ArticleScientific reports2017
Statistically strong label-free quantitative identification of native fluorophores in a biological sample.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 39 citations in OpenAlex.
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- Utilising Hyperspectral Autofluorescence Imaging in the Objective Assessment of Disease State and Pain in Patients with Rheumatoid Arthritis.International journal of molecular sciences · 2024Article
- Single cell, Label free Characterisation of Human Mesenchymal Stromal cell Stemness and Future Growth Potential by Autofluorescence Multispectral Imaging.Stem cell reviews and reports · 2024Article
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- Ageing and Polypharmacy in Mesenchymal Stromal Cells: Metabolic Impact Assessed by Hyperspectral Imaging of Autofluorescence.International journal of molecular sciences · 2024Article
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- Optical imaging detects metabolic signatures associated with oocyte quality†.Biology of reproduction · 2022Article
- Multispectral autofluorescence characteristics of reproductive aging in old and young mouse oocytes.Biogerontology · 2022Article
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- Non-invasive assessment of exfoliated kidney cells extracted from urine using multispectral autofluorescence features.Scientific reports · 2021Article
- Autofluorescence-based sorting removes senescent cells from mesenchymal stromal cell cultures.Scientific reports · 2020Article
- Non-invasive real-time imaging of reactive oxygen species (ROS) using auto-fluorescence multispectral imaging technique: A novel tool for redox biology.Redox biology · 2020Article
- NADCell reports · 2020Article
- Non-destructive, label free identification of cell cycle phase in cancer cells by multispectral microscopy of autofluorescence.BMC cancer · 2019Article
- Non-Invasive Monitoring of Functional State of Articular Cartilage Tissue with Label-Free Unsupervised Hyperspectral Imaging.Scientific reports · 2019Article
- Tissue Intrinsic Fluorescence Spectra-Based Digital Pathology of Liver Fibrosis by Marker-Controlled Segmentation.Frontiers in medicine · 2018Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioimaging using endogenous cell fluorescence, without any external biomarkers makes it possible to explore cells and tissues in their original native state, also in vivo. In order to be informative, this label-free method requires careful multispectral or hyperspectral recording of autofluorescence images followed by unsupervised extraction (unmixing) of biochemical signatures. The unmixing is difficult due to the scarcity of biochemically pure regions in cells and also because autofluorescence is weak compared with signals from labelled cells, typically leading to low signal to noise ratio. Here, we solve the problem of unsupervised hyperspectral unmixing of cellular autofluorescence by introducing the Robust Dependent Component Analysis (RoDECA). This approach provides sophisticated and statistically robust quantitative biochemical analysis of cellular autofluorescence images. We validate our method on artificial images, where the addition of varying known level of noise has allowed us to quantify the accuracy of our RoDECA analysis in a way that can be applied to real biological datasets. The same unsupervised statistical minimisation is then applied to imaging of mouse retinal photoreceptor cells where we establish the identity of key endogenous fluorophores (free NADH, FAD and lipofuscin) and derive the corresponding molecular abundance maps. The pre-processing methodology of image datasets is also presented, which is essential for the spectral unmixing analysis, but mostly overlooked in the previous studies.
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