Evidence map›Paper›PMID 27029742›Full record

ArticleScientific reports2016

Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features.

Martin E Gosnell, Ayad G Anwer, Saabah B Mahbub, Sandeep Menon Perinchery, David W Inglis, Partho P Adhikary, Jalal A Jazayeri, Michael A Cahill, Sonia Saad, Carol A Pollock and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
16.6field-weighted citation impact, top 1% 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

36 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 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

13 authors at 4 institutions in 1 country.

Martin E GosnellQuantitative Pty Ltd ABN 17165684186, Beaumont Hills NSW 2155, Australia.
Ayad G AnwerARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde 2109, NSW Australia.
Saabah B MahbubARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde 2109, NSW Australia.
Sandeep Menon PerincheryARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde 2109, NSW Australia.
David W InglisARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde 2109, NSW Australia.
Partho P AdhikarySchool of Biomedical Sciences, Charles Sturt University, Wagga Wagga, NSW, 2678, Australia.
Jalal A JazayeriSchool of Biomedical Sciences, Charles Sturt University, Wagga Wagga, NSW, 2678, Australia.
Michael A CahillSchool of Biomedical Sciences, Charles Sturt University, Wagga Wagga, NSW, 2678, Australia.
Sonia SaadKolling Institute of Medical Research, Royal North Shore Hospital/Northern Clinical School, University of Sydney, Pacific Hwy, St Leonards NSW 2065, Australia.
Carol A PollockKolling Institute of Medical Research, Royal North Shore Hospital/Northern Clinical School, University of Sydney, Pacific Hwy, St Leonards NSW 2065, Australia.
Melanie L Sutton-McDowallRobinson Research Institute, School of Paediatrics and Reproductive Health, The University of Adelaide, Medical School, Frome Road, Adelaide, South Australia, 5005, Australia.
Jeremy G ThompsonRobinson Research Institute, School of Paediatrics and Reproductive Health, The University of Adelaide, Medical School, Frome Road, Adelaide, South Australia, 5005, Australia.
Ewa M GoldysARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde 2109, NSW Australia.
Macquarie University · AUCharles Sturt University · AUAustralian Research Council · AURoyal North Shore Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biology and medicine. Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However extracting high content information from autofluorescence imaging has been hitherto impossible. Here, we quantitatively characterise cell populations in different tissue types, live or fixed, by using novel image processing and a simple multispectral upgrade of a wide-field fluorescence microscope. Our optimal discrimination approach enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent. Label-free classifications are validated by the analysis of Classification Determinant (CD) antigen expression. The versatility of our method is illustrated by detecting genetic mutations in cancer, non-invasive monitoring of CD90 expression, label-free tracking of stem cell differentiation, identifying stem cell subpopulations with varying functional characteristics, tissue diagnostics in diabetes, and assessing the condition of preimplantation embryos.

Indexed as

MutationAnimalsBlastocystCell DifferentiationCell Line, TumorCell TrackingDiabetes Mellitus, ExperimentalGene ExpressionGene Expression RegulationHumansImage Processing, Computer-AssistedMembrane ProteinsMiceOptical ImagingPancreatic NeoplasmsReceptors, ProgesteroneMembrane ProteinsPGRMC1 protein, mouseReceptors, ProgesteroneThy-1 Antigens

Identifiers

PMID27029742
PMCPMC4814840
OpenAlexW2316575571

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.