ArticleAnalytical chemistry2021
Fully Automated Sample Processing and Analysis Workflow for Low-Input Proteome Profiling.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 69 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it, 122 citations in OpenAlex.
- Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments.Nature methods · 2023Guideline
- Optimized Sample Handling Minimizes Peptide Adsorption to Plastics to Enable High Sensitivity Evosep Based Chemical Proteomics.Proteomics · 2026Article
- Optimization of trap column properties and loading conditions for proteome profiling of single-cell-level sample inputs.Analytical and bioanalytical chemistry · 2026Article
- Single-Cell Proteomic Technologies: Tools in the Quest for Principles.Annual review of biophysics · 2026Review
- High-Throughput Proteomics Sample Preparation Using a 96-Channel Pipettor and Magnetic Pin Device.Journal of proteome research · 2026Article
- Modification of a Low-Cost Pipetting Robot for Nanoliter Liquid Handling and Autosampling for Liquid Chromatography-Mass Spectrometry.Journal of separation science · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- High-Throughput Label-free Single-Cell Proteomics Enabled by Multicolumn NanoLC with a 5-min Cycle Time.Research square · 2025Article
- The $10 proteome: low-cost, deep and quantitative proteome profiling of limited sample amounts using the Orbitrap Astral and timsTOF Ultra 2 mass spectrometers.bioRxiv : the preprint server for biology · 2025Article
- Enhancing Tandem MS Sensitivity and Peptide Identification via Ion Preaccumulation in an Orbitrap Mass Spectrometer.Journal of proteome research · 2025Article
- Mass Spectrometry-based Solutions for Single-cell Proteomics.Genomics, proteomics & bioinformatics · 2025Review
- Trends in Mass Spectrometry-Based Single-Cell Proteomics.Analytical chemistry · 2025Review
- Mass Spectrometry-Based Proteomics in Clinical Diagnosis of Amyloidosis and Multiple Myeloma: A Review (2012-2024).Journal of mass spectrometry : JMS · 2025Review
- Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity.Nature methods · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Advancements in Single-Cell Proteomics and Mass Spectrometry-Based Techniques for Unmasking Cellular Diversity in Triple Negative Breast Cancer.Proteomics. Clinical applications · 2025Review
- Digital Microfluidics for Sample Preparation in Low-Input Proteomics.Small methods · 2025Review
- Spatial Proteomics towards cellular Resolution.Expert review of proteomics · 2024Article
- Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP.Nature protocols · 2024Article
- Gradient-Elution Nanoflow Liquid Chromatography Without a Binary Pump: Smoothed Step Gradients Enable Reproducible, Sensitive, and Low-Cost Separations for Single-Cell Proteomics.Molecular & cellular proteomics : MCP · 2024Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Recent advances in sample preparation and analysis have enabled direct profiling of protein expression in single mammalian cells and other trace samples. Several techniques to prepare and analyze low-input samples employ custom fluidics for nanoliter sample processing and manual sample injection onto a specialized separation column. While being effective, these highly specialized systems require significant expertise to fabricate and operate, which has greatly limited implementation in most proteomic laboratories. Here, we report a fully automated platform termed autoPOTS (automated preparation in one pot for trace samples) that uses only commercially available instrumentation for sample processing and analysis. An unmodified, low-cost commercial robotic pipetting platform was utilized for one-pot sample preparation. We used low-volume 384-well plates and periodically added water or buffer to the microwells to compensate for limited evaporation during sample incubation. Prepared samples were analyzed directly from the well plate with a commercial autosampler that was modified with a 10-port valve for compatibility with 30 μm i.d. nanoLC columns. We used autoPOTS to analyze 1-500 HeLa cells and observed only a moderate reduction in peptide coverage for 150 cells and a 24% reduction in coverage for single cells compared to our previously developed nanoPOTS platform. To evaluate clinical feasibility, we identified an average of 1095 protein groups from ∼130 sorted B or T lymphocytes. We anticipate that the straightforward implementation of autoPOTS will make it an attractive option for low-input and single-cell proteomics in many laboratories.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.