The promise and peril of comparing fluorescence lifetime in biology revealed by simulations

Reviewer #1 (Public review):

In this study, Ma et al. aimed to determine previously uncharacterized contributions of tissue autofluorescence, detector afterpulse, and background noise on fluorescence lifetime measurement interpretations. They introduce a computational framework they named "Fluorescence Lifetime Simulation for Biological Applications (FLiSimBA)" to model experimental limitations in Fluorescence Lifetime Imaging Microscopy (FLIM) and determine parameters for achieving multiplexed imaging of dynamic biosensors using lifetime and intensity. By quantitatively defining sensor photon effects on signal-to-noise in either fitting or averaging methods of determining lifetime, the authors contradict any claims of FLIM sensor expression insensitivity to fluorescence lifetime and highlight how these artifacts occur differently depending on the analysis method. Finally, the authors quantify how statistically meaningful experiments using multiplexed imaging could be achieved.

A major strength of the study is the effort to present results in a clear and understandable way given that most researchers do not think about these factors on a day-to-day basis. The model code is available and written in Matlab, which should make it readily accessible, although a version in other common languages such as Python might help with dissemination in the community. One potential weakness is that the model uses parameters that are determined in a specific way by the authors, and it is not clear how vastly other biological tissue and microscope setups may differ from the values used by the authors.

Overall, the authors achieved their aims of demonstrating how common factors (autofluorescence, background, and sensor expression) will affect lifetime measurements and they present a clear strategy for understanding how sensor expression may confound results if not properly considered. This work should bring to awareness an issue that new users of lifetime biosensors may not be aware of and that experts, while aware, have not quantitatively determined the conditions where these issues arise. This work will also point to future directions for improving experiments using fluorescence lifetime biosensors and the development of new sensors with more favorable properties.

Reviewer #2 (Public review):

Summary:

By using simulations of common signal artefacts introduced by acquisition hardware and the sample itself, the authors are able to demonstrate methods to estimate their influence on the estimated lifetime, and lifetime proportions, when using signal fitting for fluorescence lifetime imaging.

Strengths:

They consider a range of effects such as after-pulsing and background signal, and present a range of situations that are relevant to many experimental situations.

Weaknesses:

A weakness is that they do not present enough detail on the fitting method that they used to estimate lifetimes and proportions. The method used will influence the results significantly. They seem to only use the "empirical lifetime" which is not a state of the art algorithm. The method used to deconvolve two multiplexed exponential signals is not given.

Reviewer #3 (Public review):

Summary:

This study presents a useful computational tool, termed FLiSimBA. The MATLAB-based FLiSimBA simulations allow users to examine the effects of various noise factors (such as autofluorescence, afterpulse of the photomultiplier tube detector, and other background signals) and varying sensor expression levels. Under the conditions explored, the simulations unveiled how these factors affect the observed lifetime measurements, thereby providing useful guidelines for experimental designs. Further simulations with two distinct fluorophores uncovered conditions in which two different lifetime signals could be distinguished, indicating multiplexed dynamic imaging may be possible.

Strengths:

The simulations and their analyses were done systematically and rigorously. FliSimba can be useful for guiding and validating fluorescence lifetime imaging studies. The simulations could define useful parameters such as the minimum number of photons required to detect a specific lifetime, how sensor protein expression level may affect the lifetime data, the conditions under which the lifetime would be insensitive to the sensor expression levels, and whether certain multiplexing could be feasible.

Weaknesses:

The analyses have relied on a key premise that the fluorescence lifetime in the system can be described as two-component discrete exponential decay. This means that the experimenter should ensure that this is the right model for their fluorophores a priori and should keep in mind that the fluorescence lifetime of the fluorophores may not be perfectly described by a two-component discrete exponential (for which alternative algorithms have been implemented: e.g., Steinbach, P. J. Anal. Biochem. 427, 102-105, (2012)). In this regard, I also couldn't find how good the fits were for each simulation and experimental data to the given fitting equation (Equation 2, for example, for Figure 2C data).

Also, in Figure 2C, the 'sensor only' simulation without accounting for autofluorescence (as seen in Sensor + autoF) or afterpulse and background fluorescence (as seen in Final simulated data) seems to recapitulate the experimental data reasonably well. So, at least in this particular case where experimental data is limited by its broad spread with limited data points, being able to incorporate the additional noise factors into the simulation tool didn't seem to matter too much.

Author response:

eLife Assessment

This important study describes a computational tool termed FliSimBA (Fluorescence Lifetime Simulation for Biological Applications), which uses simulations to rigorously assess experimental limitations in fluorescence lifetime imaging microscopy (FLIM), including diverse noise factors, hardware effects, and sensor expression levels. The evidence from simulation and experimental measurements supporting the usefulness of FlimSimBA is solid. The authors may improve the application of the tool to a wide range of biological samples by providing the simulation package, currently in MATLB, in other common languages such as Python, and having better descriptions of the fitting algorithm and model assumptions. The work will interest scientists who wish to perform quantitative FLIM imaging for cells and tissues.

We thank the editors and reviewers for the constructive feedback. We plan to provide the FLiSimBA simulation package in Python in addition to Matlab. We will also describe in more detail in the Results section our fitting method. Furthermore, we will explain more clearly in the text that our simulation package makes almost no model assumptions, and features flexibility and adaptability so that it can be used for any fluorescence lifetime measurements. We will clearly outline what are the specific examples we use for our case studies, and how users can input their own values based on the specific sensors, autofluorescence, and hardware they use.

Public Reviews:

Reviewer #1 (Public review):

In this study, Ma et al. aimed to determine previously uncharacterized contributions of tissue autofluorescence, detector afterpulse, and background noise on fluorescence lifetime measurement interpretations. They introduce a computational framework they named "Fluorescence Lifetime Simulation for Biological Applications (FLiSimBA)" to model experimental limitations in Fluorescence Lifetime Imaging Microscopy (FLIM) and determine parameters for achieving multiplexed imaging of dynamic biosensors using lifetime and intensity. By quantitatively defining sensor photon effects on signal-to-noise in either fitting or averaging methods of determining lifetime, the authors contradict any claims of FLIM sensor expression insensitivity to fluorescence lifetime and highlight how these artifacts occur differently depending on the analysis method. Finally, the authors quantify how statistically meaningful experiments using multiplexed imaging could be achieved.

A major strength of the study is the effort to present results in a clear and understandable way given that most researchers do not think about these factors on a day-to-day basis. The model code is available and written in Matlab, which should make it readily accessible, although a version in other common languages such as Python might help with dissemination in the community. One potential weakness is that the model uses parameters that are determined in a specific way by the authors, and it is not clear how vastly other biological tissue and microscope setups may differ from the values used by the authors.

Overall, the authors achieved their aims of demonstrating how common factors (autofluorescence, background, and sensor expression) will affect lifetime measurements and they present a clear strategy for understanding how sensor expression may confound results if not properly considered. This work should bring to awareness an issue that new users of lifetime biosensors may not be aware of and that experts, while aware, have not quantitatively determined the conditions where these issues arise. This work will also point to future directions for improving experiments using fluorescence lifetime biosensors and the development of new sensors with more favorable properties.

We appreciate the comments and helpful suggestions. We plan to present FLiSimBA simulation code in Python in addition to Matlab to make it more accessible to the community.

One of the advantages of FLiSimBA is that the simulation package is flexible and adaptable, allowing users to input parameters based on the specific sensors, hardware, and autofluorescence measurements for their biological and optical systems. We used parameters based on one FRET-based sensor, measured autofluorescence from mouse tissue, and measured dark count/after pulse of our specific GaAsP PMT in this manuscript as examples. We will emphasize this advantage and further clarify how these parameters can be adapted to diverse tissues, imaging systems, and sensors based on individual users in our revision.

Reviewer #2 (Public review):

Summary:

By using simulations of common signal artefacts introduced by acquisition hardware and the sample itself, the authors are able to demonstrate methods to estimate their influence on the estimated lifetime, and lifetime proportions, when using signal fitting for fluorescence lifetime imaging.

Strengths:

They consider a range of effects such as after-pulsing and background signal, and present a range of situations that are relevant to many experimental situations.

Weaknesses:

A weakness is that they do not present enough detail on the fitting method that they used to estimate lifetimes and proportions. The method used will influence the results significantly. They seem to only use the "empirical lifetime" which is not a state of the art algorithm. The method used to deconvolve two multiplexed exponential signals is not given.

We appreciate the comments and constructive feedback and will more clearly describe the fitting methods in our revision.

Two metrics are currently used to estimate lifetime in our paper, which are currently described in the Methods section ‘Experimental data collection, parameter determination, and simulation’ and ‘FLIM analysis’: (1) fitted P1: we described how lifetime histograms were fitted to Equation 2 with the Gauss-Newton nonlinear least-square fitting algorithm and the fitted P1 was used as lifetime estimation; (2) empirical lifetime, defined by Equation 5. These two metrics were used for the following reasons: (1) when the exponential decay equation of a sensor is known (for example, the FRET-based PKA activity sensor FLIM-AKAR can be described as a double exponential equation), fitted coefficients for each exponential component provide a robust way for lifetime estimate that is less sensitive to noise and background signals; (2) when the biophysical properties of sensors are unknown, or when the sensors cannot be easily described with single or double exponential equations, empirical lifetime (i.e. average lifetime values) provides an unbiased way to quantify fluorescence lifetime without assumptions of underlying models to describe sensor lifetime.

To deconvolve two multiplexed exponential signals (Fig. 8), histograms were fitted to Equation 2 with the Gauss-Newton nonlinear least-square fitting algorithm, as described in Methods section ‘Simulation and analysis of multiplexed imaging with fluorescence intensity and lifetime data’.

Considering the importance of these methodological details for evaluating the conclusions of this study, and the importance of appreciating the advantages and limitations of different methods of lifetime estimates (e.g. Figure 7), we will move the description of the fitting method to estimate P1 and the method of calculating empirical lifetime from Methods to Results, and will further clarify the rationale of using these different methods of lifetime estimates.

Reviewer #3 (Public review):

Summary:

This study presents a useful computational tool, termed FLiSimBA. The MATLAB-based FLiSimBA simulations allow users to examine the effects of various noise factors (such as autofluorescence, afterpulse of the photomultiplier tube detector, and other background signals) and varying sensor expression levels. Under the conditions explored, the simulations unveiled how these factors affect the observed lifetime measurements, thereby providing useful guidelines for experimental designs. Further simulations with two distinct fluorophores uncovered conditions in which two different lifetime signals could be distinguished, indicating multiplexed dynamic imaging may be possible.

Strengths:

The simulations and their analyses were done systematically and rigorously. FliSimba can be useful for guiding and validating fluorescence lifetime imaging studies. The simulations could define useful parameters such as the minimum number of photons required to detect a specific lifetime, how sensor protein expression level may affect the lifetime data, the conditions under which the lifetime would be insensitive to the sensor expression levels, and whether certain multiplexing could be feasible.

Weaknesses:

The analyses have relied on a key premise that the fluorescence lifetime in the system can be described as two-component discrete exponential decay. This means that the experimenter should ensure that this is the right model for their fluorophores a priori and should keep in mind that the fluorescence lifetime of the fluorophores may not be perfectly described by a two-component discrete exponential (for which alternative algorithms have been implemented: e.g., Steinbach, P. J. Anal. Biochem. 427, 102-105, (2012)). In this regard, I also couldn't find how good the fits were for each simulation and experimental data to the given fitting equation (Equation 2, for example, for Figure 2C data).

We thank the reviewer for the constructive feedback. We agree that the FLiSimBA users should ensure that the right decay equations are used to describe the fluorescent sensors. In this study, we used a FRET-based PKA sensor FLIM-AKAR to provide a proof-of-principle demonstration of FLiSimBA usage. The donor fluorophore of FLIM-AKAR, truncated monomeric enhanced GFP, follows a single exponential decay. FLIM-AKAR, a FRET-based sensor, follows a double exponential decay. The time constants of the two exponential components were determined previously (Chen, et al, Frontiers in pharmacology (2014)). Thus, a double exponential decay equation with known τ1 and τ2 (Equation 1) was used for both simulation and fitting. In our revision, we will refer to our prior study characterizing the double exponential decay model of FLIM-AKAR. We will also emphasize the importance of using the right decay equations, strategies to estimate sensor decays, and how the flexibility of FLiSimBA allows users to input different forms of models to describe their specific sensor histograms. We will additionally provide data showing the goodness of fit for both simulated data and experimental data.

Also, in Figure 2C, the 'sensor only' simulation without accounting for autofluorescence (as seen in Sensor + autoF) or afterpulse and background fluorescence (as seen in Final simulated data) seems to recapitulate the experimental data reasonably well. So, at least in this particular case where experimental data is limited by its broad spread with limited data points, being able to incorporate the additional noise factors into the simulation tool didn't seem to matter too much.

We agree that in Figure 2C the contributions from autofluorescence, afterpulse, and background signals are small, because sensor photon count is high here. As seen in Figure 2B, when sensor photon counts are higher, the contributions from these other factors become less pronounced. The simulated data in Figure 2C were based on high photon counts because the simulated P1 value was determined by fitting experimental data. To achieve reasonable fitting with minimal interference from autofluorescence, afterpulse, and background signals, we used experimental data with high sensor expression. We will clarify these details in our revision.