Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 497

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Quantitative assessment of fluorescent proteins

Open access
Cranfill, Paula J, Sell, Brittney R, Baird, Michelle A, Allen, John R, Lavagnino, Zeno, de Gruiter, H Martijn, Kremers, Gert-Jan, Davidson, Michael W, Ustione, Alessandro, Piston, David W·2016·Nature Methods

Abstract

The advent of fluorescent proteins (FPs) for genetic labeling of molecules and cells has revolutionized fluorescence microscopy. Genetic manipulations have created a vast array of bright and stable FPs spanning blue to red spectral regions. Common to autofluorescent FPs is their tight β-barrel structure, which provides the rigidity and chemical environment needed for effectual fluorescence. Despite the common structure, each FP has unique properties. Thus, there is no single 'best' FP for every circumstance, and each FP has advantages and disadvantages. To guide decisions about which FP is right for a given application, we have quantitatively characterized the brightness, photostability, pH stability and monomeric properties of more than 40 FPs to enable straightforward and direct comparison between them. We focus on popular and/or top-performing FPs in each spectral region.

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 · 10.1038/nmeth.3891

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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

Cites

Show all 2 references

Cited by

See all 497

Metadata

Authors
Cranfill, P. J., Sell, B. R., Baird, M. A., Allen, J. R., Lavagnino, Z., de Gruiter, H. M., Kremers, G.-J., Davidson, M. W., Ustione, A., & Piston, D. W.
Venue
Nature Methods
Published
2016-05-30
Volume
13
Issue
7
Pages
557-562
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W2416471421
Cited by
497
References
2
FWCI
31.20
Type
journal article
Language
en
Open access
green

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: Quantitative assessment of fluorescent proteins. https://app.cochat.ai/papers/10.1038/nmeth.3891
The paper
Cranfill, P. J., Sell, B. R., Baird, M. A., Allen, J. R., Lavagnino, Z., de Gruiter, H. M., Kremers, G.-J., Davidson, M. W., Ustione, A., & Piston, D. W. (2016). Quantitative assessment of fluorescent proteins. Nature Methods. https://doi.org/10.1038/nmeth.3891

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.