Talanta 226 (2021) 122117

Available online 27 January 20210039-9140/© 2021 Elsevier B.V. All rights reserved.

Fluorophore unmixing based on bleaching and recovery kinetics using MCR-ALS

S. Hugelier a,*, R. Van den Eynde a, W. Vandenberg a,b, P. Dedecker a

a Laboratory for Nanobiology, KU Leuven, B-3001 Leuven, Belgium b Univ. Lille, CNRS, Laboratoire de Spectroscopie pour Les Interactions, La Reactivite et L’Environnement (LASIRE), F-59000 Lille, France

A R T I C L E I N F O

Keywords: Image unmixing Fluorescence microscopy Multi-label imaging Eukaryotic membrane labelling

A B S T R A C T

Fluorescence microscopy is a key technology in the life sciences, though its performance is constrained by the number of labels that can be recorded. We propose to use the kinetics of fluorophore photodestruction and subsequent fluorescence recovery to distinguish multiple spectrally-overlapping emitters in fixed cells, thus enhancing the information that can be obtained from a single measurement. We show that the data can be directly processed using multivariate curve resolution - alternating least squares (MCR-ALS) to deliver distinct images for each fluorophore in their local environment, and apply this methodology to membrane imaging using DiBAC4(3) and concanavalin A - Alexa Fluor 488 as the fluorophores. We find that the DiBAC4(3) displays two distinct degradation/recovery kinetics that correspond to two different label distributions, allowing us to simultaneously distinguish three different fluorescence distributions from two spectrally overlapping fluo-rophores. We expect that our approach will scale to other dynamically-binding dyes, leading to similarly increased multiplexing capability.

1. Introduction

Fluorescence imaging is an essential technique in the life and ma-terials sciences since it provides a very high specificity and sensitivity while requiring only minimal sample perturbation [1]. The amount of information that can be obtained is usually determined by the number of labels that can be visualized at once, so that a key goal is to increase the number of fluorophores that can be measured on the same sample [2–5]. Several different approaches to achieve this have been demonstrated in practice. The most straightforward solution is to use multiple spectrally-separated fluorophores, though this is limited by the spectral bandwidth of the probes and the width of the visual spectrum. Multi-spectral detector and spectral unmixing or hyperspectral imaging [6–9] can enhance this strategy allowing a high degree of spectral overlap among the different fluorophores, but requires more complex detectors and precise knowledge of the in situ emission spectrum of each fluorophore [10]. However, recent works on this topic have shown the potential of blind spectral unmixing strategies to reduce this limitation [11,12].

Fluorophore separation can also be achieved by focusing on other parameters, such as the temporal dynamics of the signal. Fluorescence

lifetime imaging, for example, can be used to separate overlapping probes based on their excited-state lifetimes [13–15]. Other approaches distinguish fluorophores based on the kinetics of reversible transitions to non-fluorescent states [16–19], including the use of photochromic labels that offer very high control over the label emission, thus allowing the multiplexing of many dyes [20]. Parallel factors analysis (PARAFAC) has also been used to separate emitters based on the spectral and photo-destruction properties of the fluorophores [21], though this requires advanced imaging equipment to measure a trilinear data structure suitable for this methodology.

Several applications have also seen the multiplexing of dyes by repeated labeling-imaging-stripping cycles [22–25], in which the sam-ple is repeatedly stained with fluorophores that target different struc-tures. This approach relies on the presence of labels that either transiently bind to the sample or that can be removed through chemical treatment, which can be time-consuming and/or laborious [26,27]. Transiently-binding fluorophores typically display association and dissociation kinetics that are determined by their affinities for the labelled structures and the concentration at which the labels are added, resulting in distinct kinetic signatures that were used to separate mul-tiple labels at the single-molecule levels [28], though so far there have

* Corresponding author. E-mail address: siewert.hugelier@gmail.com (S. Hugelier).

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Talanta

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https://doi.org/10.1016/j.talanta.2021.122117 Received 31 October 2020; Received in revised form 8 January 2021; Accepted 11 January 2021

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2

been no reports using this approach at the more common ensemble (non-single molecule) level.

While a wide range of light-induced dynamic processes have been observed in dyes, one dynamic process that is observed in every fluo-rophore is photodestruction. This presents an attractive option for flu-orophore separation in static samples since it is both ubiquitous and every fluorophore possesses distinct rates of degradation. Indeed, pre-vious work showed the feasibility of separating up to three different fluorophores based on their bleaching [29,30], which can depend on the imaging modality and local environment [31]. If the fluorophore stains via transient binding to the structure, then this photodestruction can also be followed by a recovery of the fluorescence via the replenishment of fresh fluorophores from the bulk solution. In principle, this offers further opportunities to enhance the fluorophore separation, though this has thus far not been examined.

The identification of the different fluorophore responses may not be straightforward in (complex) mixtures since both photodestruction and fluorescence recovery depend on the specifics of the sample and the measurement conditions, such that even reference measurements may be of only limited value. Ideally this would be handled via a blind analysis strategy that is sufficiently expressive (i.e. able to work with many different data structures) to include the real-world constraints inherent to (bio-)chemical systems. One highly successful approach is multivariate curve resolution - alternating least squares (MCR-ALS) [32–37]. This methodology was originally designed for the analysis of chemical reaction profiles that consist of time-resolved optical spectra, where the goal is to simultaneously recover both the concentration profiles and the spectra of the chemical species involved in the reaction. It achieves this by simultaneously analysing the full two-dimensional (time and wavelength) dataset and allows the inclusion of additional constraints, such as the non-negativity of the profiles.

In this work, we examine the feasibility of using combined photo-destruction and fluorescence recovery to distinguish between multiple labelled structures. We stain fixed cells with Bis-(1,3-Dibarbituric acid)- trimethine oxonol [DiBAC4(3)], a dye that labels inner membranes [38–40] and with concanavalin A - Alexa Fluor 488 (ConA), a dye that stains glycoproteins and glycolipids [41,42]. We find that DiBAC4(3) displays clear photodestruction and fluorescence recovery characteris-tics that can be distinguished into two populations. These can be further distinguished from the photodestruction and fluorescence recovery of ConA, allowing us to visualize three different structures. We show that the required analysis can be successfully performed by MCR-ALS. We expect that our approach can readily extend the information that can be obtained from fixed samples using only conventional imaging equipment.

2. Materials and methods

2.1. Sample preparation

U2OS cell were stained with either DiBAC4(3) (after fixation), conA (prior to fixation), or both. The protocols for fixation and staining are described below.

2.1.1. Cell fixation Before fixation, a 2x PHEM buffer was prepared by mixing 3.63 g 1,4-

Piperazinediethanesulfonic acid (PIPES, Sigma-Aldrich; n◦ P1851-25G), 1.19 g 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid (HEPES, Sigma-Aldrich; n◦ H4034-25G), 0.76 g Ethylene glycol-bis(2- aminoethylether)-N,N,N′,N′-tetraacetic acid (EGTA, Sigma-Aldrich; n◦

03777-10G) and 0.4 mL MgCl2 (Sigma-Aldrich; n◦ M1028-10x1mL) in 80 mL sterile Milli-Q water. This solution was adjusted to pH 6.9 using 6 N NaOH (Fisher Scientific; n◦ 10743591), after which sterile Milli-Q water was added to obtain a final volume of 100 mL. It was then filtered through a 0.22 μm sterile filter (Sartorius; n◦ 16541-k) and stored at 4 ◦C.

The fixation was performed by washing cells twice with 1 mL Dul-becco’s phosphate buffered saline (dPBS), followed by adding the fixa-tion mixture consisting of 0.5 mL 8% formaldehyde (FA; Thermo-Fisher; n◦ 28908), 4 μL 25% glutaraldehyde (GA, Sigma-Aldrich; n◦ G5882) and 0.5 mL of the previously prepared PHEM buffer (all brought to 37 ◦C prior to mixing). The cells with fixation mixture are then put back to incubate at 37 ◦C for 15 min, after which the fixation mixture is washed away by using 2 mL dPBS twice. The cells were then stored in 1 mL dPBS at 4 ◦C.

2.1.2. ConA staining A 500 μg/mL ConA solution (Thermo-Fisher; n◦ C11252) is prepared

in Hank’s balanced salt solution (HBSS; Gibco; n◦ 14065-049) from the supernatant of the stock solution, which was centrifuged at max speed for 30 min. Before staining the cells, they are washed once with 1 mL pre-heated HBSS (at 37 ◦C), to which 1 mL ConA solution is added (also heated up to 37 ◦C). The cells are then incubated at 37 ◦C for 30 min and finally washed twice with 1 mL HBSS. The ConA-labelled cells were then ready for fixation.

2.1.3. DiBAC4(3) staining After cells are fixated and ready to be measured, they are washed

once with HBSS, after which 1 mL of a 100 mM DiBAC4(3) solution (in dimethyl sulfoxide) is added (Biotium; n◦ 61011). Before measuring, the solution should not be removed, and at least 5 min are needed to reach an equilibrium.

Fig. 1. Photodestruction and fluorescence recovery measurements on fixed U2OS cells labelled with ConA. The first image of the acquisition is shown in (a) while the raw time traces are shown in (b). The scale bar is 4 μm.

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2.2. Cell imaging

The imaging was performed on a Ti-E2 microscope with a ×100 CFI apo TIRF objective (Nikon) using a ZT405/488/561/640rpcv2 dichroic (Chroma) paired with a ZET405/488/561/640 m emission filter (Chroma). For excitation, a 488 nm laser (Oxxius) was used which was delivered through an Oxxius laser combiner coupled into the microscope using an optical fibre. Images were acquired on a PCO edge 4.2 CMOS camera with an exposure time of 30 ms at a 0.57 mW laser power setting.

The following photodestruction illumination scheme was used: i) 60 × 0.3 s of photodestruction at 6.2 mW 488 nm laser power and ii) 90 ×0.3 s of photodestruction at 41.8 mW 488 nm laser power, where each time point was followed by an acquisition of an image. After photo-destruction, the fluorophores were left to recover and images were ac-quired each second (30 times), followed by every 5 s (30 times).

2.3. Data analysis

2.3.1. Data description Three different data sets of labelled U2OS cells were acquired ac-

cording to the illumination scheme as described above. The acquired images were cropped to a region of interest and processed by an offset- correction to remove the offset added by the camera hardware (~100 counts). The final dimensions of the single dye images were 450 pixels ×650 pixels × 210 time points and 500 pixels × 500 pixels × 210 time points, for the DiBAC4(3)-labelled (Fig. 3a) and ConA-labelled (Fig. 1) data sets, respectively. The dimensions of the data set in which both dyes (Fig. 4a) are combined were 500 pixels × 500 pixels × 210 time points.

2.3.2. Data decomposition with MCR-ALS Multivariate curve resolution - alternating least squares [32–35] is

an algorithm that assumes a bilinear model to decompose mixed data into the independent contributions of analytes and noise. It originates from the Beer-Lambert law for multicomponent samples in chemistry and aims to find the sources of data variation that contribute to the raw measurements by using an additive model of linear contributions (Eq. (1)), which can be expressed by the product of a dyad of vectors (Eq. (2)). Equation (2) can be rewritten in its compact form as shown in equation (3).

X=∑k

i=1Xi + E (1)

X=∑k

i=1cipT

i + E (2)

X=CPT + E (3)

The matrix X (m× n) is a two-way matrix that represents the data obtained on k species of the unknown mixture. The dyad of vectors C (m× k) and PT (k× n) contain the resolved pure concentration profiles and pure instrumental response profiles (time traces in this work), respectively. The noise on the measurements is represented by the ma-trix E (m× n).

The profiles in C and PT are optimized in an alternating fashion under a set of constraints (due to rotational and scale ambiguities [32, 43]) until a convergence criterion is achieved. To assess the results, the lack-of-fit (LOF) and percentage of variance explained (r2) are used.

LOF =

∑i,je2

i,j∑

i,jd2i,j

√

(4)

r2 =

∑i,jd2

i,j −∑

i,je2i,j

∑i,jd2

i,j(5)

In equations (4) and (5), di,j and ei,j are the (i× j)th element of D and

E, respectively. As the model still holds for each pixel of an image, the algorithm can

also be used for the decomposition of imaging data [44–46]. However, the original structure of the measured imaging data is a three-dimensional data cube and must be unfolded into a two-way matrix, in which the number of data rows will be equal to the number of pixels of the image. Afterwards, the individual profiles in the matrix C can be refolded to its original x-y dimensions to visualize the images related to the pure contributions.

We used the MCR-ALS method on the offset-corrected data with initial estimates chosen using a purest variable selection method included in the GUI version of MCR-ALS [36,37], similar to the simple interactive self-modelling multivariate analysis (SIMPLISMA) method [47]. Given the large size of the data, MCR-ALS was first performed on a smaller zone (100 pixels × 100 pixels × 210 time points) and was then further optimized by using the entire data set. Approaching the problem in this way makes the optimization faster as initial estimates are usually not close to the final profiles and thus more steps of the iterative pro-cedure are needed, making analysis slow and calculation-intensive. These pre-optimized profiles will of course converge to the ones optimal for the smaller data set, but they can be used as initial estimates for the entire data set. This will then only require a handful of iterations to reach the convergence criterion, but now optimized to the entire data set. This smaller zone was carefully selected to be representative of the entire data set (same rank as determined by SVD comparison), to ensure that no components are missed. Moreover, three different zones for initial MCR-ALS calculations were considered for each data set but did not influence the results.

The optimization performed in Section 3.2 used only non-negativity as a constraint (in both C and PT), and equal length of the profiles in PT

as normalization. For the data in Section 3.3, MCR-ALS was performed initially as in Section 3.2, but the decomposition results obtained were not consistent with the knowledge about the system. Therefore, a second MCR-ALS optimization was performed in which a monotonicity constraint was added, together with a mono-exponential decay hard- model constraint for all components in the time range 0–45 s.

2.3.3. Software The MCR-ALS analysis was performed with an adaptation of the

MCR-ALS command line code (https://mcrals.wordpress.com /download/mcr-als-command-line/) in Matlab R2018b (The Math-Works, USA).

3. Results and discussion

3.1. Data exploration

We labelled fixed U2OS cells with DiBAC4(3) and ConA. Since we were interested in the dynamic properties of the fluorescence, we applied a sequential illumination strategy consisting of a moderate illumination, strong illumination and fluorescence recovery (no addi-tional illumination beyond the acquisition of the fluorescence images) phase. We observed strong differences in behaviour of the two dyes in their photodestruction and fluorescence recovery, with ConA showing essentially no recovery (Fig. 1) and DiBAC4(3) showing recovery of the fluorescence due to replenishment of the degraded fluorophores from the bulk solution (Fig. 3a–b).

Intriguingly, a singular value decomposition (SVD) analysis on the pure DiBAC4(3) data revealed the presence of at least two major con-tributions, indicating that DiBAC4(3) is present in at least two states that presumably correspond to different local environments. On the other hand, the pure ConA data contains only one major component, indi-cating a uniform response to the applied radiation. Moreover, looking at the data in which cells were labelled with both DiBAC4(3) and conA (Fig. 4a–b), we do not see pure ConA responses in the time traces, confirming that the data is quite complex.

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Fig. 2. Conceptual illustration of the blind fluorophore separation using MCR-ALS.

Fig. 3. U2OS cells labelled with DiBAC4(3). The first image of the acquisition is shown in (a) while the raw time traces are shown in (b). We show the component distribution maps and corresponding time traces of (c) an MCR-ALS analysis with non-negativity constraints on C and PT (LOF: 6.00%; R2: 99.64%). The scale bar is 4 μm.

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3.2. MCR-ALS unmixing

We performed MCR-ALS on the different data sets obtained from cells stained with DiBAC4(3) and stained with a combination of DiBAC4(3) and conA. The cells stained with only conA did not need any unmixing as SVD revealed only one component. We show this concep-tually in Fig. 2.

3.2.1. MCR-ALS on cell stained with DiBAC4(3) The SVD analysis indicated that the response of DiBAC4(3) to the

irradiation and recovery contained at least two different contributions. We were curious to what extent these contributions could be separated using MCR-ALS to resolve both the temporal profiles and spatial distri-butions associated with each labelled structure.

We performed MCR-ALS analysis on our data, requiring only that all the signals are non-negative. The LOF obtained was 6.00% (with an explained variance of 99.64%). The results of this analysis are shown in Fig. 3 and support that DiBAC4(3) targets two distinct labelling envi-ronments, which can be separated based on their response to irradiation and fluorescence recovery. These components reflect an apparent

localization to a more nuclear organization (component 1) and to the endoplasmatic reticulum (ER; component 2), and show different degradation and recovery kinetics. We do not have a clear explanation for the difference in the observed rates of photodestruction, though it likely arises through differences in the local membrane composition, which could exert its influence through factors such as rigidity and ox-ygen accessibility. Likewise, differences in the binding equilibrium can also influence photodestruction since the degradation process presum-ably occurs only when the dye is bound to the membrane. The different recovery rates presumably reflect different accessibilities to the struc-tures and indicate that it is easier for DiBAC4(3) molecules to quickly exchange in the ER membranes than the more nuclear localization.

3.2.2. MCR-ALS on cells stained with DiBAC4(3) and ConA We then extended our analysis methodology to the measurement of

U2OS cells stained with DiBAC4(3) and ConA. The results of the three- component MCR-ALS analysis under non-negativity constraints are shown in Fig. 4a (LOF of 4.25% and an explained variance of 99.82%). The resulting images were highly reminiscent of what we previously observed for the isolated dyes. However, the fluorescence recovery for

Fig. 4. U2OS cells labelled with DiBAC4(3) and conA fluorophores. The first image of the acquisition is shown in (a) while the raw time traces are shown in (b). We show the component distribution maps and corresponding time traces from MCR-ALS analyses with (c) non-negativity constraints on C and PT (LOF: 4.25%; R2: 99.82%) and (d) non-negativity constraints on C and PT, a monotonicity constraint and a mono-exponential hard-modelling constraint on the photodestruction part of the time traces for all components (LOF: 7.91%; R2: 99.37%). The scale bar is 4 μm.

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component 2 displayed a non-monotonic behaviour, consisting of a rise and subsequent decay, seemingly in contradiction to the absence of irradiation during this phase. We therefore expanded the MCR-ALS to include a monotonicity constraint and forced a mono-exponential decay for all components in the time range 0–45 s, as this is the model for photodestruction, leading to the results shown in Fig. 4b. The LOF of this model was 7.91% and the explained variance 99.37%. The latter model has a slightly higher LOF with respect to the first one, but still consistent with what is expected due to added constraints. This highlights a key advantage of the MCR-ALS analysis, in that it readily offers the flexibility to include additional knowledge about the data as constraints that the model must fulfill [32,45,48,49].

The resulting images show that the first component is now free of contributions related to the ER-bound DiBAC4(3) molecules. We do observe that the fluorescence intensity is higher at the end of the experiment compared to the beginning, which presumably reflects that the experiments were started before the equilibration of the dye addition.

Taken together, our results show that three different label distribu-tions can be resolved by combining photodestruction and fluorescence recovery with MCR-ALS.

4. Conclusion

Multicolour fluorescence imaging allows the visualization of different structures but is often limited by the number of available colour channels. In this work, we have shown that multiple spectrally- overlapping fluorophores can be separated based on the kinetics of their photodestruction and fluorescence recovery, and that this approach can even be used to separate regions that display different labelling affinities for the same fluorophore. We have further showed that MCR-ALS is an excellent technique to analyse such data, even in the absence of reference measurements, because of its intrinsic robustness and the straightforward manner to include (bio-)chemical knowledge about the system as constraints to produce meaningful results and reduce rotational ambiguities. To further reduce the extent of the rota-tional ambiguities in the data decomposition, we also recommend users of this approach to analyse multiple data sets simultaneously using an augmented data matrix approach [50].

Our method does require the use of labels that can be replenished, though a wide variety of such labels is available and further possibilities are available thanks to innovative labelling schemes such as DNA-PAINT or other systems that rely on dynamically binding dyes [23,24,51,52]. This will also allow our approach to be used across multiple spectral channels, further enhancing the amount of information that can be ob-tained. We expect that our approach will allow for enhanced multi-plexing capabilities while still being compatible with existing fluorescence imaging equipment.

Credit author statement

Siewert Hugelier: conceptualization, methodology, software, formal analysis, investigation, writing - original draft, visualization. Robin Van den Eynde: conceptualization, investigation, writing - original draft. Wim Vandenberg: conceptualization, methodology, investigation, writing - original draft. Peter Dedecker: conceptualiza-tion, funding acquisition, resources, writing - original draft, supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to thank Sam Duwe (Hasselt University) for performing initial experiments with the DiBAC4(3) dye. S.H. and R.V. thank the Research Foundation Flanders for a postdoctoral and doctoral fellowship, respectively. P.D. thanks the Research Foundation Flanders through grants 1514319N, G090819N, G0B8817N, the KU Leuven for grant C14/17/111, and the European Research Council through grant 714688 NanoCellActivity.

References

[1] M.C. Leake, Biophysics: Tools and Techniques, CRC Press, 2016. [2] A. Schonle, S.W. Hell, Fluorescence nanoscopy goes multicolor, Nat. Biotechnol. 25

(2007) 1234–1235, https://doi.org/10.1038/nbt1107-1234. [3] M. Bates, B. Huang, G.T. Dempsey, X. Zhuang, Multicolor super-resolution imaging

with photo-switchable fluorescent probes, Science 317 (2007) 1749–1753, https:// doi.org/10.1126/science.1146598.

[4] M. Minoshima, J. Kikuta, Y. Omori, S. Seno, R. Suehara, H. Maeda, H. Matsuda, M. Ishii, K. Kikuchi, In vivo multicolor imaging with fluorescent probes revealed the dynamics and function of osteoclast proton pumps, ACS Cent. Sci. 5 (2019) 1059–1066, https://doi.org/10.1021/acscentsci.9b00220.

[5] M.L. Perez-Bueno, M. Pineda, F.M. Cabeza, M. Baron, Multicolor Fluorescence imaging as a candidate for disease detection in plant phenotyping, Front. Plant Sci. 7 (2016) 1790, https://doi.org/10.3389/fpls.2016.01790.

[6] M.E. Dickinson, E. Simbuerger, B. Zimmermann, C.W. Waters, S.E. Fraser, Multiphoton excitation spectra in biological samples, J. Biomed. Optic. 8 (2003) 329–338, https://doi.org/10.1117/1.1583734.

[7] W. Jahr, B. Schmid, C. Schmied, F.O. Fahrbach, J. Huisken, Hyperspectral light sheet microscopy, Nat. Commun. 610 (2015) 7990, https://doi.org/10.1038/ ncomms8990.

[8] N. Keshava, J.F. Mustard, Spectral Unmixing vol. 19, IEEE Signal Process, 2002, pp. 44–57, https://doi.org/10.1109/79.974727.

[9] J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, Hyperspectral unmixing overview: geometrical, statistical, and sparse regression- based approaches, IEEE J. Sel. Top. Appl. 5 (2012) 354–379, https://doi.org/ 10.1109/JSTARS.2012.2194696.

[10] H. Tsurui, H. Nishimura, S. Hattori, S. Hirose, K. Okumura, T. Shirai, Seven-color fluorescence imaging of tissue samples based on fourier spectroscopy and singular value decomposition, J. Histochem. Cytochem. 48 (2000) 653–662, https://doi. org/10.1177/002215540004800509.

[11] J.T. Smith, M. Ochoa, X.R.M. Intes, UNMIX-ME: spectral and lifetime fluorescence unmixing via deep learning, Biomed. Optic Express 11 (2020) 3857–3874, https:// doi.org/10.1364/BOE.391992.

[12] T.D. McRae, D. Oleksyn, J. Miller, Y.-R. Gao, Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning, PloS One 14 (2019), e0225410, https://doi.org/10.1371/journal.pone.0225410.

[13] J.R. Lakowicz, H. Szmacinski, K. Nowaczyk, K.W. Berndt, M. Johnson, Fluorescence lifetime imaging, Anal. Biochem. 202 (1992) 316–330, https://doi. org/10.1016/0003-2697(92)90112-k.

[14] T. Oida, Y. Sako, A. Kusumi, Fluorescence lifetime imaging microscopy (flimscopy) – methodology, development and application to studies of endosome fusion in single cells, Biophys. J. 64 (1993) 676–685, https://doi.org/10.1016/S0006-3495 (93)81427-9.

[15] G.-J. Kremers, E.B. Van Munster, J. Goedhart, T.W.J. Gadella Jr., Quantitative lifetime unmixing of multiexponentially decaying fluorophores using single- frequency fluorescence lifetime imaging microscopy, Biophys. J. 95 (2008) 378–389, https://doi.org/10.1529/biophysj.107.125229.

[16] H.S. Park, D.U. Kim, J. Lee, G.H. Kim, K.S. Chang, Simultaneous multicolor imaging of wide-field epi-fluorescence microscopy with four-bucker detection, Biomed. Optic Express 7 (2016) 2285–2294, https://doi.org/10.1364/ BOE.7.002285.

[17] R. Zhang, R. Chouket, A.G. Tebo, M.-A. Plamont, Z. Kelemen, L. Gissot, J.-D. Faure, A. Gautier, V. Croquette, L. Julien, T. Le Saux, Simple imaging protocol for autofluorescence elimination and optical sectioning in fluorescence endomicroscopy, Optica 6 (2019) 972–980, https://doi.org/10.1364/ OPTICA.6.000972.

[18] G. Abbandonato, B. Storti, G. Signore, F. Beltram, R. Bizzarri, Quantitative optical lock-in detection for quantitative imaging of switchable and non-switchable components, Microsc. Res. Tech. 79 (2016) 929–937, https://doi.org/10.1002/ jemt.22724.

[19] S. Duwe, W. Vandenberg, P. Dedecker, Live-cell monochromatic dual-label sub- diffraction microscopy by mt-pcSOFI, Chem. Commun. (J. Chem. Soc. Sect. D) 53 (2017) 7242–7245, https://doi.org/10.1039/C7CC02344H.

[20] J. Querard, R. Zhang, Z. Kelemen, M.-A. Plamont, X. Xie, R. Chouket, I. Roemgens, Y. Korepina, S. Albright, E. Ipendey, M. Volovitch, H.L. Sladitschek, P. Neveu, L. Gissot, A. Gautier, J.-D. Faure, V. Croquette, T. Le Saux, L. Julien, Resonant out- of-phase fluorescence microscopy and remote imaging overcome spectral limitations, Nat. Commun. 8 (2017) 969, https://doi.org/10.1038/s41467-017- 00847-3.

[21] M.H. Van Benthem, T.W. Lane, R.W. Davis, P. D Lane, M.R. Keenan, PARAFAC modelling of three-way hyperspectral images: endogenous fluorophores as health

S. Hugelier et al.

Talanta 226 (2021) 122117

7

biomarkers in aquatic species, Chemometr. Intell. Lab. Syst. 106 (2011) 115–124, https://doi.org/10.1016/j.chemolab.2010.09.003.

[22] C.C. Valley, S. Liu, D.S. Lidke, K.A. Lidke, Sequential superresolution imaging of multiple targets using a single fluorophore, PloS One 10 (2015), e0123941, https://doi.org/10.1371/journal.pone.0123941.

[23] F. Schueder, M.T. Strauss, D. Hoerl, J. Schnitzbauer, T. Schlichthaerle, S. Strauss, P. Yin, H. Harz, H. Leonhardt, R. Jungmann, Universal super-resolution multiplexing by DNA exchange, Angew. Chem. Int. Ed. 56 (2018) 4052–4055, https://doi.org/10.1002/anie.201611729.

[24] Y. Wang, J.B. Woehrstein, N. Donoghue, M. Dai, M.S. Avendano, R.C. J. Schackmann, J.J. Zoeller, S.S.H. Wang, P.W. Tillberg, D. Park, S.W. Lapan, E. S. Boyden, J.S. Brugge, P.S. Kaeser, G.M. Church, S.S. Agasti, R. Jungmann, P. Yin, Rapid sequential in situ multiplexing with DNA exchange imaging in neuronal cells and tissues, Nano Lett. 17 (2017) 6131–6139, https://doi.org/10.1021/acs. nanolett.7b02716.

[25] R. Jungmann, M.S. Avendano, J.B. Woehrstein, M. Dai, W.M. Shih, P. Yin, Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and exchange- PAINT, Nat. Methods 11 (2014) 313–318, https://doi.org/10.1038/nmeth.2835.

[26] A. Raj, P. van den Bogaard, S.A. Rifkin, A. van Oudenaarden, S. Tyagi, Imaging individual mRNA molecules using multiple singly labeled probes, Nat. Methods 5 (2008) 877–879, https://doi.org/10.1038/nmeth.1253.

[27] K.H. Chen, A.N. Boettiger, J.R. Moffitt, S. Wang, X. Zhuang, RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells, Science 348 (2015) aaa6090, https://doi.org/10.1126/science.aaa6090.

[28] O.K. Wade, J.B. Woehrstein, P.C. Nickels, S. Strauss, F. Stehr, J. Stein, F. Schueder, M.T. Strauss, M. Ganji, J. Schnitzbauer, H. Grabmayr, P. Yin, P. Schwille, R. Jungmann, 124-color super-resolution imaging by engineering DNA-PAINT blinking kinetics, Nano Lett. 19 (2019) 2641–2646, https://doi.org/10.1021/acs. nanolett.9b00508.

[29] G.J. Brakenhoff, K. Visscher, E.J. Gijsbers, Fluorescence bleach rate imaging, J. Microsc. 175 (1994) 154–161, https://doi.org/10.1111/j.1365-2818.1994. tb03478.x.

[30] A. Orth, R.N. Ghosh, E.R. Wilson, T. Doughney, H. Brown, P. Reineck, J. G. Thompson, B.C. Gibson, Super-multiplexed fluorescence microscopy via photostability contrast, Biomed. Optic Express 9 (2018) 2943–2954, https://doi. org/10.1364/BOE.9.002943.

[31] N. Malkani, J.A. Schmid, Some secrets of fluorescent proteins: distinct bleaching in various mounting fluids and photoactivation of cyan fluorescent proteins at YFP- excitation, PloS One 6 (2011), e18586, https://doi.org/10.1371/journal. pone.0018586.

[32] R. Tauler, A. Smilde, B. Kowalski, Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution, J. Chemom. 9 (1995) 31–58, https://doi.org/10.1002/cem.1180090105.

[33] R. Tauler, Multivariate curve resolution applied to second order data, Chemometr. Intell. Lab. Syst. 30 (1995) 133–146, https://doi.org/10.1016/0169-7439(95) 00047-X.

[34] C. Ruckebusch, L. Blanchet, Multivariate curve resolution: a review of advanced and tailored applications and challenges, Anal. Chim. Acta 765 (2007) 28–36, https://doi.org/10.1016/j.aca.2012.12.028.

[35] A. de Juan, R. Tauler, Multivariate curve resolution (MCR) from 2000: progress in concepts and applications, Crit. Rev. Anal. Chem. 36 (2006) 163–176, https://doi. org/10.1080/10408340600970005.

[36] J. Jaumot, R. Gargallo, A. de Juan, R. Tauler, A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in Matlab, Chemometr. Intell. Lab. 76 (2005) 101–110, https://doi.org/10.1016/j.chemolab.2004.12.007.

[37] J. Jaumot, A. de Juan, R. Tauler, MCR-ALS GUI 2.0: new features and applications, Chemometr. Intell. Lab. 140 (2015) 1–12, https://doi.org/10.1016/j. chemolab.2014.10.003.

[38] H.-J. Apell, B. Bersch, Oxonol VI as an optical indicator for membrane potentials in lipid vesicles, BBA-Biomembranes 903 (1987) 480–494, https://doi.org/10.1016/ 0005-2736(87)90055-1.

[39] T. Klapperstück, D. Glanz, M. Klapperstück, J. Wohlrab, Methodological aspects of measuring absolute values of membrane potential in human cells by flow cytometry, Cytometry 75 (2009) 593–608, https://doi.org/10.1002/cyto.a.20735.

[40] D.S. Adams, M. Levin, Measuring resting membrane potential using the fluorescent voltage reporters DiBAC4(3) and CC2-DMPE, Cold Spring Harb. Protoc. 4 (2012) 459–464, https://doi.org/10.1101/pdb.top067710.

[41] K.D. Hardman, C.F. Ainsworth, Structure of concanavalin A at 2.4-Ang resolution, Biochemistry 11 (1972) 4910–4919, https://doi.org/10.1021/bi00776a006.

[42] H.-Y. Lei, C.-P. Chang, Lectin of concanavalin A as an anti-hepatoma therapeutic agent, J. Biomed. Sci. 16 (2009) 10, https://doi.org/10.1186/1423-0127-16-10.

[43] H. Abdollahi, R. Tauler, Uniqueness and rotation ambiguities in multivariate curve resolution methods, Chemometr. Intell. Lab. Syst. 108 (2011) 100–111, https:// doi.org/10.1016/j.chemolab.2011.05.009.

[44] A. de Juan, S. Piqueras, M. Maeder, T. Hancewicz, L. Duponchel, R. Tauler, Chemometric tools for image analysis, in: R. Salzer, H. Siesler (Eds.), Infrared and Raman Spectroscopic Imaging, Wiley-VCH Verlag GmbH & Co. KGa, Weinheim, Germany, 2014, pp. 57–110.

[45] S. Hugelier, O. Devos, C. Ruckebusch, On the implementation of spatial constraints in multivariate curve resolution alternating least squares for hyperspectral image analysis, J. Chemom. 29 (2015) 557–561, https://doi.org/10.1002/cem.2742.

[46] S. Hugelier, R. Vitale, C. Ruckebusch, Image processing in chemometrics, in: S. Brown, B. Walczak, R. Tauler (Eds.), Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, Elsevier, Amsterdam, 2020, pp. 411–436.

[47] W. Windig, J. Guilment, Interactive self-modeling mixture analysis, Anal. Chem. 63 (1991) 1425–1432, https://doi.org/10.1021/ac00014a016.

[48] L. Blanchet, C. Ruckebusch, J.-P. Huvenne, A. de Juan, Focus on the potential of hybrid hard- and soft-MCR–ALS in time resolved spectroscopy, J. Chemom. 22 (2008) 666–673, https://doi.org/10.1002/cem.1144.

[49] S. Benabou, C. Ruckebusch, M. Sliwa, A. Avino, R. Eritja, R. Gargallo, A. de Juan, Study of conformational transitions of i-motif DNA using time-resolved fluorescence and multivariate analysis methods, Nucleic Acids Res. 47 (2019) 6590–6605, https://doi.org/10.1093/nar/gkz522.

[50] A.C. Oliveiri, R. Tauler, The effect of data matrix augmentation and constraints in extended multivariate curve resolution–alternating least squares, J. Chemom. 31 (2017) e2875, https://doi.org/10.1002/cem.2875.

[51] C.K. Spahn, M. Glaesmann, J.B. Grimm, A.X. Ayala, L.D. Lavis, M. Heilemann, A toolbox for multiplexed super-resolution imaging of the E. coli nucleoid and membrane using novel PAINT labels, Sci. Rep. 8 (2018), e14768, https://doi.org/ 10.1038/s41598-018-33052-3.

[52] A. Gautier, A.G. Tebo, Fluorogenic protein-based strategies for detection, actuation, and sensing, Bioessays 40 (2018) 1800118, https://doi.org/10.1002/ bies.201800118.

S. Hugelier et al.