FluoQ: A Tool for Rapid Analysis of Multiparameter FluorescenceImaging Data Applied to Oscillatory EventsFrank Stein, Manuel Kress, Sabine Reither, Alen Piljic, and Carsten Schultz*

European Molecular Biology Laboratory (EMBL), Cell Biology and Biophysics Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany

*S Supporting Information

ABSTRACT: The number of fluorescent sensors and their use inliving cells has significantly increased in the past years. Yet, theanalysis of data from single cells or cell populations usuallyremains a very time-consuming enterprise. Here, we introduceFluoQ, a new macro for the image analysis software ImageJ, whichenables fast analysis of multiparameter time-lapse fluorescencemicroscopy data with minimal manual input. FluoQ providesstatistical analysis of all measured parameters and delivers theresults in multiple graphic and numeric displays. We demonstratethe power of FluoQ by applying the macro to data analysis in thedevelopment and optimization of novel FRET reporters for monitoring the performance of calcium/calmodulin-binding inositoltrisphosphate kinases A and B (ITPKA and ITPKB) in HeLa cells. We find that conformational changes in the ITPKA-basedsensor follow receptor-mediated calcium oscillations. This indicates that ITPKA contributes to the regulation of intracellularcalcium transients by limiting inositol trisphosphate levels.

The development of fluorescent sensors and their use in singleliving cells has altered our understanding of how cellular eventsdevelop and progress.1−3 Unlike other techniques, real-timemonitoring provides single cell or even subcellular resolutionand teaches us that cultured cells usually react individuallyrather than in synchrony. This is particularly true for complexsignaling patterns such as oscillations of ion concentration orenzyme activity.4 While calcium oscillations are frequentlytracked by small molecule-based or genetically encodedfluorescent reporters, much less is investigated how theoscillatory behavior is translated into downstream signalingevents at the single cell level. It is of special interest to knowhow the activity of the many calcium/calmodulin-bindingproteins 5 transmits calcium oscillations in the signalingnetwork.6,7 One calcium/calmodulin-binding protein directlyinvolved in the modulation of calcium oscillations is inositoltrisphosphate kinase (ITPK).8−10 It is an enzyme thatspecifically phosphorylates the second messenger Ins(1,4,5)P3to Ins(1,3,4,5)P4 and thereby limits the opening frequency ofthe Ins(1,4,5)P3 receptor, a calcium channel in the endoplasmicreticulum that permits calcium release from internal stores. Thelatter is the main regulator of calcium oscillations and it hasbeen demonstrated in the past that Ins(1,4,5)P3 levels oscillatein synchrony with calcium oscillations.11−13 Therefore, wedecided to create genetically encoded FRET sensors based onITPKA and ITPKB to investigate in how far the activity ofthese enzymes follows calcium oscillations at an equivalentpace.Although time-consuming optimization of FRET reporters in

the wet-lab is already speeded up by using FRET framebackbone cassettes,14,15 the analysis of the obtained image datais still very laborious and challenging. Some recent software

solutions simplified automated image analysis,16 but there is stilla growing need for new image analysis tools.17 We thereforedeveloped FluoQ, an extensive macro extending the popularimage analysis software ImageJ 18 for fast and reproducibleanalysis of imaging data. The macro processes most life sciencesimage file formats and analyzes any number of experiments in abatch-mode fashion. Image processing and cell segmentationworks either interactively or in an automatic fashion andtherefore covers any application between low and highthroughput analysis. As a final step, FluoQ automaticallysummarizes the analysis in multiple plots and generates datatables that can be imported into any data analysis software suchas MS Excel, Origin, or R. This significantly accelerates dataanalysis and makes large amounts of data manageable. Inaddition, FluoQ saves a full documentation of all performedmanipulation steps making the whole process transparent andreproducible.Figure 1 shows the overall workflow we pursued to developnew FRET sensors based on ITPKA and ITPKB. To obtain theITPK FRET sensor constructs, truncated sequences encodingITPKA and ITPKB fragments consisting only of the activekinase and the calmodulin binding domain (Figure 1a) werecloned into a library of 30 vector backbones. This set of 30plasmids was described earlier for the development of Camk2aand DAPK1 sensors 14 (Figure 1b). The set contained thedonor fluorophore mTurquoise 19 and the acceptor fluorophoreVenusd 15 in various combinations of orientations through

Received: May 15, 2013Accepted: July 24, 2013Published: July 24, 2013

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circular permutations and linker lengths. These differentcombinations lead to a different Forster resonance energytransfer (FRET) efficiency for each backbone when expressedin cells. HeLa Kyoto cells were transfected with the reporterplasmid DNA in 8-chamber Lab-Tek dishes (Figure 1c).Transfected cells were stimulated with the ionophoreionomycin and the fluorescent intensity was followed overtime. The raw microscopy data were analyzed with the newlydeveloped ImageJ macro FluoQ allowing fast identification ofthe best performing FRET sensor (Figure 1d, e).Initial attempts were made with the vector F40, since this

backbone yielded robust ratio changes in previous screens forDAPK and Camk2a.14 Cell transfection and subsequentanalysis by fluorescence microscopy revealed good transfectionefficiency and cytoplasmic localization of the sensors. Cellswere monitored by fluorescence microscopy before and afterincreasing intracellular calcium levels and activation ofcalmodulin with ionomycin (10 μmol L−1). Binding ofcalmodulin to the ITPK insert led to a change in theorientation and/or distance of the donor and acceptorfluorophores and a difference in FRET. To extract thisinformation from the raw microscopy data, images wereprocessed and the acceptor/donor (Venusd/mTurquoise)ratio images were calculated (see Supporting Information(SI) Figure 1). Subsequently, cell images were segmented andthe mean pixel intensity for each region of interest (ROI) overtime was extracted from all channels (Figure 2a). For eachtime-lapse trace, the change in amplitude was calculatedaccording to formula 1 (Figure 2a).FluoQ was initially applied to automate and standardize the

image processing steps and the extraction and evaluation oftime-traces from single cells expressing the F40-ITPKAconstruct. FluoQ performed all of the above-mentionedimage processing and evaluation steps while also summarizingall extracted data in various spreadsheets, text files, andstatistical plots (see SI Figure 2). The initial user interface(see SI Figure 3) offers various options for each step to ensurecustomized data handling (see SI Table 1). Furthermore, allprocessed images, dialogue choices, and a full record of theanalysis are saved automatically together with the statisticalsummaries into a new folder to ensure maximal transparencyand reproducibility (see SI Table 2). FluoQ gives the user theoption to interfere with any part of the image analysis duringthe process. Being able to manually correct for potential errorsof the algorithm is a crucial feature for reliable data analysis.However, FluoQ offers also fully automated analysis. In anymode of FluoQ, each process is displayed on the screen toenable full control of how the data appear after eachmanipulation and what data will be extracted for statisticalsummaries (see SI Figure 4).FluoQ analysis of ITPKA-F40 revealed an amplitude change

of 25% in the ratio channel upon ionomycin stimulation. Thisratio change is due to an intensity increase in the donor and adecrease in the acceptor (sometimes called transfer) channel(Figure 2b and c). The ITPKB-F40 sensor was analyzed in thesame manner and a ratio change of about 10% was observed(see Figure 2c).To further increase the performance of the sensors based on

ITPKA and ITPKB, the inserts were cloned into the wholelibrary of 30 vectors. The constructs were treated and analyzedin the same manner as the F40 construct. We performed atleast six independent experiments for each construct to evaluatetechnical and biological deviation.

Figure 1. Work-flow overview for the development of new FRETreporters. (a) Domain structure of ITPKA and ITPKB and insertdesign (black, protein sequence; dark blue, substrate binding residues(both according to UniProtKB P23677 (ITPKA) and similarity(ITPKB)); light blue, residues for ATP binding; light orange,calmodulin binding site; red, amplified regions; and gray (IPK),kinase domain). (b) The ITPK insert was cloned into the FRETbackbone library that contained donor and acceptor pairs in differentorientation and linker lengths. (c) Cells were seeded in 8-chamberLab-Teks, transfected with the plasmid DNA and imaged byfluorescence microscopy. (d) Microscopy data were analyzed withthe newly developed ImageJ macro FluoQ for rapidly identifying thebest performing sensor (e).

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FluoQ is able to analyze the raw data of the whole library byprocessing multiple experimental files in a row. This batchmode is implemented in a very user-friendly manner. FluoQtakes only a single directory as input and searches automaticallyfor experiments in any subfolder that matches the conditionsselected in the initial user interface. The macro makes use ofthe LOCI Bio-Formats importer 20 and is therefore able tohandle most of the life-sciences image file formats. Also,multiple experiments saved in a single file are recognized. Thesearch results in a list where the user ticks the experiments toanalyze. In addition to the analysis of single experiments, FluoQsummarizes averaged time-traces and amplitude changes of allexperiments to immediately judge the overall outcome of theexperiment set and to simplify further data processing andanalysis (see SI Figure 4).The analysis of the library screen (the whole library is

depicted in Figure 3a) by FluoQ revealed the ratio changesshown in Figure 3b (see SI Figure 5 for averaged time-traces).On average, sensors with the ITPKA insert gave a higherdynamic range (higher ratio changes) than sensors with theITPKB insert. In addition, comparison between ITPKA andITPKB showed differences in dynamics and a different FRET-frame gave the best performance (F41 for ITPKA and F46 forITPKB). Additionally, the influence of the linker length (2, 4,or 8 amino acids) was highly variable. In some cases (ITPKAF34−F36 and ITPKB F49−F51) different linker lengths didnot influence the dynamic range of the sensors, whereas weobserved a dramatic impact in other cases (e.g. ITPKA F37−F39 and ITPKB F58−F60). Also, it was not always the shortestlinker (2 amino acids) that gave the largest ratio changedemonstrating the benefit of including different linker lengthsin the library screen. However, most of the difference in ratio

changes can be explained with the different fluorophores andtheir orientations.The three best performing backbones for ITPKA were F41

(−31.2 ± 5.2%), F49 (−28.5 ± 6.0%) and F43 (−26.5 ± 4%),whereas for ITPKB the three best performing sensors were F46(−19.6 ± 1.4%), F59 (−18.5 ± 4.6%), and F49 (−16.1 ±2.2%).Treating cells with ionomycin lead to a major increase in the

intracellular calcium concentration way beyond the physio-logical level. To investigate whether the sensors also report onmore subtle calcium changes, we stimulated cells with ATP(100 μmol L−1). This is known to lead to calcium oscillationsvia activation of purinergic receptors.21 We measured calciumlevels with the genetically encoded calcium sensor R-GECO 22

in parallel to the ratiometric ITPK probes. The resultingmultiparameter imaging data set was analyzed simultaneouslywith FluoQ, since the macro can handle any number ofintensiometric and/or ratiometric channels.Cells not expressing any sensor for ITPKA or ITPKB showed

calcium oscillations upon ATP stimulation (Figure 4, greenbar). However, calcium transients were not observed in cellsexpressing the kinase active ratiometric probes (Figure 4,orange bar, or compare averaged time-traces in SI Figure 6).Also, no FRET changes of the kinase probes were detected.Ionomycin treatment at the end of each experiment showedthat the sensors were functional. This indicated that over-expression of unregulated and constitutively active ITPKsensors interfered with the calcium signaling machinery afterATP stimulation. Presumably, Ins(1,4,5)P3 was rapidlyphosphorylated to Ins(1,3,4,5)P4, decreasing Ins(1,4,5)P3 levelsfor Ins(1,4,5)P3 receptor binding and calcium release frominternal stores.23 To further prove that the kinase activity of theprobes was responsible for the loss of calcium oscillations upon

Figure 2. Analysis of ITPKA- and ITPKB-based sensors. (a) The mean pixel intensity for each time point of one segmented cell was extracted. Theamplitude change after ionomycin (10 μmol L−1) stimulation was calculated according to formula 1. Cyan bar indicate unused time points duringionomycin treatment and signal equilibration . (b) Averaged time-traces for all cells in the field of view ordered by channel (Error bars: standarddeviation). (c) Mean of ratio changes for exploratory experiments for ITPKA-F40 and ITPKB-F40 (Error bars: standard deviation).

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ATP stimulation, we created kinase inactive mutants of the bestperforming probes by introducing a mutation at the ATPbinding site 24 (see Figure 1a for mutation sites). Byintroducing the mutations into the ITPKs, we were able tofully recover the ATP-induced calcium oscillations (Figure 4aand SI Figure 7) indicating that the sensors were now unable tointerfere with the signaling network. We also observed thatITPK mutants responded to the calcium increase upon ATPstimulation with a synchronous (within the time resolution of3s) ratio change (see SI Figure 6). The best performing probes(ITPKA-F41, F44, and F49) could follow transient calciumchanges in an oscillatory manner (see Figure 4b and SI Figure 8for more example traces). These results indicated that ITPKAwas able to translate very transient calcium signals into directkinase activity and thereby it becomes conceivable that ITPKAplays a key role in modulating calcium oscillations by limitingthe oscillatory behavior of Ins(1,4,5)P3 levels.4 We were notable to verify if this is also true for ITPKB. For ITPKA, onlysensors with a ratio change of at least 30% were able to resolvecalcium oscillations. Since for ITPKB, maximum ratio changeswere below 20%, we can only speculate if ITPKB also translatestransient calcium signals into kinase activity.For the underlying study, we applied the FRET frame library ofPiljic et al.14 to screen for novel genetically encoded FRETsensors based on ITPKA and ITPKB. We used the three best

performing candidates to study how far the activity of thesekinases respond to ATP induced calcium oscillations. We foundthat changes in the conformation of ITPKA follow receptor-mediated calcium oscillations at an equivalent pace. Thisexemplifies that oscillatory calcium signaling can be directlytranslated into transient downstream effects. We therebyunraveled an additional component of the signaling networkregulating calcium oscillations. Although, the function of theITPK product Ins(1,3,4,5)P4 is to date not fully understood,the rapid degradation of Ins(1,4,5)P3 seems to contribute to thesteep decay in each spike of the Ins(1,4,5)P3/Ca

2+ signal andtherefore promotes the sharp appearance of calciumoscillations.To make full use of the FRET frame library and their rapid

access to FRET reporter candidates, we developed FluoQ. ThisImageJ-based macro facilitates the analysis of the resultingimaging data at a single cell level in a reproducible andstandardized fashion. Using FluoQ, the time required for theanalysis of the example shown here was reduced from days tohours (see SI Figure 9). The macro is compatible with mostbioimage file formats and processes image data sets either in avery interactive or completely automatic way. The incorporatedbatch-mode automatically finds experiments in the file systemwith similar setups to simplify high-throughput analysis.Compared to other existing software solutions (e.g. Cell

Figure 3. Overview of the FRET construct library and performance of ITPKA and ITPKB sensors. (a) List of constructs (aa = amino acids, mTurq =Turquoise,19 mTurqD = Turquoise lacking C-terminal 11 aa, Ven = Venusd,15 ITPK = truncated ITPK insert (see Figure 2a) with different linkerlengths as indicated, cp = circular permutated). (b) Mean and standard deviation of ratio changes derived from averaged cell traces of independentexperiments. Experiment numbers are given in each bar.

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Profiler 25), FluoQ automatically generates multiple plots andsummaries. This renders not only the possibility for animmediate evaluation of multiple experiments but also easesfurther data processing with almost any statistical software.FluoQ is designed to visualize, quantify, and summarize cellularresponses to any stimulus one might add to cells in afluorescent microscopy setup. We are convinced that thesefeatures will have a huge impact on acceleration andreproducibility of time-lapse image analysis of many chemicalbiology tools with a fluorescent readout.In conclusion, by combining automated data analysis by

FluoQ with rapid cloning using the FRET construct cassetteand potentially automated microscopy for analysis of theconstructs,14 another step has been made toward the rapid andefficient production of semioptimized FRET sensors at a verylarge scale.The example of FRET sensors based on ITPKA and ITPKB

demonstrated not only the usefulness of the combinedapproaches, but it also highlighted ITPK as an additionalcomponent to the signaling network regulating calciumoscillations.

■ METHODS

Cloning. FRET constructs where generated using thepreviously described FRET sensor library of Piljic et al. 14

human ITPKA (amplified from IRAT p970 H0522D clonepurchased from Source Bioscience ImaGenes) and ITPKB(amplified from plasmid 32528 GFP-IP3KB26 purchased fromaddgene) were amplified by PCR using the following primers.ITPKA: ITPKA-F CGATC TACCG GTGAA GCGGG

CGAGG ACGTG GGTCA G, ITPKA-R GGTAC CACGC

GTTCT CTCAG CCAGG CTGGC CAGGA TG (536−1454bp according to NM 00220),ITPKB: ITPKB-F AGCGC TACCG GTCTG GACCA

GCAGA AACCT AGAGT GAGC, ITPKB-R AATATTACGC GTGGC GAGTG GGGCA TCCTG GGACA TC(2249−3178 bp according to NM 00221).The restriction sites AgeI and MluI were introduced through

primers in the course of PCR. Backbones and inserts weredigested with AgeI and MluI, and purified using the Qiagen GelExtraction Kit. The designated FRET construct-insert combi-nations were ligated using T4 Ligase from Fermentas. Kinasedead mutations of ITPKA and ITPKB were K246Aand K745A,respectively. They were introduced by site directed mutagenesisusing the following primers and the QuikChange LightningSite-Directed mutagenesis kit. (ITPKA K246A-F GTGCTCGACT GCGCC ATGGG CGTCA GG, ITPKA K246A-RCCTGA CGCCC ATGGC GCAGT CGAGC AC, ITPKBK745A-F TGTGT GATGG ACTGC GCCAT GGGAATCAGG ACC, ITPKB K745A-R GGTCC TGATT CCCATGGCGC AGTCC ATCAC ACA).

Cell Culture and Transfection. All cell experiments wereperformed in HeLa Kyoto cells. The cells were passaged andmaintained in DMEM consisting of 1 g L−1 glucose withoutpyruvate (Gibco), 10% FBS (Gibco), and 0.1 mg mL−1

Primocin (Invitrogen). For imaging, eight chambered Lab-Tek dishes were used. (4−5) × 105 cells per chamber wereseeded and incubated for 20−24 h. Transfection was performedat around 50% confluence in Opti-MEM (Gibco) with 1 μLFugene HD (Promega) for 100 ng of DNA. In case of doubletransfection, 75 ng of each construct was used with 2 μL ofFugene HD.

Figure 4. Performance of the three best sensors upon ATP stimulation (100 μmol L−1). (a) Effect of kinase active and inactive ITPK-based sensorson calcium transients upon ATP stimulation. Cells were transfected with R-GECO and with or without constructs encoding for kinase active(ITPKA-WT) or kinase inactive mutants. Number of calcium transients within 720 s after ATP stimulation was counted. Shown is a BoxWhiskerplot with different vectors and conditions on the x-axis and the number of calcium transients on the y-axis. Cell numbers for each condition are givenunder each bar. (b) Single cell time trace comparison of calcium and ITPK sensor activity response upon ATP (after 90 s) and ionomycin (after 810s) stimulation. Traces were rescaled and y-scales were removed for clarity. Note that only ITPKA-based sensors are suitable to follow calciumoscillations.

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Twenty to twenty-four hours after transfection, the cells weregently washed and incubated in imaging buffer (20 mmol−1

Hepes, pH 7.4, 115 mmol−1 NaCl, 1.2 mmol−1 CaCl2, 1.2mmol−1 MgCl2, 2.4 mmol−1 K2HPO4, 2 g L−1 D-glucose) at 37°C with 5% CO2 for at least 15 min before imaging. Cells werestimulated with either 10 μmol−1 ionomycin or 100 μmol−1

ATP. For imaging of calcium, the calcium sensor R-GECO(CMV-R-GECO) was used. The calcium sensor wascotransfected with the FRET construct.Microscopy. All experiments were performed on a Leica

TCS SP2 AOBS microscope (Leica Microsystems) at roomtemperature (RT). For imaging an HCX PL APO lbd.BL 40.0×/1.40 oil objective was used. The excitation and emissionsettings were kept identical for all experiments (excitationwavelength, 405 nm; emission detection of acceptor, 520−540nm; donor emission, 470−510 nm). Images were taken withfully opened pinhole, in 8 bit mode and with 10 s between twoscans. The cells were stimulated after 100 s with ionomycin at afinal concentration of 10 μmol−1.For calcium imaging R-GECO was excited with 532 nm and

emission was detected between 580 and 640 nm. Sequencedrecording was used in order to prevent the 532 nm laser toexcite the fluorophores of the FRET constructs. ATP wasadded to a final concentration of 100 μmol−1 followed byionomycin addition.Image Processing and Analysis. Images were analyzed

using FluoQ and FIJI,27 a distribution of ImageJ (FIJI is therecommended ImageJ distribution for FluoQ, since the macromakes use of several plug-ins that come with FIJI, but not withplain ImageJ). The following processing options were chosenwithin the macro: the background was subtracted usingImageJ’s built in function. A median filter (radius size = 2)was used to smooth the images. Before calculating the ratiochannel, images were transformed to 32-bit float and athreshold was applied to remove low value pixels from analysis.Cells were segmented automatically by FluoQ using thehistogram-based “Triangle” threshold algorithm to create abinary cell mask, the watershed algorithm to separate cellclumps, and finally the particle analyzer plug-in to define ROIs.FluoQ measured the mean pixel intensity of each ROI overtime and saved all measured data and calculated parameter in atext file (EXPNAME_data set.txt, see SI Table 2) that wassubsequently loaded into the R program 28 in order to do thedata analysis. Experiments that showed no or a delayed FRETresponse to ionomycin due to a pipetting error where excludedfrom the analysis. Plots were produced using the gglot2 Rpackage.29

■ ASSOCIATED CONTENT

*S Supporting InformationAdditional figures and tables as described in the text. FluoQMacro. This material is free via the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATION

Corresponding Author*E-mail: schultz@embl.de.

NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

We thank K. Sauer (Scripps Research Institute) for ITPKBconstructs, G. Reither, A. Nadler, V. Laketa, and D. Hoglingerfor beta-testing of FluoQ and K. Miura for support and advicewith the ImageJ macrolanguage. We are grateful for expertsupport by the EMBL Advanced Light Microscopy Facility inmaintaining the used microscopes. This work was partiallyfunded by Transregio 83 of the DFG and the EU-fundedIntegrated Project LIVIMODE. FluoQ Macro is available freeof charge from the Supporting Information or from https://github.com/fstein/FluoQ.

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