Applications Leica TCS SMD FLIM

Example of FLIM-FRET application

Using FLIM, the FRET efficiency is calculated from the difference of donor lifetime in absence and presence of the acceptor. For reliable quantification the detection window must match the donor emission spectrum. It must exclude any other fluorescence, such as autofluorescence or acceptor fluorescence.

Finding the correct detection range can be time-consuming when band pass filters have to be switched. A faster and more convenient approach is based on spectrally resolved detection. The FLIM wizard contains a xyλ-mode which allows the user to automate recording of a wavelength scan. This option is exclusively available to systems containing internal SP FLIM detectors.

Sample: Identify optimal spectral detection range for unbiased FRET (see steps 1 to 4 ). 
GFP, GFP-mCherry tandem, GFP+mCherry were expressed in HeLa cells. A FLIM λ series of the three samples was acquired. The plot of average lifetime over detection wavelength shows that the optimal FRET detection window ranges from 485–545 nm (constant values). Courtesy: M. Weiss, J. Szymanski, DKFZ, Heidelberg.

Example of SP FLIM application

Many biological samples exhibit autofluorescence. Its often broad spectra can interfere with fluorescent labelling strategies.

However, autofluorescence as such has its merits, too. It is an intrinsic form of fluorescent label which comes for free and serves as completely non-invasive contrast.

Even more, the combination of spectral imaging with fluorescence lifetime information is especially useful to distinguish and potentially identify different fluorescent species in biological samples. An automated acquisition of the underlying FLIM λ stacks can be easily done within the FLIM wizard using SP FLIM detectors.

Sample

• Lambda-Stacks of FLIM images of invading pathogenic hyphae in tomato fruit (1) using autofluorescence excited with 470 nm.
• Spectral scan of 50 nm steps from 440 to 740 nm (2).
• Intensity spectra (3) and lifetime spectra (4) show strongly overlapping, non-separable species.
• Spectral information with fluorescence lifetimes allows disentangling a complex mixture of autofluorescent species (i.e. “fingerprinting” of each species).

Protein activity is often affected by the local environment, in particular by pH, ion concentration, and polarity.

Fluorescence Lifetime Imaging FLIM identifies environmental changes, both spatially and temporally resolved.

Application
A GFP fusion protein was expressed in living cells that localizes to different cellular structures. Subcellular localization of this GFP fusion protein gives rise to two protein populations.

Each population exhibits a different average lifetime as revealed by the lifetime changes with localization to different organelles (yellow and red lines).

We thank Dr. Matthias Weiss, Dr. Jedrzej Szymanski and Nina Malchus, DKFZ, Heidelberg, Germany

The FLIM wizard is a special part of the confocal software LAS AF.

Using this wizard the user defines and optimizes all relevant parameters for FLIM acquisition. These settings are transferred to SymPhoTime software and automatically used by it.

The SMD FLIM wizard offers a variety of FLIM scan modules to set up for more complex sequences of FLIM data acquisition. Furthermore, all photon events are fed back to LAS AF permitting easy optimization of data quality and automated brightness control.

Special features

• FLIM volume stacks (xyz and xzy) give information about the lifetime distribution in ticker samples such as tissues and small organisms
• With FLIM time series the researcher can follow dynamic changes of fluorescence lifetimes, especially in live cells and tissues
• Slow dynamics in thicker samples can be followed by combining volume stacks and time series
• For characterization of special lifetime properties an automated spectral FLIM stack can be acquired
• Automated brightness control provides maximum data reliability. This unique feature generates images with pre-defined brightness
• During FLIM acquisition an online FAST-FLIM image display already yields information about data quality

Application example: 3D reconstruction of FLIM z-stack
Automated 3D-FLIM stacks were acquired using the SMD wizard. The autofluorescence FLIM data reveal complex geometrics. The 3D reconstruction was done using external software.

The Leica TCS SMD FLIM is a dedicated system for FLIM measurements. These measurements reveal information about the direct neighborhood of molecules on the nanometer scale. The local conditions can determine folding states that influence the reactivity of molecules.

Furthermore, FLIM is used for binding studies (FLIM-FRET) and to identify measurement conditions and allows the setup of FLIM time series or FLIM stacks.

Special features

• Online display of Fast-FLIM-images, count rate and photon number allow to evaluate data quality during a running experiment
• A variety of pulsed lasers from UV via VIS to MP excitation can be selected according to the researchers needs
• Spectral FLIM detection combines lifetime and spectral information for the analysis of more complex fluorescence properties
• Leica TCS SMD FLIM systems equipped with PDM APDs from MPD are ready for an upgrade with FCS to an Leica TCS SMD FLCS system

Typical FLIM applications

Non-stained samples:

• Metabolic state characterization
• Cancer research: identification of modified cells
• Plant physiology: pathogen-host interaction
• Identification of chitin of lignin structures

Stained samples:

• Energy transfer (FLIM-FRET for molecule binding)
• Molecular sub-states (e.g. isomerization, protonation)
• Local concentration of ions and small ligands
• Environmental studies (viscosity, refractive index, pH)
• Dye separation
• Photophysical dye properties

Application Example (antennae of scale insect)
3D rendering of FLIM z-stack using autofluorescence. Color coded lifetime images were segmented according to chitin lifetime for isosurface rendering (green) and volume rendering of surrounding structures (red). Rendering was done using external software. Sample courtesy of Prof. Kees Jalink, NKI, Amsterdam, Netherlands