Algorithms for the selection of fluorescent reporters
Preprint posted on May 16, 2020 https://www.biorxiv.org/content/10.1101/2020.05.15.098186v1
Various fields in biology extensively use fluorescent probes to visualize a plethora of phenomena including cell-cell interactions, protein dynamics, immune responses, and signaling among others. Fluorophores fall into various categories, but they all share general properties, including excitation and emission spectra for example. One important challenge has been to maximize the number of different signals that can be distinguished in a single measurement, to maximize the number of probes that can be used simultaneously. The challenge arises from the fact that many fluorescent probes emit light spectra that overlap with one another, making it difficult to separate signals from different probes (a problem known as spectral spill-over, or bleed-through). While it is possible to correct for spectral spillover using algebraic operations for spectral compensation, if compensation is incorrectly performed, it can lead to incorrect biological measurements and/or incorrect conclusions. Choosing the right set of fluorophores and detectors is key for the experimental setup. In their work, Vaidyanathan, Appeleton et al generated algorithms and implemented them in an open-source software tool, to allow users to design an n-colour panel of fluorophores optimized for maximal signal and minimal bleed-through (1).
Key findings and developments
The authors designed an open-source web-application and command-line tool that allows users to design an n-colour panel for a specific measurement instrument: http://fpselection.org. The solution can be constructed from a library of fluorophores for a fluorescence measurement instrument to find the optimal panel configurations, as well as allowing the user to upload values such as emission and excitation spectra, autofluorescence of samples used, and brightness of fluorophores. Two properties were chosen to optimize the n-colour panel: a) the amount of signal measured by a detector for the fluorophore it is supposed to detect, and b) the amount of bleed-through from all other fluorophores in that detector. The authors highlight that obtaining a valid panel is not trivial, and that on one hand the probability of obtaining a valid panel using the maximum number of fluorophores is relatively low, while a valid panel may not measure fluorescence efficiently. The authors found a logarithmic approach for fluorophore selection: FP selection uses the search algorithm to find a valid panel where the amount of signal is each detector is maximized and bleed-through is minimized. The recommended algorithm uses simulated annealing to quickly and reliably find an optimal result. The authors tested the efficiency of the search algorithm by comparing it against a list of valid panels for 3 different flow cytometers with unique sets of lasers and detectors. They noticed that the run-time for simulated annealing was constant and each run took less than 1 second to complete. Moreover, they found that simulated annealing performs well as a heuristic, and returned an optimal solution in most runs.
The authors then went on to compare the computational predictions against experimental measurements, considering two metrics: a) the number of panels where, for all detectors in the panel, the fluorophores with normalized values equaling 1 matched, and b) the number of panels where for all detectors in the panel, the difference between each normalized predictions and measurement value must be within 0.05, 0.10, and 0,20 of one another. Altogether, they demonstrate that computational predictions of signal and bleed-through reliably match experimental observations.
What I like about this preprint
I like that the paper provides a new tool for optimal selection of fluorophores for different experimental setups. Working with fluorescence-based setups myself, I know that fluorophore choice can be very time-consuming and limiting. I find that a tool which would allow me to create an optimal panel reliably, would be hugely time-saving and perhaps result in less experimental problems/error with bleed-through.
- Experimentally, what are important limitations of your tool that users should be aware of?
- Does your tool allow for correction of autofluorescence during the generation of an optimal panel?
- Is this compatible with live imaging, whereby perhaps large averaging or scan speed might not be possible?
- Is it possible to account for fluorescence decrease upon treatments of cells, such as fixation, permeabilitzation or mounting, for the generation of optimal panels?
- Vaidyanathan P, Appleton E, et al . Algorithms for the selection of fluorescent reporters, bioRxiv, 2020.
Posted on: 30th June 2020Read preprint
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