Statistically strong label-free quantitative identification of native fluorophores in a biological sample.

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

SUBMITTER: Mahbub SB 

PROVIDER: S-EPMC5693869 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature