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Spectral flow cytometric FRET: Towards a hyper dimensional flow cytometry

Photons can convey information via their energy (or color), polarization, coherence, and timing. Energetics of molecules is dealt with spectroscopy, and the absorption and emission spectra act as fingerprints of molecules, which reflect changes in structure and environment with high sensitivity, being the spectra continuous in general. In conventional flow cytometry detection of fluorescence color has been mainly restricted to the detecting just a few discrete wavelength ranges (bands) often separated by gaps like “missing teeth.” The enhanced information content meant by continuity of spectra has been first exploited in imaging when the output of a scanning microscope has been fed into a spectrograph and the whole emission spectrum has been recorded with a point detector at each pixel of the image [1, 2]. Recording emission spectra can also be parallelized by dispersing and projecting the light by a prism onto a pixel array of a CCD camera, onto an array of photomultipliers (PMTs), alternatively onto a multicathode PMT, through micro lenses [3, 4]. This latter method has also been applied in flow cytometry, where serially dispersive methods are not feasible due to the short dwell time of cells in the illuminating light beam. The finesse of the spectra on the wavelength scale is determined by the quality of the dispersive element and the number of detectors, or number of pixels in a CCD array. The fact that whole spectra are now recorded per cell in a flow cytometer, in dozens of channels instead of only a few, however, has drastically changed the attitude towards data analysis [5–7]. Classically fluorescence light is detected via just a few discrete channels from some fluorophores. However, due to the substantial width of emission spectra of the fluorophores, the contents of the different channels are not uniquely characterizing the individual fluorophores, but they are often characteristic mixtures of the individual emissions. Cleaning of signals of the different channels has been accomplished in hardware as well as software levels. At the hardware-, or “before acquisition” level, it covers the often not so easily done procedure, termed “compensation.” At the software, or “after acquisition” level, it covers signal cleaning via extra calculations with the so called “spillage factors,” applied, for example, in the ratiometric “Förster resonance energy transfer (FRET)” method, in the version “dual laser flow cytometric FRET” called FCET. Determination of compensation or spillage factors necessitates extra measurements on the pure spectral components. With a huge number of channels, as in spectral cytometry, however, such a correction is impossible. If the number of spectral components, fluorophores, is known in advance, with well-determined and time-constant spectra as their “fingerprints,” and the components are not interacting, then the net spectrum is considered as a linear superposition of the component spectra (Figure 1) [5]. Alternatively, the net spectrum is considered as a vector, which can be expanded according to the component spectra as basis vectors, with the individual coefficients as the “coordinates,” which are relative amounts, or concentrations in nature. Interaction between the components, for example, FRET, can be taken into account by appropriate extra relationships, “constraints” between the expansion coefficients. When the number of spectral components is large and unknown, another approach called principle components analysis (PCA) can be applied (Figure 2) [6, 7]. If a phenomenon is described by a few variables then they can be dependent on each other, the degree of which is quantified by the pair wise covariances. Their partial dependencies Received: 7 March 2022 Revised: 11 April 2022 Accepted: 19 April 2022