Reference data

TitleQuantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features
AuthorMartin E. Gosnell 1,2 Ayad G. Anwer 2 Saabah B. Mahbub 2 Sandeep Menon Perinchery 2 David W. Inglis 2 Partho P. Adhikary 3 Jalal A. Jazayeri 3 Michael A. Cahill 3 Sonia Saad 4 Carol A. Pollock 4 Melanie L. Sutton-McDowall 5,6 Jeremy G. Thompson 5,6 and Ewa M. Goldysb 2
Affiliation(s)1 Quantitative Pty Ltd ABN 17165684186, Beaumont Hills NSW 2155, Australia. 2 ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde 2109, NSW Australia 3 School of Biomedical Sciences, Charles Sturt University, Wagga, Wagga, NSW, 2678, Australia 4 Kolling Institute of Medical Research, Royal North Shore Hospital/Northern Clinical School, University of Sydney, Pacific Hwy, St Leonards NSW 2065, Australia 5 Robinson Research Institute, School of Paediatrics and Reproductive Health, The University of Adelaide, Medical School, Frome Road, Adelaide, South Australia, 5005, Australia 6 Australian Research Council Centre of Excellence for Nanoscale Biophotonics and Institute for Photonics and Advanced Sensing, The University of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
PublishedSci Rep. 2016; 6: 23453. doi: 10.1038/srep23453
Keywordmulti-LED light source
SnippetExcitation wavelengths were supplied by low cost multi-LED light source (Mic-LED Light Source) from Prizmatix Ltd., Givat-Shmuel ...
AbstractAutomated 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.


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