Evidence map›Paper›PMID 32639140›Full record

ArticleAnalytical chemistry2020

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography-Mass Spectrometry for High-Throughput Single-Cell Proteomics.

Sarah M Williams, Andrey V Liyu, Chia-Feng Tsai, Ronald J Moore, Daniel J Orton, William B Chrisler, Matthew J Gaffrey, Tao Liu, Richard D Smith, Ryan T Kelly and 2 more

Open access · greenAbstract read
In one paragraph

Article in Analytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed
11.8field-weighted citation impact, top 1% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

85 citing papers in PubMed, 162 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Accelerated protein digestion by numbering-up nanochannels, followed by LC-MS analysis.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026
    Article
  5. Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Oocyte mitochondria link maternal environment to offspring phenotype.bioRxiv : the preprint server for biology · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Innovative Advances in Droplet Microfluidics.Research (Washington, D.C.) · 2025
    Review
  18. Review
  19. Spatial Proteomics towards cellular Resolution.Expert review of proteomics · 2024
    Article
  20. Article

25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Sarah M WilliamsEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.
Andrey V LiyuEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.
Chia-Feng TsaiBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.ORCID 0000-0002-6514-6911
Ronald J MooreBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.ORCID 0000-0003-2806-2855
Daniel J OrtonBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.
William B ChrislerBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.
Matthew J GaffreyBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.
Tao LiuBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.ORCID 0000-0001-9529-6550
Richard D SmithBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.
Ryan T KellyEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.ORCID 0000-0002-3339-4443
Ljiljana Pasa-TolicEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.
Ying ZhuEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354 United States.ORCID 0000-0002-5416-0566
Pacific Northwest National Laboratory · USBrigham Young University · US

Funding

Equipment Supplement Request for a Research Resource for Ultra-sensitive High Throughput ProteomicsP41GM103493 · NIGMS · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI SMITH, RICHARD D · 2012 to 2022
$22.6M
PNNL Proteome Characterization CenterU24CA210955 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI LIU, TAO, SMITH, RICHARD D · 2016 to 2020
$5.3M
NCI NIH HHS U24 CA210955NIGMS NIH HHS P41 GM103493
6 · The paper itself

Abstract

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. We have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ∼77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

Indexed as

Analytic Sample Preparation MethodsAutomationCell Line, TumorChromatography, LiquidHumansMass SpectrometryNanotechnologyProteomicsSingle-Cell Analysis

Identifiers

PMID32639140
PMCPMC7793572
OpenAlexW3041326933

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.