Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/FrVTNiCKGb --- #proteomics #prot-paper https://t.co/PXJE5eDnby
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP.
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Nov 2023 – Nov 2025Where it's discussed
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X / Twitter8 posts
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YouTube7 videos
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Bluesky4 posts
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News2 articles
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Blogs2 articles
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What people are saying
The discussions collectively praise the publication for its innovative approach to single-cell proteomics, with posts emphasizing the method's scalability, speed, and use of common lab materials. For example, users refer to the study as a 'preprint of great significance' and highlight specific figures, demonstrating both enthusiasm and detailed engagement that underscore its potential to transform proteomics research.
Most discussions express strong support or positive acknowledgment of the study, highlighting its significance in the field.
The topic clearly piques interest, with multiple posts emphasizing enthusiasm for the technical advancements.
Posts often reference specific figures like 'figure 5' or methodologies such as 'plexDIA,' indicating moderate but active engagement.
Many comments describe the method as a 'game changer' or 'significant,' reflecting perceived high impact on proteomics research.
Abstract in brief
- nPOP is a high-throughput method that allows the preparation of thousands of single-cell samples in tiny droplets on glass slides, making single-cell proteomics faster and more scalable.
- It uses mass spectrometry, specifically plexDIA, to accurately measure around 3000 to 3700 proteins per human cell, with the whole process taking only 1 or 2 days for over 3000 cells.
- The protocol is flexible, utilizes common lab materials, and includes quality control tools, making it easier for labs to adopt and expand for large-scale single-cell protein analysis.]
Top posts
Most liked on X and BlueskyMassively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/dRljOR5m3g --- #proteomics #prot-preprint https://t.co/GBhisslTel
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. https://t.co/u4cMhLv8ga
Great preprint of "Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP" from @slavovLab Look at the figure 5! https://t.co/JYGRzKDR7S
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP www.biorxiv.org/cont... --- #proteomics #prot-preprint
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/hjHMUQpBQc
All mentions
Single-cell Mass Spectrometry Proteomics Data Acquisition and Analysis Techniques
Workshop on single-cell mass spectrometry proteomic data acquisition using plexDIA and timsTOF, with initial interpretation via QuantQC, held at SCP2024. Focuses on improving data quality by minimizing protein leakage…
Advances in Single-Cell Mass Spectrometry Proteomics
Prof Nikolai Slavov discusses trends and challenges in single-cell mass spectrometry proteomics, focusing on massively parallel sample preparation and strategies to improve sensitivity and data depth for proteome…
Regulators of Protein Transport Revealed by Single-Nucleus Proteomics
Presentation by Dr. Jason Derks at SCP2024 discussing how singlenucleus proteomics identifies regulators of protein transport, leveraging multiplexed analysis to enhance proteome coverage and accuracy in small samples…
Parallel Sample Preparation for Single-Cell Proteomics Using nPOP
This workshop covers highly parallel sample preparation for multiplexed single-cell proteomics using nPOP by Dr. Josh Cantlon at SCP2024. It introduces CellenOne for isolating single-cell samples for mass spectrometry…
Advancements in Single-Cell Proteomics with plexDIA Methodology
The video discusses advancements in single-cell proteomics using mass spectrometry focusing on overcoming current limitations. plexDIA is highlighted as a framework for increasing throughput and sensitivity…
Enhancing Single-Cell Proteomics: Techniques and Data Interpretation
Parallel Sample Preparation for Single-Cell Proteomics Using nPOP
This workshop covers highly parallel sample prep for multiplexed single-cell proteomics with nPOP by Dr. Josh Cantlon. It introduces CellenOne for isolating single cells for LCMS analysis, demonstrating processing over…
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP www.nature.com/artic... --- #proteomics #prot-paper
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/FrVTNiCKGb --- #proteomics #prot-paper https://t.co/PXJE5eDnby
(BioRxiv All) Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/hlVkSFLjKY (RSS) #BioRxiv #MassSpecRSS https://t.co/UIVbKW7Rzm
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. https://t.co/u4cMhLv8ga
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/hjHMUQpBQc
RT @PastelBio: Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/dRljOR5m3g --- #proteo…
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP www.biorxiv.org/cont... --- #proteomics #prot-preprint
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/dRljOR5m3g --- #proteomics #prot-preprint https://t.co/GBhisslTel
Great preprint of "Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP" from @slavovLab Look at the figure 5! https://t.co/JYGRzKDR7S
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://t.co/r0NF8dCQgW #biorxiv_bioE
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://www.biorxiv.org/content/10.1101/2023.11.27.568927v1
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP https://www.biorxiv.org/content/10.1101/2023.11.27.568927v1