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Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features
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Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features
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Journal Article
Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features
2016
Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group
Subject
/ Animals
/ Cancer
/ Cell Tracking - instrumentation
/ Diabetes Mellitus, Experimental - genetics
/ Diabetes Mellitus, Experimental - metabolism
/ Diabetes Mellitus, Experimental - pathology
/ Embryos
/ Enzymes
/ Humanities and Social Sciences
/ Humans
/ Image Processing, Computer-Assisted - statistics & numerical data
/ Membrane Proteins - genetics
/ Membrane Proteins - metabolism
/ Mice
/ Mutation
/ Optical Imaging - statistics & numerical data
/ Pancreatic Neoplasms - genetics
/ Pancreatic Neoplasms - metabolism
/ Pancreatic Neoplasms - ultrastructure
/ Receptors, Progesterone - genetics
/ Receptors, Progesterone - metabolism
/ Science
