Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Increasing the throughput of sensitive proteomics by plexDIA

ABSTRACT: Mass-spectrometry methods enable high-throughput proteomics with large input samples, but the depth and throughput of protein analysis remain limited for smaller samples. We aimed to increase throughput for analyzing limited samples while achieving high proteome coverage and quantitative accuracy. Thus, we developed a general experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing protein coverage or quantitative accuracy. Specifically, 3-plex nonisobaric labeling of sub-microgram samples increases the number of quantitative protein ratios by about 3-fold, enabling the quantification of over 25,000 protein data points per hour of active gradient on a first-generation Q Exactive instrument. Furthermore, plexDIA increases the consistency of protein detection and quantification across samples and reduces missing data by over 2-fold. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content. The high sensitivity and accuracy of plexDIA detected many classical cell cycle proteins and discovered new ones. These results establish a general framework for increasing the throughput of highly sensitive and quantitative protein analysis.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Escherichia Coli (ncbitaxon:562) Saccharomyces Cerevisiae (ncbitaxon:4932) Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Nikolai Slavov

PROVIDER: MSV000088302 | MassIVE | Tue Nov 02 15:14:00 GMT 2021

SECONDARY ACCESSION(S): PXD029531

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Increasing the throughput of sensitive proteomics by plexDIA

Project description:Current mass-spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using 3-plex nonisobaric mass tags, plexDIA enables quantifying 3-fold more protein ratios among nanogram-level samples. Using 1 hour active gradients and first-generation Q Exactive, plexDIA quantified about 8,000 proteins in each sample of labeled 3-plex sets. plexDIA also increases data completeness, reducing missing data over 2-fold across samples. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content; plexDIA detected many classical cell cycle proteins and discovered new ones. When applied to single human cells, plexDIA quantified about 1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using about 5 min of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

2022-03-21 | MSV000089093 | MassIVE

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (tubulointerstitium)

Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.

2013-08-06 | E-GEOD-47184 | biostudies-arrayexpress

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (glomeruli)

2013-08-06 | E-GEOD-47183 | biostudies-arrayexpress

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

Project description:Application of a quantitative framework to improve the accuracy of a bacterial infection model

| PRJNA909326 | ENA

Strategies for increasing the depth and throughput of protein analysis by plexDIA

Project description:Raw files, spectral library, DIA-NN and Dinosaur outputs for "Strategies for increasing the depth and throughput of protein analysis by plexDIA"

2022-11-04 | MSV000090650 | MassIVE

A proximity proteomics pipeline with improved reproducibility and throughput

Project description:Proximity labeling coupled with mass spectrometry (proximity proteomics) have emerged as a powerful technique to map proximal protein interactions in living cells. However, the large-scale and unbiased sample analysis in proximity proteomics necessitates a universal and high-throughput workflow to reduce hands-on time and increase quantitative reproducibility. To address this issue, we developed a systematic and automated approach, including generation and characterization of monoclonal cell lines, automated enrichment of biotinylated proteins in a 96-well format, optimization of quantitative mass spectrometry acquisition methods, and advanced computational framework to deconvolute spatial and temporal protein interaction networks. The quantitative reproducibility and accuracy of this streamlined workflow clearly outperforms the conventional manual enrichment. We first applied this approach to investigate subcellular proteomics, including cytosol, endosome, Golgi, lysosome, and plasma membrane. Moreover, using serotonin receptor (5HT2A) as a model, we mapped dynamic protein-protein interactions of GPCR induced by agonist. Collectively, this systematic approach could be widely used in proximity proteomics with high throughput, high reproducibility, and minimal manual intervention.

2024-07-26 | PXD040762 | Pride

mDRIP-seq is a high-throughput method for quantitative R-loop landscape profiling

Project description:Current methods for R-loop profiling need to perform experiments for each sample individually, with consequent limitations in throughput. Here, based on the barcoding strategy, we develop mDRIP-seq, a high-throughput method showing equivalent performance as conventional methods, but with merits of 7-fold less cost and 6-fold less hand-on time per sample. We also show the simplicity and effectiveness of mDRIP-seq for relative and absolute quantitation of genomic R-loop fractions for multiple samples. Together, mDRIP-seq is a high-throughput and cost-efficient method for R-loop mapping and quantitative assessment that can be widely applied to large-scale dynamic profiles of these important structures for diverse organisms.

2024-06-11 | GSE219071 | GEO

Selection of Heating Temperatures Improves the Sensitivity of Proteome Integral Solubility Alteration (PISA) Assay

Project description:The thermal shift assay is a robust method of discovering protein-ligand interactions by measuring the alterations in protein thermal stability under various conditions. Several thermal shift assays have been developed and their throughput has been advanced greatly by the rapid progress in tandem mass tag-based quantitative proteomics. A recent paper by Gaetani et al. (J Proteome Res 2019, 18 (11), 4027-4037) introduced proteome integral solubility alteration (PISA) assay, further increasing throughput and simplifying the data analysis. Yet, it remains unclear how fold changes (integral treated samples versus integral control samples) perform in this assay. We show that fold changes have compromised linearity with ΔTm (shift in melting points) by simulation, and the magnitudes of the fold changes are inherently small in PISA assay, which is a challenge for quantitation. Both simulation and experimental results show that the selection of heating temperatures can tackle the small fold change problem and improve the sensitivity and specificity of the PISA assay.

2020-04-06 | PXD017346 | Pride

Project description:High-throughput accuracy for multiplex amplicon sequencing

| PRJNA245467 | ENA