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; Melanie L. Sutton-Mcdowall; Jeremy G. Thompson; Ewa M. Goldys

doi:10.1038/srep23453

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, Melanie L. Sutton-Mcdowall, Jeremy G. Thompson, Ewa M. Goldys

^*Corresponding author for this work

ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP)

Research output: Contribution to journal › Article › peer-review

69 Citations (Scopus)

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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.

Original language	English
Article number	23453
Pages (from-to)	1-11
Number of pages	11
Journal	Scientific Reports
Volume	6
DOIs	https://doi.org/10.1038/srep23453
Publication status	Published - 31 Mar 2016

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Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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Gosnell, M. E., Anwer, A. G., Mahbub, S. B., Menon Perinchery, S., Inglis, D. W., Adhikary, P. P., Jazayeri, J. A., Cahill, M. A., Saad, S., Pollock, C. A., Sutton-Mcdowall, M. L., Thompson, J. G., & Goldys, E. M. (2016). Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features. Scientific Reports, 6, 1-11. Article 23453. https://doi.org/10.1038/srep23453

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title = "Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features",

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.",

author = "Gosnell, {Martin E.} and Anwer, {Ayad G.} and Mahbub, {Saabah B.} and {Menon Perinchery}, Sandeep and Inglis, {David W.} and Adhikary, {Partho P.} and Jazayeri, {Jalal A.} and Cahill, {Michael A.} and Sonia Saad and Pollock, {Carol A.} and Sutton-Mcdowall, {Melanie L.} and Thompson, {Jeremy G.} and Goldys, {Ewa M.}",

note = "Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",

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Gosnell, ME, Anwer, AG, Mahbub, SB, Menon Perinchery, S, Inglis, DW, Adhikary, PP, Jazayeri, JA, Cahill, MA, Saad, S, Pollock, CA, Sutton-Mcdowall, ML, Thompson, JG & Goldys, EM 2016, 'Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features', Scientific Reports, vol. 6, 23453, pp. 1-11. https://doi.org/10.1038/srep23453

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AU - Inglis, David W.

AU - Adhikary, Partho P.

AU - Jazayeri, Jalal A.

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AU - Saad, Sonia

AU - Pollock, Carol A.

AU - Sutton-Mcdowall, Melanie L.

AU - Thompson, Jeremy G.

AU - Goldys, Ewa M.

N1 - Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2016/3/31

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N2 - 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.

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

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