Evidence map›Paper›PMID 22038904›Full record

ArticleProteomics2011

Sensitive single-molecule protein quantification and protein complex detection in a microarray format.

Lee A Tessler, Robi D Mitra

Abstract read
In one paragraph

Article in Proteomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
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

2 authors.

Lee A TesslerDepartment of Genetics, Center for Genome Sciences and Systems Biology, Washington University, St. Louis, MO 63108, USA.
Robi D Mitra

Funding

METHYLMAP: A TECHNOLOGY TO ANALYZE PROMOTER METHYLATION IN MICRODISSECTED CELLSR01DA025744 · NIDA · WASHINGTON UNIVERSITY · PI MITRA, ROBI D · 2008 to 2011
$1.7M
HIGHLY PARALLEL EDMAN SEQUENCING OF INDIVIDUAL PEPTIDE MOLECULESR21CA151197 · NCI · WASHINGTON UNIVERSITY · PI MITRA, ROBI D · 2010 to 2012
$648k
NCI NIH HHS 1R21CA151197-01NCI NIH HHS R21 CA151197NIDA NIH HHS 1R01DA025744-01NIDA NIH HHS R01 DA025744
6 · The paper itself

Abstract

Single-molecule protein analysis provides sensitive protein quantitation with a digital read-out and is promising for studying biological systems and detecting biomarkers clinically. However, current single-molecule platforms rely on the quantification of one protein at a time. Conventional antibody microarrays are scalable to detect many proteins simultaneously, but they rely on less sensitive and less quantitative quantification by the ensemble averaging of fluorescent molecules. Here, we demonstrate a single-molecule protein assay in a microarray format enabled by an ultra-low background surface and single-molecule imaging. The digital read-out provides a highly sensitive, low femtomolar limit of detection and four orders of magnitude of dynamic range through the use of hybrid digital-analog quantification. From crude cell lysate, we measured levels of p53 and MDM2 in parallel, proving the concept of a digital antibody microarray for use in proteomic profiling. We also applied the single-molecule microarray to detect the p53-MDM2 protein complex in cell lysate. Our study is promising for development and application of single-molecule protein methods because it represents a technological bridge between single-plex and highly multiplex studies.

Indexed as

Cell Line, TumorHumansMultiprotein ComplexesProtein Array AnalysisProteinsProto-Oncogene Proteins c-mdm2Tumor Suppressor Protein p53MDM2 protein, humanMultiprotein ComplexesProteinsProto-Oncogene Proteins c-mdm2TP53 protein, humanTumor Suppressor Protein p53

Identifiers

PMID22038904
PMCPMC5396177

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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.