Evidence map›Paper›PMID 33688052›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis.

Ke Xia, James T Hagan, Li Fu, Brian S Sheetz, Somdatta Bhattacharya, Fuming Zhang, Jason R Dwyer, Robert J Linhardt

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Glycan Sequencing, A Brief Primer.Glycoscience & therapy · 2026
    Article
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  6. Review
  7. Article
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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

8 authors.

Ke XiaDepartment of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180-3590.ORCID 0000-0001-9699-9901
James T HaganDepartment of Chemistry, University of Rhode Island, Kingston, RI 02881.ORCID 0000-0001-8125-8596
Li FuCenter for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180-3590.ORCID 0000-0001-9935-3874
Brian S SheetzDepartment of Chemistry, University of Rhode Island, Kingston, RI 02881.ORCID 0000-0002-7456-9692
Somdatta BhattacharyaHoward P. Isermann Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180.
Fuming ZhangHoward P. Isermann Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180.
Jason R DwyerDepartment of Chemistry, University of Rhode Island, Kingston, RI 02881; jason_dwyer@uri.edu linhar@rpi.edu.ORCID 0000-0002-2938-2888
Robert J LinhardtDepartment of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180-3590; jason_dwyer@uri.edu linhar@rpi.edu.

Funding

Endothelial glycocalyx reconstitution during sepsisR01HL125371 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI LINHARDT, ROBERT J, SCHMIDT, ERIC PETER · 2014 to 2018
$3.4M
The role ofglycosaminoglycan N-sulfation in glomerular biology/pathobiologyR01DK111958 · NIDDK · LOUISIANA STATE UNIV HSC SHREVEPORT · PI MCCARTHY, KEVIN JOHN · 2017 to 2021
$2.6M
An Automated Platform for the CE-MS Analysis of GlycosaminoglycansU01CA231074 · NCI · UNIVERSITY OF GEORGIA · PI AMSTER, I JONATHAN · 2018 to 2020
$895k
NCI NIH HHS U01 CA231074NHLBI NIH HHS R01 HL125371NIDDK NIH HHS R01 DK111958
6 · The paper itself

Abstract

The application of solid-state (SS) nanopore devices to single-molecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have been surprising, motivating us to examine the potential use of an SS nanopore to analyze synthetic heparan sulfate GAG chains of controlled composition and sequence prepared through a promising, recently developed chemoenzymatic route. A minimal representation of the nanopore data, using only signal magnitude and duration, revealed, by eye and image recognition algorithms, clear differences between the signals generated by four synthetic GAGs. By subsequent machine learning, it was possible to determine disaccharide and even monosaccharide composition of these four synthetic GAGs using as few as 500 events, corresponding to a zeptomole of sample. These data suggest that ultrasensitive GAG analysis may be possible using SS nanopore detection and well-characterized molecular training sets.

Indexed as

Machine LearningNanoporesCarbohydrate SequenceDisaccharidesGlycomicsHeparan SulfateMonosaccharidesDisaccharidesHeparan SulfateMonosaccharidesglycosaminoglycanpolysaccharidesequencingsingle-molecule analysissolid-state nanopore

Identifiers

PMID33688052
PMCPMC7980385

What OpenQuestion holds

Textmetadata
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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.