Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 187

The DOI matches its Crossref record.

Array-Based ELISAs for High-Throughput Analysis of Human Cytokines

Open access
Moody, M.D., Arsdell, S.W. Van, Murphy, K.P., Orencole, S.F., Burns, C.·2001·BioTechniques

Abstract

In this report, we describe the development of a mini-array system suitable for high-throughput quantification of proteins. This mini-array is a multiplexed, sandwich-type ELISA that measures the concentration of seven different human cytokines--TNF-alpha, IFN alpha, IFN gamma, IL-1 alpha, IL-1 beta, IL-6, and IL-10--from a single sample in each well of a 96-well plate. The mini-array is produced by spotting monoclonal antibodies (mAbs) in a 3 x 3 pattern in the bottom of the wells of 96-well polystyrene plates. Cytokines that are captured by the arrayed mAbs are detected by using biotinylated mAbs, followed by the addition of a streptavidin-horseradish peroxidase (HRP) conjugate and a chemiluminescent substrate. The light produced from the HRP-catalyzed oxidation of the substrate is measured at each spot in the array by imaging the entire plate with a commercially available CCD camera. Here, we demonstrate that these 96-well-plate format mini-arrays have performance characteristics that make them suitable for the high-throughput screening of anti-inflammatory compounds.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2144/01311dd03

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 187

Metadata

Authors
Moody, M. D., Arsdell, S. W. V., Murphy, K. P., Orencole, S. F., & Burns, C.
Venue
BioTechniques
Published
2001-07
Volume
31
Issue
1
Pages
186-194
ISSN
  • 0736-6205 (ISSN-L)
  • 1940-9818
OpenAlex
W1543099628
Cited by
187
References
0
FWCI
6.62
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Array-Based ELISAs for High-Throughput Analysis of Human Cytokines. https://app.cochat.ai/papers/10.2144/01311dd03
The paper
Moody, M. D., Arsdell, S. W. V., Murphy, K. P., Orencole, S. F., & Burns, C. (2001). Array-Based ELISAs for High-Throughput Analysis of Human Cytokines. BioTechniques. https://doi.org/10.2144/01311dd03

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.