Algorithms for the selection of fluorescent reporters

Prashant Vaidyanathan, Evan Appleton, David Tran, Alexander Vahid, George Church, Douglas Densmore

Preprint posted on 16 May 2020

Article now published in Communications Biology at

Somewhere over the rainbow: automated fluorophore selection

Selected by Mariana De Niz

Categories: cell biology


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).

Figure 1. Selection of fluorophores based on instrument and experimental setup. (Figure 1 from Ref 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:  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.

Open questions

  1. Experimentally, what are important limitations of your tool that users should be aware of?
  2. Does your tool allow for correction of autofluorescence during the generation of an optimal panel?
  3. Is this compatible with live imaging, whereby perhaps large averaging or scan speed might not be possible?
  4. Is it possible to account for fluorescence decrease upon treatments of cells, such as fixation, permeabilitzation or mounting, for the generation of optimal panels?



  1. Vaidyanathan P, Appleton E, et al . Algorithms for the selection of fluorescent reporters, bioRxiv, 2020.


Posted on: 30 June 2020 , updated on: 7 July 2020


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Author's response

Vaidyanathan Prashant, Douglas Densmore, Evan Appleton shared

Open questions

1.Experimentally, what are important limitations of your tool that users should be aware of?

Like any other computational tool, the biggest limitation of our tool is that, our predictions are only as good as the accuracy of the inputs provided to the tool (which includes fluorophore spectral data and laser and filter settings of the measurement instrument). If the spectral information is incomplete (i.e. the data is not available for certain range of wavelengths) or if this data is incorrect, it could potentially skew the results towards sub-optimal panels. In our experience, we often found that there are multiple resources on the internet that have contradicting information or incomplete data regarding emission or excitation spectra for certain fluorophores. For our case studies, used fluorophores for which we had reliable spectral data, which we obtained from FPbase, since this was a reliable resource, with references to literature. Another aspect to watch out, is the use of “brightness”, which we covered in our case study. Theoretical brightness of a fluorophore is the product of the extinction coefficient and quantum yield. However, scaling the emission of the fluorophore based on the brightness might not necessarily accurately reflect what is practically observed in an experiment. Similarly, if brightness is not specified, then the tool may skew results towards an optimal panel that may not contain a bright fluorophore. The tool also currently does not consider sophisticated parameters such as laser strength, and PMT sensitivity. However, the code is open source (, and hence the algorithms can easily be modified to account for additional parameters.

2.Does your tool allow for correction of autofluorescence during the generation of an optimal panel?

Yes. This can be done by additionally specifying the emission and excitation spectra of the autofluorescence of the cell-line in the input file specifying the spectral information. The algorithm then treats this just like any other fluorophore and adds the potential bleed-through due to autofluorescence to each detector of the panel. This way, the algorithm designs panels where the impact of autofluorescence is minimal.

3.Is this compatible with live imaging, whereby perhaps large averaging or scan speed might not be possible?

Yes, the predictions for fluorophore panels are relevant for live imaging, or any other type of machine that uses lasers to excite fluorophores and capture emitted light in filtered wavelengths. This includes microscopes, sequencers, and other types of machines used for this purpose.

4.Is it possible to account for fluorescence decrease upon treatments of cells, such as fixation, permeabilitzation or mounting, for the generation of optimal panels?

If it is known that the fluorophore will decrease in brightness relative to the default reported values, this may be tweaked upon input to the tool. If this information is not explicitly known though, this feature cannot be captured by the tool.

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