Evidence map›Paper›PMID 25218182›Full record

ArticleNature methods2014

Nanoparticle vesicle encoding for imaging and tracking cell populations.

Paul Rees, John W Wills, M Rowan Brown, James Tonkin, Mark D Holton, Nicole Hondow, Andrew P Brown, Rik Brydson, Val Millar, Anne E Carpenter and 1 more

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Nature methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
6.6field-weighted citation impact, top 4% 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

13 citing papers in PubMed, 31 citations in OpenAlex.

  1. Article
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  4. Imaging flow cytometry: a primer.Nature reviews. Methods primers · 2022
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  7. Article
  8. Single-Nanoparticle Cell Barcoding by Tunable FRET from Lanthanides to Quantum Dots.Angewandte Chemie (International ed. in English) · 2018
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
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 4 institutions in 2 countries.

Paul Rees1] Centre for Nanohealth, School of Engineering, Swansea University, Swansea, UK. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
John W WillsCentre for Nanohealth, School of Engineering, Swansea University, Swansea, UK.
M Rowan BrownCentre for Nanohealth, School of Engineering, Swansea University, Swansea, UK.
James TonkinCentre for Nanohealth, School of Engineering, Swansea University, Swansea, UK.
Mark D HoltonCentre for Nanohealth, School of Engineering, Swansea University, Swansea, UK.
Nicole HondowInstitute for Materials Research, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds, UK.
Andrew P BrownInstitute for Materials Research, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds, UK.
Rik BrydsonInstitute for Materials Research, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds, UK.
Val MillarGeneral Electric Healthcare, The Maynard Centre, Cardiff, UK.
Anne E CarpenterBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Huw D SummersCentre for Nanohealth, School of Engineering, Swansea University, Swansea, UK.
Swansea University · GBUniversity of Leeds · GBBroad Institute · USGeneral Electric (United Kingdom) · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For phenotypic behavior to be understood in the context of cell lineage and local environment, properties of individual cells must be measured relative to population-wide traits. However, the inability to accurately identify, track and measure thousands of single cells via high-throughput microscopy has impeded dynamic studies of cell populations. We demonstrate unique labeling of cells, driven by the heterogeneous random uptake of fluorescent nanoparticles of different emission colors. By sequentially exposing a cell population to different particles, we generated a large number of unique digital codes, which corresponded to the cell-specific number of nanoparticle-loaded vesicles and were visible within a given fluorescence channel. When three colors are used, the assay can self-generate over 17,000 individual codes identifiable using a typical fluorescence microscope. The color-codes provided immediate visualization of cell identity and allowed us to track human cells with a success rate of 78% across image frames separated by 8 h.

Indexed as

Fluorescent DyesQuantum DotsCell LineCell TrackingHumansMicroscopy, FluorescenceFluorescent Dyes

Identifiers

PMID25218182
OpenAlexW2087037704

What OpenQuestion holds

Textmetadata
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.