Evidence map›Paper›PMID 33382587›Full record

ArticleAccounts of chemical research2021

Direct Kinetic Fingerprinting for High-Accuracy Single-Molecule Counting of Diverse Disease Biomarkers.

Shankar Mandal, Zi Li, Tanmay Chatterjee, Kunal Khanna, Karen Montoya, Liuhan Dai, Chandler Petersen, Lidan Li, Muneesh Tewari, Alexander Johnson-Buck and 1 more

Open access · greenAbstract read
In one paragraph

Article in Accounts of chemical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.2field-weighted citation impact, top 11% 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

18 citing papers in PubMed, 57 citations in OpenAlex.

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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

11 authors at 1 institution in 1 country.

Shankar Mandal
Zi Li
Tanmay Chatterjee
Kunal Khanna
Karen Montoya
Liuhan Dai
Chandler Petersen
Lidan Li
Muneesh Tewari
Alexander Johnson-BuckORCID 0000-0003-3748-6000
University of Michigan · US

Funding

The RNA nanomachines of the gene expression machinery dissected at the single molecule levelR35GM131922 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NILS G WALTER · 2019 to 2026
$6.9M
University of Michigan Postbaccalaureate Research Education Program (UM PREP)R25GM086262 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALLEN, BENJAMIN · 2009 to 2024
$6.1M
Michigan Chemistry-Biology Interface Training ProgramT32GM132046 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Emily E Scott, Wenjing Wang · 2019 to 2026
$4.1M
Optimization and Validation of Single-Molecule Kinetic Fingerprinting Technology for Rapid, Ultra-Specific Detection of Cancer MutationsR33CA229023 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI TEWARI, MUNEESH, WALTER, NILS G · 2018 to 2020
$1.2M
Single-molecule counting of cancer biomarker miRNAs in human biofluidsR21CA204560 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WALTER, NILS G · 2017 to 2019
$614k
Highly specific, amplification-free, single-molecule counting of rare methylated DNA cancer biomarkersR21CA225493 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI TEWARI, MUNEESH, WALTER, NILS G · 2020 to 2020
$609k
NCI NIH HHS R21 CA204560NCI NIH HHS R21 CA225493NCI NIH HHS R33 CA229023NIGMS NIH HHS R25 GM086262NIGMS NIH HHS R35 GM131922NIGMS NIH HHS T32 GM132046
6 · The paper itself

Abstract

Methods for detecting and quantifying disease biomarkers in biofluids with high specificity and sensitivity play a pivotal role in enabling clinical diagnostics, including point-of-care tests. The most widely used molecular biomarkers include proteins, nucleic acids, hormones, metabolites, and other small molecules. While numerous methods have been developed for analyzing biomarkers, most techniques are challenging to implement for clinical use due to insufficient analytical performance, high cost, and/or other practical shortcomings. For instance, the detection of cell-free nucleic acid (cfNA) biomarkers by digital PCR and next-generation sequencing (NGS) requires time-consuming nucleic acid extraction steps, often introduces enzymatic amplification bias, and can be costly when high specificity is required. While several amplification-free methods for detecting cfNAs have been reported, these techniques generally suffer from low specificity and sensitivity. Meanwhile, the quantification of protein biomarkers is generally performed using immunoassays such as enzyme-linked immunosorbent assay (ELISA); the analytical performance of these methods is often limited by the availability of antibodies with high affinity and specificity as well as the significant nonspecific binding of antibodies to assay surfaces. To address the drawbacks of existing biomarker detection methods and establish a universal diagnostics platform capable of detecting different types of analytes, we have developed an amplification-free approach, named single-molecule recognition through equilibrium Poisson sampling (SiMREPS), for the detection of diverse biomarkers with arbitrarily high specificity and single-molecule sensitivity. SiMREPS utilizes the transient, reversible binding of fluorescent detection probes to immobilized target molecules to generate kinetic fingerprints that are detected by single-molecule fluorescence microscopy. The analysis of these kinetic fingerprints enables nearly perfect discrimination between specific binding to target molecules and any nonspecific binding. Early proof-of-concept studies demonstrated the

Indexed as

BiomarkersCell LineDeep LearningFluorescent DyesHumansKineticsLimit of DetectionMicroRNAsProteinsReal-Time Polymerase Chain ReactionSingle Molecule ImagingBiomarkersFluorescent DyesMicroRNAsProteins

Identifiers

PMID33382587
PMCPMC8752314
OpenAlexW3115324994

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.