiplexed proteomics of trace samples. This comprehensive benchmarking gives an overview of currently available techniques and guides through conceptualizing the optimal single-cell proteomics experiment.

2022-01-28 | PXD027912 | Pride

Offset mass carrier proteome improves quantification of multiplexed single cell proteomics

Project description:Multiplexed single cell proteomics by mass spectrometry (scpMS) approaches currently offer the highest throughput as measured by cells analyzed per day. These methods employ isobaric labels and typically a carrier proteome - a sample added at 20-500x the single cell level that improves peptide sampling and identification. Peptides from the carrier and single cell proteomes exist within the same precursor isotopic cluster and are co-isolated for identification and quantification. This represents a challenge as high levels of carrier proteome limit the sampling of peptide ions from single cell samples and can potentially lead to decreased accuracy of quantitative measurements. Here, we address this limitation by introducing a triggered by offset mass acquisition method for scpMS (toma-scpMS) that utilizes a carrier proteome labeled with non-isobaric tags that have the same chemical composition but different mass as the labels used for quantitative multiplexing. Within toma-scpMS the carrier proteome and single cell proteome are separated at the precursor level, enabling separate isolation, fragmentation, and quantitation of the single cell samples. To enable this workflow we implemented a custom data acquisition scheme within inSeqAPI, an instrument application programming interface program, that performed real-time identification of carrier proteome peptides and subsequent triggering of offset single cell quantification scans. We demonstrate that toma-scpMS is more robust to high-levels of carrier proteome and offers superior quantitative accuracy as compared to traditional multiplexed scpMS approaches when similar carrier proteome levels are employed.

2024-08-16 | MSV000095629 | MassIVE

An intelligent Boosting to Amplify Signal with Isobaric Labeling (iBASIL) strategy for precise quantitative single-cell proteomics

Project description:Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells, In particular the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection (e.g., our recent boosting to amplify signal with isobaric labeling (BASIL) approach1) has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have systematically evaluated and optimized the BASIL approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of single cells. This improved, intelligent BASIL (iBASIL) approach enables comprehensive and quantitative single-cell proteomics analysis by carefully controlling the boosting-to-sample ratio and optimizing MS automatic gain control and ion injection time settings. Using standard LC-MS platforms, iBASIL enabled precise quantification of >1,000 proteins from 0.1 ng of peptide (equivalent to a single cell), or ~800 proteins from single FACS-isolated MCF10A cells. Moreover, coupling iBASIL to nanoscale fractionation enabled quantification of>1,438 proteins while readily separating 3 different cell lines using single-cell-equivalent peptides. We believe iBASIL has broad utility for comprehensive and precise quantitative single-cell proteomics for systems biology and biomedical research, as well as for comprehensive proteomic analysis of size-limited clinical specimens that cannot be readily accessed by regular proteomics platforms.

2020-03-05 | PXD016057 | JPOST Repository

An improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) strategy for precise quantitative single-cell proteomics

Project description:Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g., in general <100x) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g., 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). Using standard LC-MS platforms, iBASIL enabled identification of ~2,500 proteins and precise quantification of ~1,500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

2020-03-05 | PXD017626 | JPOST Repository

Optimizing accuracy and depth of protein quantification in experiments using isobaric carriers

Project description:The isobaric carrier approach, which combines small isobarically-labeled samples with a larger isobarically-labeled carrier sample, is finding diverse applications in ultrasensitive mass-spectrometry analysis of very small samples, such as single cells. To enhance the growing use of isobaric carriers, we characterized the trade-offs of using isobaric carriers in controlled experiments with complex human proteomes. The data indicate that isobaric carriers directly enhances peptide sequence identification without simultaneously increasing the number of protein copies sampled from small samples. The results also indicate strategies for optimizing the amount of isobaric carrier and analytical parameters, such as ion accumulation time, for different priorities such as improved quantification or increased number of identified proteins. Balancing these trade-offs enables adapting isobaric carrier experiments to different applications, such as quantifying proteins from limited biopsies or organoids, building single-cell atlases, or modeling protein networks in single cells. In all cases, the reliability of protein quantification should be estimated and incorporated in all subsequent analysis. We expect that these guidelines will aid in explicit incorporation of the characterized trade-offs in experimental designs and transparent error propagation in data analysis.

2020-08-24 | MSV000086004 | MassIVE

A deeper look at carrier proteome effects for single-cell proteomics

Project description:We probe the carrier proteome effects in single cell proteomics with mixed species TMTpro-labeled samples. We demonstrate that carrier proteomes, while increasing overall identifications, dictate which proteins are identified. We show that quantitative precision and signal intensity are limited at high carrier levels, hindering the recognition of regulated proteins. Guidelines for optimized mass spectrometry acquisition parameters and best practices for fold-change or protein copy number-based comparisons are provided.

2022-02-24 | PXD027742 | Pride

Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

Project description:Cellular heterogeneity is important to biological processes, including cancer and development. However, proteome heterogeneity is largely unexplored because of the limitations of existing methods for quantifying protein levels in single cells. To alleviate these limitations, we developed Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS), and validated its ability to identify distinct human cancer cell types based on their proteomes. We used SCoPE-MS to quantify thousands of proteins in differentiating mouse embryonic stem (ES) cells. The single-cell proteomes enabled us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicated coordinated mRNA and protein covariation. Yet many genes exhibited functionally concerted and distinct regulatory patterns at the mRNA and the protein levels, suggesting that post-transcriptional regulatory mechanisms contribute to proteome remodeling during lineage specification, especially for developmental genes. SCoPE-MS is broadly applicable to measuring proteome configurations of single cells and linking them to functional phenotypes, such as cell type and differentiation potentials.

2018-02-19 | MSV000082077 | MassIVE

Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

2017-01-23 | MSV000080489 | MassIVE

A hybrid single cell demultiplexing strategy that increases both cell recovery rate and calling accuracy

Project description:Recent advances in single cell RNA sequencing allow users to pool multiple samples into one run and demultiplex in downstream analysis, greatly increasing the experimental efficiency and cost-effectiveness. However, the expensive reagents for cell labeling, limited pooling capacity, non-ideal cell recovery rate and calling accuracy remain great challenges for this approach. To date, there are two major demultiplexing methods, antibody-based cell hashing and Single Nucleotide Polymorphism (SNP)-based genomic signature profiling, and each method has advantages and limitations. Here, we propose a hybrid demultiplexing strategy that increases calling accuracy and cell recovery at the same time. We first develop a computational algorithm that significantly increases calling accuracy of cell hashing. Next, we cluster all single cells based on their SNP profiles. Finally, we integrate results from both methods to make corrections and retrieve cells that are only identifiable in one method but not the other. By testing on several real-world datasets, we demonstrate that this hybrid strategy combines advantages of both methods, resulting in increased cell recovery and calling accuracy at lower cost.

2024-05-29 | GSE230810 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data