Introduction

Confocal laser scanning microscopy (CLSM) with fluorescence allows for simultaneous three-dimensional imaging, deep imaging, live cell imaging, and quantitative imaging of a biological target. Prior to imaging, the targets are stained with various fluorescent dyes, used also as labels during multi-species fluorescence imaging. Conventionally, simultaneous multi-species CLSM imaging is carried out by isolating the resulting fluorescent signal from the excitation beam, and diving this into a series of spectral components using a combination of filters, dichroic mirrors and/or adjustable acousto-optic filters. A detector is then used to record each spectral band. This technique, however has a couple of shortcomings as the number of detectors required increases with the number of probes, and the cross-talk between the emission spectra of different probes degrades the image quality.

Technical features

A multi-species fluorescence microscope has been configured to illuminate a sample with a plurality of pulsed excitation light beams comprising different excitation spectral components has been developed. The microscope consists of i) a single-photon detector configured to detect a fluorescence signal emitted by a sample, ii) a fluorescence signal comprising different spectral components, iii) an excitation spectrum encoder configured to impose a respective time delay on each excitation spectral component, iv) an emission spectrum encoder that can impose a respective time delay on each emission spectral component, and v) a multi-species decoder configured to decode the excitation spectrum, the emission spectrum and the fluorescence decay curve for the fluorescent species contained in the sample analysed.

Possible Applications

• Biological imagining;
• Laser microscopy applications.

Advantages

• Compatible with current CLSM systems;
• Compatible with ISM approaches;
• Allows full exploration of the three main probe signatures alone and in combination in order to achieve multi-species imaging in a scalable and simple manner;
• Compatible with known spectral unmixing, deconvolution, phasor, fitting, and machine learning algorithms.