Flim fret software

The software is designed for a 64 bit operation system and features a graphical user interface GUI , which guides the user through all necessary steps for an individual analysis or measurement process. Data dependencies are directly visible in the underlying workspace concept. In these modes, photons on each detection channel are tagged with the absolute arrival time since the beginning of the measurement and, in certain cases, also along with the time difference to the last laser pulse.

This scheme preserves all photon timing information and allows a large range of data interpretation ranging from simple TCSPC histograms to complex imaging and correlation analysis. With the SymPhoTime 64, analysis of time-resolved imaging measurements will be easier than ever before.

Each interface only makes those procedures available that are directly required for the individual analysis. This ensures a steep learning curve as well as quick and correct analysis results. The SymPhoTime 64 also sets a new standard for analysis of fluorescence correlation spectroscopy measurements. The software provides a wide range of specially adapted correlation analysis procedures, which range from classical auto-correlation FCS and cross-correlation FCCS to lifetime based correlation analysis FLCS and total correlation.

By exploiting the full power of a multi-core computer system, the SymPhoTime 64 is one of the fastest software correlators on the market. The analysis of fluorescence intensity time traces is another core feature of the SymPhoTime Fluorescence intensity time traces display the measured fluorescence dynamics and can be analysed in a variety of ways. Multiple image processing approaches such as gSTED and Pattern Matching are implemented for even further improvement of the resolution.

The software is available in five different packages in order to meet the different needs of the individual users:. The latest version supports for the MultiHarp and includes additional antibunching functionality separate version. Multi step process to analyze the fluorescence lifetime and distribution for each of the four cells using by SPCImage software.

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Abstract Background Despite the broad use of FRET techniques, available methods for analyzing protein-protein interaction are subject to high labor and lack of systematic analysis. Introduction Over the past decades, light microscopy has been customized to facilitate the investigation of protein assembly into macromolecular complexes in living cells. Full size image. Conclusion The stand-alone software described here aims to simplify and accelerate the process of analyzing multivariate FLIM data sets for single cell lifetime quantification.

R eferences 1. PubMed Google Scholar 2. Article Google Scholar Download references. Availability of data and materials The application, the C. In addition, recent studies have provided compelling evidence that the time course as well as precise subcellular localization of cAMP increases plays a pivotal role in determining the outcome of the signaling cascade 16 , 17 , The extensive set of proteins involved in synthesis of and response to cAMP underscores the importance of this messenger.

The kinetic properties of signaling are determined by the balance of production and degradation of cAMP. Based on their sequence relatedness, kinetics, modes of regulation, and pharmacological properties, the PDEs can be divided into 11 families Selectivity in this case is defined as high substrate preference at physiological concentrations.

Genes for individual PDEs can have multiple promoters, and the transcripts are subject to alternative splicing, resulting in nearly a hundred different PDE messenger RNAs However, most cell types express only a subset of PDE family members e.

Epac-S H consists of most of the sequence of Epac-1, with mutations to render it catalytically dead as well as dislodge it from membranes by deletion of the DEP domain.

Since this sensor lacks acceptor emission, it is possible to collect photons from a large part of the donor spectrum without the need to apply corrections for the acceptor lifetime. The high FRET span and photostability of this sensor made it ideal for rapid screening purposes when the photon budget is limited. In HeLa cells grown on well plates, a specific PDE was suppressed in each well with a set of 4 different siRNA oligonucleotides, administered 72 h prior to imaging.

For completeness of the feasibility study, we included all PDE families, irrespective of their selectiveness for cAMP. Cells were automatically segmented using an established deep-learning based segmentation protocol, Cellpose 26 , and the various kinetic properties of cAMP signals in the cell interior were extracted by custom-made Python analysis routines.

For creation of the stable cell line expressing the Epac-S H biosensor 25 transfection of HeLa cells was performed with the Tol2 transposon system For transfection two plasmids are used: a cDNA with the transposase sequence and another cDNA with the following elements: Tol2 , promoter, the puromycin resistance gene, gene encoding for Epac-S H and a second Tol2 sequence. After 4 days cells were sorted on a fluorescence-activated cell sorter FACS based on mTurq2 fluorescence intensity.

After incubation for at least 48 h, cells were imaged in fresh serum-free F12 culture medium Gibco, 21,— on 96 well cell culture microplates Greiner Bio-one, , This FLIM sensor features a tandem dark i. Cells were excited with a pulsed diode laser PicoQuant at nm, and photon arrival times were recorded with two HyD detectors, together covering the mTurquoise emission spectrum, adjusted to count photons at approximately equal rates — nm and — nm, respectively.

Experiments were conducted in Well plates Greiner Bio-one, using a 20X 0. In all experiments, the position of the focal plane was actively stabilized using the Leica Auto Focus Control AFC to prevent focal drift or focus artifacts from pipetting of stimuli.

The recorded photon arrival time histograms showed multi-exponential decay, suggesting the superposition of different FRET states. The resulting two images contained the amplitudes of these two components and were saved as TIF files, reducing the amount of raw data more than fold. In order to relate to conventionally reported lifetime values we map the ratio of the components back to the original 0. As rapid and reliable mixing of the stimuli was paramount in these experiments, we first compared several methods of administration, including replacing the entire well content with new medium containing each stimulus, administration from an automated pipetting system and manual pipetting from concentrated stock solutions.

A graphical representation of the automated workflow and analysis pipeline is shown in Fig. All raw data is available on Zenodo 28 and custom written software with the link to the corresponding Zenodo data repository can be found on our GitHub page These are the steps that are taken in the analysis, they can be repeated by running the software found online:.

Cells are segmented using the deep learning algorithm Cellpose Due to minimal cell movement during acquisition, intensity data from all frames can be combined.

The mean intensity is sent to Cellpose for deep-learning based cell segmentation. In the final step of the analysis is the generation of the figures using individual Jupyter notebooks that can be found in the GitHub repository We initially used a conventional image analysis approach for cell segmentation by implementing Voronoi segmentation in an ImageJ macro Github 29 , using the thresholded signal of nuclei stained with SiR-DNA as seeds. Note that nuclear labeling proved not to be necessary for Cellpose segmentation.

In comparing two independent segmentation runs, one with and the other without inclusion of the SiR-DNA channel, Cellpose yielded comparable high-precision labelmaps for all imaged FOVs. The results of Voronoi segmentation and Cellpose were in very good agreement, with the former providing significantly faster segmentation, at the expense of slightly reduced reliability and dependance on nuclear seeds. Since analysis time was not restrictive, we include segmentation with Cellpose in this study.

For further details, see Fig. S1 and the text. Global fitting indicated dominant lifetime components of 3. Saturation of the sensor with cAMP, as induced by the treatment of cells with the direct AC activator forskolin, changed the relative magnitude of the two populations but not their lifetimes.

All time-lapse images were therefore fitted with a n-Exponential Reconvolution model using two fixed lifetime components of 3. However, possible photodamage, bleaching, and the necessary throughput set upper limits to the excitation power and acquisition time. To reliably resolve small differences in cAMP concentration, we aimed to achieve a lifetime repeatability i.

It can be seen in Fig. The lifetimes of FRET sensors at resting state appeared near-normally distributed 2. Interestingly, a small percentage of cells with slightly increased cAMP levels were found Fig.

When imaged 2 days after culturing, these cells usually grouped together, suggesting clonal differences in baseline cAMP levels in WT HeLa cells.

We also noticed that in most cells cAMP levels do not return to the initial resting values after transient stimulation with NE. B Calibration bar: lifetime in ns. Panel C shows the ROIs color-coded for each individual cell, as segmented using Cellpose, overlayed with fluorescence intensity. The bold black line represents the mean of all cells. E Distribution of the baseline values average of 20 samples for each cell.

Yellow arrows in A , D and E indicate cells with higher baseline lifetimes. The required throughput is determined by both the temporal resolution necessary to capture cAMP dynamics time-lapse interval and by the number of cells to be recorded from. The latter depends on several factors, including cell-to-cell variability due to stochastic differences inherent in signal transduction cascades and on incomplete penetration of the genetic perturbations carried out in the screen.

Most siRNA mediated knockdown experiments display considerable variability in gene silencing resulting in incomplete or even no detectable knockdown in a percentage of cells Our pilot studies showed that recording from a few hundred cells in a single FOV captured most of the variation in each well. To minimize the risk that factors such as ongoing aging of the medium and increasing cell confluency might bias the results, we decided to run each entire screen, i.

Under these conditions, we found near-identical lifetimes in the experiments recorded at the onset and at the end of the 6-h long screen Fig.

To optimize automated image analysis on a cell-by-cell base, we started by comparing algorithms for reliable segmentation of individual cells. We initially adapted standard image analysis methods by generating a dedicated Image J macro tailored to our cells. In essence, cell nuclei were detected by in vivo staining with SiR-DNA, followed by Voronoi segmentation to determine cell boundaries, which was based on the time-averaged intensity of the time-lapse images.

This macro 29 yielded good results, i. However, while our experiments were in progress, a general algorithm for segmentation of cells based on deep-learning algorithms was reported 26 , the performance of which we tested against our own developments. In several independent experiments we found Cellpose 26 to be superior in reliability compared to more conventional image segmentation algorithms, including our own developments Fig.

For each individual cell ROI , we extracted mean fluorescence intensity and donor lifetime Fig. These data also were used to calculate RMS noise values of intensity and lifetime signals. Moreover, after fitting the agonist induced responses of cells to a suitable model Fig.

The time-average of fluorescence intensity was used for segmentation using Cellpose, whereas the fluorescence lifetime data were fitted with a double-exponential decay using fixed fast and slow components of 0.

The magnitudes of those two components were exported to Python for further analysis. Based on the segmented ROIs, lifetime data were plotted for each individual cell, subjected to quality control, and agonist-induced changes were fitted with a suitable model. The fitting parameters are then summarized in descriptive plots. Next, we tested the reproducibility of our results with different batches of cells on different days.

Baseline values were slightly more variable Table S2 , most likely reflecting small batch-to-batch variations in basal cAMP levels. These observations stress the importance of carrying out signaling screens within a limited time span, i.

Uncaging with a ms UV pulse caused an immediate increase in intracellular cAMP levels, and thus in donor lifetime, which subsequently returned towards its baseline level Fig. Hundreds of cells within a single FOV were imaged every 2 s for at least s or longer, if slow recovery called for that and acquired data was stored for analysis offline. The time trace right is from the green cell indicated in the left.

Cells were imaged every 2 s and uncaging was at 25 s using a ms flash of UV light. Orange line shows the logistic function fitted to the data.

The reported breakdown time black arrow is the time between the vertical two lines. The lower right panel shows the fit residuals. From these data, it is apparent that knockdown of PDE3A markedly affects the breakdown time in these cells Datapoints are fitted decay times of single cells. For each condition, the experiment was performed in duplicates, with cells grown, transfected, and assayed in two independent wells.

Cells were imaged every 2 s; uncaging at 25 s using a ms flash of UV light. From the data in Fig. Moreover, the extremely large span of the observations seen for knockdown of PDE3A and PDE10A suggest that lack of or incomplete PDE knockdown in individual cells is a further major determinant of variability in these wells.

Furthermore, cell shape differences, e. This view is supported by the observation that very similar results were obtained when we repeated selected conditions, again in duplicate, a month later. It is also noteworthy that unlike PDE knockdown, inhibitor pretreatments selectively wiped out the population of cells with fastest breakdown times, consistent with the notion that high variability in breakdown speeds in the population of PDE3A and PDE10A knockdown cells reflects incomplete knockdown by siRNAs.

The mechanisms involved remain to be elucidated in further studies. Further details are as in Fig. While analyzing these data, we noted that baseline donor lifetimes in cells pretreated with DMNB-cAMP showed considerable biological variability, ranging between 2.

In contrast, untreated cells had average lifetimes of 2. The difference increased when cells were incubated with increasing concentrations of DMNB-cAMP, indicating some leakiness spontaneous decomposition of the caging group in the cells of this compound. S4 A , but not in untreated controls. We also noted that in the vast majority of stimulated cells, cAMP levels eventually returned to their pre-stimulation value Fig.

S4 C,D. A similar observation holds true for PDE knockdown cells. We conclude that our screening platform is well suited to resolve even minor differences in cAMP clearance kinetics, and that variability between experiments carried out several weeks apart is only minor.

We therefore redesigned the experimental paradigm to circumvent the confounding effect of caged cAMP. Dynamic screens can also be carried out when AC is activated following stimulation of GPCRs with their cognate ligands. However, receptor inactivation is much slower and, in most cases, not complete: a small proportion of receptors is thought to recycle to the plasma membrane where they can become reactivated by the agonist and continue to signal Therefore, we adopted a protocol in which cells were stimulated with a receptor agonist, followed within 10—15 s by addition of excess of a potent competitive antagonist.

We first stimulated HeLa cells with 40 nM of the receptor agonist isoproterenol which caused a rapid rise in cAMP levels and subsequently added the antagonist propranolol at 60 nM concentration which caused a sharp decline following the stimulation. Figure 6 shows a representative single-cell lifetime trace along with a fitted logistic curve capturing the decay kinetics. Assessing receptor mediated cAMP pathway. Also shown is a logistic fit to estimate the cAMP breakdown time orange. Importantly, we noted that cAMP decay rates as determined following this experimental protocol in WT cells were approximately equal to those measured after photorelease of caged cAMP.

This implicates that following addition of propranolol, all upstream steps in the signaling cascade became inactivated within seconds. It was also confirmed that when propranolol was added as first stimulus, no detectable response followed upon subsequent addition of isoproterenol. Figure 7 shows effects of individual knockdown of the same set of 22 PDEs assayed according to this protocol.

The effects of knockdown of other PDEs were not significant. Furthermore, the double knockdown of PDE isoforms 3A and 10A together is also in good agreement with the data from the first screen after photorelease of caged cAMP.