Laser Scanning Confocal Microscopy (LSCM) at CTEM - Servicio de Microscopía Confocal y Electrónica de Transmisión de la EEZ. CSIC

Laser Scanning Confocal Microscopy (LSCM) is an advanced fluorescence imaging technique that provides high-resolution optical sections of biological samples. The system uses a focused laser beam to scan the specimen point by point, while a spatial pinhole located in front of the detector removes most of the out-of-focus fluorescence. This produces images with improved contrast and spatial resolution compared with conventional widefield fluorescence microscopy.

By acquiring consecutive optical sections at different depths, LSCM enables the visualization of internal structures without physically sectioning the sample. These optical sections can be combined to generate three-dimensional reconstructions of cells, tissues and microorganisms. The technique also supports multichannel imaging, allowing several fluorescent markers to be detected within the same sample and facilitating the study of their spatial distribution and potential colocalization.

The Leica STELLARIS platform is a point-scanning confocal system designed for flexible and sensitive fluorescence imaging. Depending on the installed configuration, the platform may combine tunable laser excitation, spectral detection and highly sensitive photon-counting detectors. These features enable precise selection of excitation and emission ranges, efficient detection of weak fluorescence signals and improved separation of fluorophores with partially overlapping emission spectra.

Advanced tools available on STELLARIS platforms may include spectral imaging, fluorescence-lifetime-based contrast through TauSense, and LIGHTNING confocal super-resolution processing. These technologies can provide additional contrast, improve image quality and help distinguish fluorophores or biological structures that are difficult to separate using fluorescence intensity alone. Availability of these functions depends on the specific configuration and software modules installed in the instrument.

LSCM can be applied to both fixed and living samples, including cultured cells, animal and plant tissues, microorganisms, organoids and other fluorescently labelled biological materials. It is particularly useful for studying cellular and subcellular organization, protein and molecular localization, cytoskeletal structures, organelles, tissue architecture, host–microorganism interactions and dynamic cellular processes.

Typical applications include:

• Acquisition of high-resolution single-plane fluorescence images.

• Sequential and simultaneous multichannel fluorescence imaging.

• Optical sectioning and acquisition of z-stacks.

• Two-dimensional and three-dimensional reconstruction of biological structures.

• Colocalization and spatial distribution studies.

• Spectral separation of fluorophores with overlapping emission profiles.

• Time-lapse imaging of living cells and dynamic biological processes.

• Quantitative analysis of fluorescence intensity, morphology, area, volume and object distribution.

• Imaging of large sample areas through mosaic or tile-scanning acquisition.

• Photobleaching-based experiments, such as fluorescence recovery after photobleaching, when supported by the experimental configuration.

The main outputs include high-resolution two-dimensional images, three-dimensional reconstructions, multichannel fluorescence datasets, time-series recordings and quantitative measurements of fluorescent structures. Consequently, LSCM is a powerful and versatile technique for investigating the morphology, organization, localization and dynamics of biological systems at cellular and subcellular levels.

Spectral imaging is an advanced fluorescence microscopy technique that records the spectral characteristics of the fluorescence emitted by a biological sample. Unlike conventional multichannel imaging, in which fluorescence is collected within a small number of predefined detection windows, spectral imaging acquires the emission signal across a wider wavelength range using a series of narrow spectral intervals. This generates a spectral profile for each pixel or region of the image.

The Leica STELLARIS confocal platform combines tunable excitation, sensitive spectral detection and computer-controlled adjustment of the detection bandwidth. Its White Light Laser, Acousto-Optical Beam Splitter and prism-based spectral detection system provide considerable flexibility for selecting excitation wavelengths and emission ranges according to the fluorophores present in the sample. Depending on the system configuration, multiple excitation lines can be selected across the visible and near-infrared range, facilitating the design of complex multicolour experiments.

A typical spectral imaging experiment involves acquiring a lambda stack, in which a series of images is collected at consecutive emission wavelength intervals. Each image represents the fluorescence intensity detected within a defined spectral band. When the complete lambda stack is analysed, an emission spectrum can be reconstructed for each pixel, cellular structure or selected region of interest.

The spectral profiles obtained from the sample can be compared with reference spectra from individual fluorophores, unstained controls or known sources of autofluorescence. This information can then be used to identify the fluorescent components present and determine their relative contributions to the recorded signal.

Spectral unmixing

Spectral unmixing is a computational procedure used to separate the contributions of different fluorescent species when their emission spectra overlap. The recorded fluorescence signal at each pixel is treated as a combination of the signals produced by the different fluorophores and autofluorescent components present in the sample.

Using reference spectral profiles and appropriate mathematical algorithms, the mixed signal can be decomposed into separate image channels corresponding to each fluorescent component. The resulting unmixed images generally show reduced spectral cross-talk and improved discrimination between markers compared with images obtained using conventional fixed emission windows.

Spectral unmixing is particularly valuable when:

• Several fluorophores are detected simultaneously.

• The emission spectra of the fluorophores partially overlap.

• The sample presents strong or heterogeneous autofluorescence.

• A fluorescent signal is weak relative to the background.

• Available fluorophores cannot be separated adequately using conventional filters.

• Complex tissues contain several endogenous fluorescent components.

• A large number of fluorescent markers must be analysed in the same sample.

The reliability of spectral unmixing depends strongly on the quality of the reference spectra. Whenever possible, reference spectra should be acquired using samples labelled separately with each fluorophore and under imaging conditions equivalent to those used for the experimental sample. An unstained or untransfected sample should also be analysed to characterize the intrinsic autofluorescence of the specimen.

Reference spectra obtained only from fluorophore databases may not fully reproduce the spectra observed in the experimental sample. Fluorescence emission can be influenced by pH, polarity, molecular binding, fluorophore concentration, fixation procedures and the local cellular environment. Consequently, experimentally acquired reference controls generally provide more reliable spectral separation.

Autofluorescence separation

Spectral imaging is especially useful for analysing biological materials with substantial endogenous fluorescence. Plant tissues, microorganisms, extracellular matrices, fixed tissues and samples containing pigments or accumulated metabolites may produce broad and complex autofluorescence signals.

When the autofluorescence spectrum differs sufficiently from that of the fluorescent marker, it can be treated as an additional spectral component and computationally separated from the specific signal. This can improve the detection of labelled proteins, organelles or cellular structures that would otherwise be partially obscured by the background.

This application is particularly relevant for plant samples containing chlorophyll, phenolic compounds, lignin or other naturally fluorescent molecules. However, spectral unmixing cannot completely recover a specific signal when the fluorescence marker is extremely weak or when its spectral profile is virtually identical to that of the background.

Lifetime-assisted separation

In addition to conventional wavelength-based separation, the Leica STELLARIS platform can use fluorescence lifetime information through TauSense tools. Fluorescent species with similar emission spectra may display different fluorescence decay characteristics and can therefore be distinguished using lifetime-derived contrast.

Tools such as TauScan and TauSeparation can complement spectral detection by exploiting differences in fluorescence lifetime. This can improve the separation of spectrally overlapping fluorophores and help distinguish specific fluorescence from autofluorescence when wavelength information alone is insufficient.

Lifetime-assisted separation should be distinguished from conventional spectral unmixing. Spectral unmixing separates components according to their emission spectra, whereas TauSeparation uses differences in fluorescence decay behaviour. Both types of information may provide complementary contrast in complex multicolour samples.

Typical applications

Spectral imaging and spectral unmixing can be applied to:

• Multicolour fluorescence imaging.

• Separation of fluorophores with overlapping emission spectra.

• Identification and characterization of fluorescent signals.

• Discrimination of specific labelling from tissue autofluorescence.

• Imaging of highly autofluorescent plant and animal samples.

• Analysis of fluorescent proteins, antibodies and chemical probes.

• Colocalization studies with reduced spectral cross-talk.

• Detection of weak fluorescent markers in complex backgrounds.

• Analysis of endogenous pigments and fluorescent metabolites.

• Characterization of fluorescent nanoparticles and biomaterials.

• Multiplex imaging of cells and tissues.

• Live-cell and fixed-sample imaging.

• Three-dimensional spectral imaging of z-stacks.

• Monitoring spectral changes associated with the molecular environment.

Experimental considerations

The excitation wavelength, emission range, spectral interval width and acquisition sequence must be optimized according to the fluorophores and biological sample. Narrower spectral intervals provide more detailed spectral information but increase acquisition time and may expose the sample to additional illumination.

For live-cell experiments, acquisition conditions should be adjusted to minimize photobleaching, phototoxicity and spectral changes caused by cellular stress. Sequential acquisition may reduce cross-excitation and cross-talk but can be unsuitable for very rapid biological processes because the different spectral channels are not acquired at exactly the same time.

Quantitative comparisons require standardized acquisition settings, appropriate controls and avoidance of detector saturation. Spectral bleed-through, direct excitation of secondary fluorophores, differences in fluorophore brightness and sample movement must also be considered when interpreting the results.

Main outputs

The principal outputs include:

• Spectral or lambda image stacks.

• Pixel-associated fluorescence emission spectra.

• Reference spectral libraries.

• Unmixed images for each fluorophore or autofluorescent component.

• Spectral distribution maps.

• Multichannel two-dimensional and three-dimensional reconstructions.

• Quantitative measurements of fluorescence intensity after signal separation.

• Improved colocalization and spatial-distribution analyses.

Spectral imaging and spectral unmixing therefore provide a flexible approach for analysing complex fluorescence patterns, improving multicolour discrimination and separating specific molecular labelling from endogenous autofluorescence in biological samples.

Photoactivation, photoconversion and photoswitching are fluorescence microscopy techniques used to selectively alter the fluorescent state of light-responsive probes within a defined region of a biological sample. These probes, often referred to as optical highlighters, include genetically encoded fluorescent proteins and synthetic fluorescent molecules whose emission properties can be modified by illumination at specific wavelengths.

Unlike conventional fluorescent markers, which remain continuously fluorescent after excitation, optical highlighters allow selected molecular populations to be activated, converted or switched at a particular time and location. This makes it possible to track the movement, redistribution, turnover and fate of proteins, cells and subcellular structures in living specimens.

The Leica STELLARIS confocal platform can be used to define precise regions of interest, illuminate them with selected laser wavelengths and subsequently monitor changes using multichannel or time-lapse imaging. The combination of spatially controlled laser illumination, flexible excitation and spectral detection makes these approaches particularly valuable for dynamic studies in living cells and tissues.

Photoactivation

Photoactivation involves converting a fluorescent probe from a non-fluorescent or weakly fluorescent state into a strongly fluorescent state by illumination with a specific activation wavelength. In many commonly used photoactivatable fluorescent proteins, this transformation is irreversible.

Before activation, the labelled molecule produces little or no detectable signal. A selected cellular region is then exposed to the activation laser, causing only the molecules located within that region to become fluorescent. The activated molecular population can subsequently be followed over time.

Photoactivation can be used to investigate:

• Protein movement between cellular compartments.

• Intracellular trafficking and molecular transport.

• Protein redistribution and turnover.

• Cell migration and lineage tracing.

• Movement and fusion of organelles.

• Diffusion of molecules within membranes or the cytoplasm.

• Exchange between biomolecular condensates and their environment.

• Spatially restricted signalling events.

Because only a selected molecular population is activated, the technique allows molecules originating from a specific region to be distinguished from identical molecules elsewhere in the sample.

Photoconversion

Photoconversion involves changing the fluorescence emission of a probe from one spectral state to another. A photoconvertible fluorescent protein may initially emit in one colour and, after illumination at a defined wavelength, emit at a different wavelength.

For example, a population initially detected in a green channel may be converted so that it subsequently emits in a red channel. The converted molecules can then be distinguished from the remaining non-converted population.

A typical photoconversion experiment includes:

1. Acquisition of images before conversion.

2. Selection of a region of interest.

3. Illumination of that region with the appropriate conversion wavelength.

4. Confirmation of the decrease in the original fluorescence and appearance of the converted signal.

5. Time-lapse monitoring of the converted molecular population.

Photoconversion is particularly useful for studying:

• Protein trafficking and redistribution.

• Organelle dynamics and continuity.

• Cell migration and developmental fate.

• Movement of proteins through cellular networks.

• Exchange between subcellular compartments.

• Protein turnover and degradation.

• Cell–cell interactions.

• Long-range transport in cells, tissues and model organisms.

• Dynamic processes in plant cells and tissues.

Photoconversion experiments generally require at least two detection channels: one for the original fluorescent state and another for the converted state. Leica systems can combine spatially defined confocal photomanipulation with subsequent fluorescence imaging, including long-term observation when suitable imaging and environmental-control modules are available.

Photoswitching

Photoswitching refers to the reversible transition of a fluorophore between fluorescent and non-fluorescent states, or between two different fluorescent states. The transition is normally controlled by illumination with one or more specific wavelengths.

A photoswitchable probe may be switched from an “on” state to an “off” state and subsequently returned to the fluorescent state. Depending on the probe, this cycle can be repeated multiple times. This distinguishes reversible photoswitching from conventional photoactivation, which is frequently irreversible.

Photoswitching can be applied to:

• Repeated highlighting of selected molecular populations.

• Analysis of protein mobility and turnover.

• Tracking of dynamic cellular structures.

• Investigation of molecular exchange between compartments.

• Measurement of residence times.

• Study of membrane and organelle dynamics.

• Single-molecule localization microscopy.

• Super-resolution imaging with suitable probes and microscope configurations.

The number of switching cycles, fluorescence brightness, switching efficiency and photostability depend strongly on the fluorescent probe and experimental conditions.

Experimental workflow

A typical experiment consists of three main phases:

1. Pre-manipulation imaging

Images are acquired before photoactivation, photoconversion or photoswitching to establish the initial fluorescence distribution and confirm sample stability.

Laser power should be kept sufficiently low to minimize unintended activation, conversion, switching or photobleaching.

2. Spatially controlled photomanipulation

A defined region of interest is illuminated using the wavelength and laser intensity required for the selected optical highlighter. The region may correspond to a point, line, cellular compartment, complete cell or larger tissue area.

The duration and intensity of illumination must be optimized to achieve efficient photomanipulation without causing excessive photobleaching, phototoxicity or cellular damage.

3. Post-manipulation monitoring

After photomanipulation, the activated, converted or switched molecules are monitored by acquiring images at defined time intervals. Depending on the experiment, this may involve single optical sections, z-stacks, multiple positions or multidimensional time-lapse datasets.

Quantitative analysis can then be performed by measuring fluorescence intensity, spatial distribution, displacement or redistribution of the highlighted molecular population.

Quantitative analysis

The resulting datasets can be analysed to obtain information about:

• Movement of activated or converted molecules.

• Rate and direction of molecular transport.

• Redistribution between cellular compartments.

• Retention within a defined structure.

• Molecular turnover and replacement.

• Residence time in specific cellular locations.

• Changes in the size, shape or position of labelled structures.

• Percentage of signal remaining within or leaving a region.

• Cellular migration and lineage progression.

Regions of interest can be used to compare the fluorescence intensity of the manipulated population in different cellular locations over time. Particle tracking, object tracking and three-dimensional reconstruction may also be used when labelled structures or cells move through the sample.

Typical fluorescent probes

Examples of optical highlighters include:

• Photoactivatable fluorescent proteins such as PA-GFP and PAmCherry.

• Photoconvertible proteins such as Kaede, KikGR, EosFP, mEos and Dendra.

• Reversibly photoswitchable proteins such as Dronpa.

• Photoactivatable or photoswitchable synthetic fluorophores.

• Genetically encoded constructs targeted to specific proteins, organelles or cellular compartments.

Probe selection depends on the available activation and excitation wavelengths, emission spectra, brightness, maturation time, photostability, oligomerization state and compatibility with the biological system.

Applications

Photoactivation, photoconversion and photoswitching can be applied to:

• Live-cell fluorescence imaging.

• Protein localization and trafficking.

• Nuclear–cytoplasmic transport.

• Membrane-protein mobility.

• Organelle dynamics and fusion.

• Cytoskeletal rearrangement.

• Vesicle transport.

• Cell migration and lineage tracing.

• Developmental biology.

• Host–microorganism interactions.

• Cell–cell communication.

• Plant-cell transport and tissue development.

• Analysis of protein turnover.

• Study of biomolecular condensates.

• Spatially restricted analysis of signalling pathways.

• Super-resolution microscopy using appropriate photoswitchable probes.

Experimental considerations

Successful experiments require careful selection of the fluorescent probe and optimization of the activation, conversion or switching conditions. The wavelengths available on the microscope must be compatible with both the photomanipulation and subsequent imaging of the probe.

Important variables include:

• Activation or conversion wavelength.

• Laser intensity and exposure duration.

• Size and geometry of the selected region.

• Acquisition frequency.

• Fluorescence-detection windows.

• Efficiency of activation, conversion or switching.

• Photobleaching during post-manipulation imaging.

• Phototoxicity and cellular viability.

• Movement of the sample during acquisition.

• Spectral cross-talk between the original and modified states.

• Expression level and subcellular localization of the probe.

Unmanipulated control regions should be monitored to distinguish experimentally induced changes from conventional photobleaching or fluctuations in fluorescence intensity. Non-activated or non-converted samples can also be used to evaluate spontaneous changes in the fluorescent state.

For living specimens, appropriate control of temperature, CO₂, humidity and culture conditions may be required. Acquisition settings should be optimized to maintain sample viability, particularly during prolonged time-lapse experiments.

Main outputs

The principal outputs include:

• Images acquired before and after photomanipulation.

• Time-lapse sequences of activated, converted or switched molecular populations.

• Multichannel images showing original and converted fluorescent states.

• Two-dimensional and three-dimensional tracking datasets.

• Fluorescence-intensity curves.

• Spatial maps of molecular redistribution.

• Measurements of displacement, transport and residence time.

• Quantitative analysis of molecular or cellular turnover.

• Three-dimensional and four-dimensional reconstructions of dynamic processes.

Photoactivation, photoconversion and photoswitching therefore provide powerful approaches for selectively labelling molecular populations and following their behaviour with high spatial and temporal control. They are especially valuable for investigating dynamic biological processes that cannot be resolved using conventional static fluorescence labelling.

Fluorescence Localization After Photobleaching (FLAP) is a ratiometric fluorescence microscopy technique used for the localized photolabelling and subsequent tracking of selected molecular populations in living cells. The method was developed to investigate the movement, redistribution and fate of specific molecules while correcting for changes in fluorescence caused by sample movement, variations in cell morphology or fluctuations in the amount of labelled material within the observation region.

FLAP requires the molecule or cellular structure of interest to be labelled with two spectrally distinguishable fluorophores. One fluorophore is selectively photobleached within a defined region of interest, whereas the second fluorophore remains fluorescent and acts as an internal reference. The relationship between the bleached and reference signals produces a spatially restricted optical mark that can subsequently be followed over time.

Principle of the technique

Before photobleaching, the two fluorescent labels should display approximately the same spatial distribution because they are associated with the same molecular population or biological structure. A selected region is then illuminated at high intensity using a wavelength that photobleaches one fluorophore while producing minimal bleaching of the reference fluorophore.

After selective photobleaching, the fluorescence intensity of the bleached channel decreases within the selected region, whereas the reference channel remains comparatively unchanged. A ratio image is calculated using the fluorescence intensities recorded in the two channels.

The resulting ratiometric image identifies the initially photobleached molecular population, even though the bleached fluorophore itself is no longer directly visible. The non-bleached fluorophore provides positional information about the labelled molecules and compensates for changes in their concentration, movement or morphology. The ratio can therefore be followed in subsequent images to determine where the photolabelled molecular population moves over time.

Labelling strategies

The two fluorophores can be introduced using different experimental strategies. The molecule of interest may be directly labelled with two fluorescent dyes, or two fluorescently labelled molecules may be associated with the same molecular complex or cellular structure.

Possible approaches include:

• Dual labelling of the same protein or molecular population.

• Labelling of a protein with two spectrally distinct fluorescent probes.

• Use of a fluorescent fusion protein together with a second reference label.

• Dual fluorescent labelling of antibodies, ligands or molecular complexes.

• Labelling of the same cellular structure using two compatible markers.

For reliable ratiometric tracking, both labels should remain associated with the same molecular population throughout the experiment. Differences in dissociation, degradation, trafficking or binding between the two labels may alter the fluorescence ratio and complicate interpretation.

The two fluorophores must also be spectrally distinguishable, and the bleaching wavelength should preferentially affect only one of them. Spectral cross-talk, cross-excitation and unintended bleaching of the reference fluorophore should therefore be evaluated experimentally.

Experimental workflow

A typical FLAP experiment consists of the following stages:

1. Pre-bleach acquisition

Images of both fluorescent channels are acquired before photobleaching. These images establish the initial spatial distribution of the two labels and provide a baseline fluorescence ratio.

Several pre-bleach frames may be collected to confirm that:

• Fluorescence intensity is stable.

• The two labels show comparable distributions.

• The specimen is not moving excessively.

• Photobleaching during normal image acquisition is limited.

• Neither fluorescence channel is saturated.

2. Selection of the photobleaching region

A region of interest is defined within the cell or biological structure. The selected region may correspond to:

• A portion of the cytoplasm.

• A membrane domain.

• A region of the nucleus.

• A cytoskeletal structure.

• A cell–cell junction.

• An organelle.

• A moving cellular structure.

• A defined region within a tissue.

The geometry and size of the region should be selected according to the biological process and expected molecular movement.

3. Selective photobleaching

One fluorophore is photobleached within the selected region using high-intensity laser illumination. The second fluorophore is maintained as the non-bleached reference signal.

Bleaching conditions must be optimized to produce a sufficient change in the ratio while minimizing:

• Phototoxicity.

• Damage to the biological structure.

• Bleaching of the reference fluorophore.

• Bleaching outside the selected region.

• Unwanted activation or conversion of other fluorescent probes.

4. Post-bleach time-lapse acquisition

After photobleaching, both fluorescence channels are acquired repeatedly using low excitation intensity. Depending on the sample and biological process, acquisition may involve:

• Single optical sections.

• Rapid time-lapse series.

• Z-stacks.

• Multichannel imaging.

• Multiple sample positions.

• Three-dimensional time series.

The acquisition interval should be sufficiently short to resolve the expected molecular movement without causing excessive photobleaching or phototoxicity.

5. Ratiometric image analysis

After background subtraction and correction, the signal from the photobleached channel is divided by, or otherwise normalized against, the reference-channel signal. This generates a ratio image in which the locally photolabelled molecular population can be identified and followed.

The exact direction of the ratio calculation must remain consistent throughout the analysis. Depending on whether the bleached channel is divided by the reference channel or vice versa, the photolabelled region may appear as a lower or higher ratio value.

Advantages over conventional FRAP

In conventional FRAP, the photobleached molecules become non-fluorescent and cannot be directly tracked after leaving the original bleaching region. FRAP therefore measures the recovery of unbleached fluorescence entering the bleached region rather than directly following the molecules that were originally located there.

FLAP overcomes this limitation by using the second, non-bleached fluorophore as a reference label. The molecules originally located within the photobleached region retain the reference fluorescence and can be identified through the ratio between the two channels. FLAP can therefore provide localized photolabelling and direct tracking of a selected molecular population.

The ratiometric approach is particularly advantageous when the labelled structure:

• Moves across the field of view.

• Changes its shape.

• Contracts or expands.

• Enters or leaves a defined cellular region.

• Undergoes substantial morphological rearrangement.

• Displays changes in fluorescence intensity caused by variations in thickness or concentration.

Because the reference signal reports the distribution of the labelled material, FLAP can distinguish movement of the photolabelled population from apparent intensity changes caused by displacement or deformation.

Information obtained from FLAP

FLAP can provide information about:

• Direction and extent of molecular movement.

• Redistribution of proteins within living cells.

• Transport between cellular compartments.

• Movement of proteins along cellular structures.

• Molecular retention within a specific region.

• Exchange between mobile and stationary populations.

• Turnover of structural components.

• Persistence and destination of a locally marked molecular population.

• Spatial and temporal heterogeneity of molecular transport.

• Movement of molecules within structures that change shape over time.

Unlike conventional FRAP, FLAP is primarily a localization and tracking technique. Although fluorescence-ratio changes may be quantified over time, FLAP does not automatically provide a diffusion coefficient or binding constant. Extraction of kinetic parameters requires an appropriate experimental design and mathematical model.

Typical applications

FLAP can be applied to:

• Protein trafficking in living cells.

• Nuclear–cytoplasmic transport.

• Redistribution of nuclear proteins.

• Movement of proteins along cytoskeletal structures.

• Turnover and transport within focal adhesions.

• Cell-membrane dynamics.

• Analysis of cell–cell junctions.

• Organelle movement and remodelling.

• Vesicular transport.

• Protein redistribution during cell migration.

• Analysis of highly dynamic or deformable cellular structures.

• Movement of molecules during mitosis and cytokinesis.

• Host–microorganism interactions.

• Transport processes in plant cells.

• Developmental and morphogenetic processes.

• Tracking of locally defined molecular populations in tissues.

Quantitative analysis

FLAP analysis usually requires measurements from both fluorescent channels at every time point. Before calculating the fluorescence ratio, several corrections may be necessary:

• Background subtraction in both channels.

• Correction for acquisition-induced photobleaching.

• Image registration to correct sample drift.

• Correction for spectral cross-talk.

• Exclusion of saturated pixels.

• Correction for differences in detector response.

• Verification of reference-fluorophore stability.

• Segmentation of the moving structure or molecular population.

Ratio images can be used to determine:

• Position of the photolabelled population.

• Distance and direction of displacement.

• Distribution of the population between cellular regions.

• Percentage of the marked population retained within the original region.

• Rate of redistribution.

• Persistence of the optical mark.

• Changes in the spatial extent of the labelled population.

Object tracking, particle tracking, line-profile analysis and two- or three-dimensional segmentation may be combined with the ratiometric data when the labelled structure moves through the specimen.

Experimental considerations

The success of FLAP depends strongly on the properties of the two fluorescent labels. Important factors include:

• Spectral separation between the fluorophores.

• Relative fluorescence brightness.

• Photostability of the reference fluorophore.

• Selective bleachability of the photobleached fluorophore.

• Similar localization and molecular behaviour of both labels.

• Stability of the association between the labels and the target molecule.

• Minimal fluorescence resonance energy transfer between the two probes, unless specifically incorporated into the experimental design.

• Limited cross-excitation and emission bleed-through.

• Compatibility with live-cell conditions.

Selective photobleaching should produce a substantial change in the bleached-channel signal without significantly altering the reference channel. Control experiments containing each fluorophore separately are useful for determining excitation cross-talk, emission bleed-through and unintended photobleaching.

The reference fluorophore must remain stable during the complete experiment. Significant bleaching of the reference channel can alter the ratio independently of molecular movement and lead to misleading results.

Use with the Leica STELLARIS platform

FLAP can be implemented on a Leica STELLARIS platform provided that the installed system supports two-channel fluorescence imaging, spatially controlled region-of-interest photobleaching and time-lapse acquisition.

The STELLARIS photomanipulation workflow can deliver high laser intensity to a defined region for bleaching and subsequently acquire both fluorescence channels at lower illumination intensity. Leica describes FLAP as a two-channel ratiometric derivative of photobleaching microscopy, while its confocal software supports ROI-based high-power photobleaching and time-resolved imaging.

Implementation does not necessarily require a dedicated FLAP acquisition wizard. It can be performed by combining the general photobleaching, multichannel and time-series functions of the microscope, followed by ratiometric image processing. This is an inference from the documented imaging and photomanipulation capabilities and should be confirmed for the installed LAS X version and system configuration.

The selected fluorophores must be compatible with the excitation, bleaching and detection wavelengths available on the installed STELLARIS system.

Main outputs

The principal outputs of a FLAP experiment include:

• Dual-channel pre-bleach images.

• Images acquired immediately after selective photobleaching.

• Dual-channel time-lapse sequences.

• Ratiometric fluorescence images.

• Spatial maps of the locally photolabelled molecular population.

• Molecular or structural tracking trajectories.

• Measurements of displacement and redistribution.

• Fluorescence-ratio curves.

• Measurements of retention within or movement between cellular regions.

• Two-dimensional, three-dimensional or four-dimensional tracking datasets.

FLAP therefore provides a powerful method for creating a spatially restricted optical mark and directly following a selected molecular population in living biological samples. Its dual-label ratiometric design makes it particularly valuable for studying proteins and cellular structures that move, deform or undergo substantial morphological changes during observation.

Number and Brightness analysis (N&B) is a fluorescence fluctuation technique used to investigate the concentration, molecular brightness, aggregation and oligomerization state of fluorescently labelled molecules in living cells and other biological samples. It analyses temporal variations in fluorescence intensity within each pixel of a sequence of confocal images.

Unlike conventional fluorescence-intensity measurements, which primarily indicate the total amount of detected fluorescence, N&B separates this signal into two apparent components: the average number of fluorescent particles and the average molecular brightness of those particles. Molecular brightness is related to the amount of fluorescence emitted by each independently moving fluorescent entity and can therefore provide information about whether labelled proteins are present predominantly as monomers, dimers, oligomers or larger molecular assemblies. The method was originally developed as a pixel-based moment analysis for laser-scanning fluorescence microscopy.

Principle of the technique

Fluorescent molecules continuously move into and out of the confocal observation volume as a consequence of diffusion, transport, binding and other molecular processes. These movements produce small temporal fluctuations in the fluorescence intensity detected at each pixel.

N&B analyses a series of images acquired repeatedly from the same field of view. For every pixel, the mean fluorescence intensity and the temporal variance are calculated. The relationship between these values is used to estimate:

• Apparent molecular brightness, which reflects the fluorescence associated with each independently fluctuating particle.

• Apparent particle number, which estimates the number of fluorescent entities contributing to the signal.

A region containing many dim fluorescent particles may have an average fluorescence intensity similar to a region containing fewer but brighter particles. Conventional intensity imaging cannot readily distinguish these situations, whereas N&B can separate their relative contributions.

The measurements are described as apparent number and brightness because they depend on the fluorophore, detector response, acquisition settings, background correction and mathematical assumptions used in the analysis. Absolute molecular numbers generally require careful calibration and additional methodological controls.

Detection of molecular oligomerization

One of the principal applications of N&B is the analysis of protein oligomerization in living cells. When fluorescently labelled proteins associate, the resulting molecular complex normally contains more fluorescent molecules and therefore exhibits greater molecular brightness than a monomeric reference.

For example, under ideal conditions:

• A fluorescent monomer has a characteristic reference brightness.

• A dimer may exhibit approximately twice the monomer brightness.

• Larger oligomers may show progressively higher brightness values.

The brightness measured for the protein of interest can therefore be compared with that of a monomeric fluorescent control acquired under the same instrumental and experimental conditions. This allows relative oligomerization states to be mapped across different cellular regions. N&B has been applied to investigate the density and oligomerization of receptors, adhesion proteins and other molecular complexes directly within cells.

In practice, brightness does not always increase in exact integer multiples. Incomplete fluorophore maturation, non-fluorescent proteins, blinking, photobleaching, differences in local environment and heterogeneous molecular populations can all affect the observed brightness. Results should therefore usually be interpreted as apparent or relative oligomerization states rather than exact molecular stoichiometries unless the experiment has been rigorously calibrated.

Experimental workflow

A typical N&B experiment involves the following stages:

1. Sample preparation

The molecule of interest is labelled with a fluorescent protein or another suitable fluorescent probe. Genetically encoded fluorescent proteins are commonly used because they allow molecular dynamics to be investigated in living cells.

Expression levels should be sufficiently low to preserve physiological behaviour but high enough to provide adequate photon statistics. Excessively high expression may lead to artificial aggregation, detector saturation or unreliable fluctuation measurements.

2. Acquisition of an image time series

A sequence of confocal images is repeatedly acquired from the same field of view. The sample should remain as stable as possible during acquisition so that changes between frames primarily reflect molecular fluorescence fluctuations rather than movement of the entire cell or specimen.

The required number of frames depends on signal strength, molecular dynamics, acquisition speed and the precision required. The same scanning parameters must be maintained throughout the sequence.

Important acquisition parameters include:

• Pixel size.

• Pixel dwell time.

• Frame rate.

• Number of frames.

• Laser power.

• Detector mode and gain.

• Pinhole diameter.

• Image dimensions.

• Scan direction.

• Fluorescence intensity and background level.

3. Image correction and preprocessing

Before N&B calculation, the image sequence may require:

• Background subtraction.

• Correction for photobleaching.

• Removal of frames affected by sudden movement.

• Image registration to correct lateral drift.

• Exclusion of saturated pixels.

• Segmentation of the cell or structure of interest.

• Correction for slow changes in cell morphology or fluorescence intensity.

Slow variations, such as sample drift or progressive photobleaching, can increase the calculated variance and be mistaken for molecular fluctuations. Appropriate preprocessing is therefore essential.

4. Calculation of number and brightness maps

For each pixel, the temporal mean and variance are calculated across the image sequence. These values are converted into apparent number and brightness parameters after correction for detector noise and background.

The result is normally displayed as spatial maps showing:

• Fluorescence intensity.

• Apparent particle number.

• Apparent molecular brightness.

• Regions assigned to different brightness or oligomerization populations.

Brightness maps can reveal molecular heterogeneity that is not evident in the original fluorescence-intensity image.

5. Calibration and interpretation

A monomeric fluorescent reference should be measured under the same optical and acquisition conditions as the protein of interest. This establishes the brightness associated with a single fluorescent unit.

Additional controls may include:

• A known dimeric or oligomeric construct.

• Cells expressing the fluorescent protein alone.

• Untransfected or unlabelled samples.

• Samples acquired at different expression levels.

• Positive and negative interaction controls.

• Measurements under conditions expected to alter oligomerization.

Calibration should ideally be repeated whenever the fluorophore, detector, objective, acquisition settings or optical configuration changes.

Typical applications

Number and Brightness analysis can be used for:

• Detection of protein oligomerization in living cells.

• Mapping monomeric, dimeric and oligomeric populations.

• Identification of protein aggregation.

• Analysis of receptor clustering at the plasma membrane.

• Study of protein–protein associations.

• Investigation of signalling-complex assembly.

• Analysis of molecular density within cellular compartments.

• Characterization of membrane-protein organization.

• Study of focal adhesions and cell–matrix interactions.

• Analysis of biomolecular condensates.

• Comparison of molecular organization before and after treatment.

• Monitoring changes in protein assembly during cellular stress.

• Investigation of host–microorganism interactions.

• Analysis of spatial heterogeneity in molecular complexes.

• Study of changes in protein organization during differentiation or development.

N&B can also be combined with spatial segmentation to compare molecular behaviour between the nucleus, cytoplasm, plasma membrane, organelles or other cellular regions.

Relationship with FCS and FCCS

N&B belongs to the broader group of fluorescence fluctuation spectroscopy techniques. It is related to Fluorescence Correlation Spectroscopy (FCS), but the two approaches provide different forms of information.

FCS usually records rapid fluorescence fluctuations from a small, fixed confocal observation volume and analyses their temporal autocorrelation. It can provide detailed information about molecular concentration, diffusion and interaction kinetics at a selected point.

N&B analyses fluctuations across a sequence of complete images. Its principal advantage is that it generates spatial maps of molecular number and brightness throughout the observed cell or structure. However, it normally provides less detailed temporal information about diffusion kinetics than FCS.

Thus:

• FCS provides detailed fluctuation kinetics at selected positions.

• N&B provides spatially resolved maps of molecular brightness and apparent particle number.

• FCCS examines the coordinated fluctuations of two differently labelled molecular species.

Use with the Leica STELLARIS platform

The Leica STELLARIS platform is suitable for acquiring image sequences for N&B analysis because it is a point-scanning confocal system equipped, depending on configuration, with Power HyD detectors capable of photon-counting acquisition. Leica identifies photon-counting sensitivity as one of the central detection capabilities of the STELLARIS platform.

Leica has previously demonstrated the use of HyD photon-counting detectors for measuring molecular number and brightness from confocal image sequences. The associated workflow combines photon-counting image acquisition with subsequent statistical analysis of the temporal fluorescence fluctuations.

For reliable N&B acquisition on the STELLARIS, the following should be available:

• Power HyD detector operating in a suitable photon-counting mode.

• Stable time-series acquisition.

• Appropriate control of scanning speed and pixel dwell time.

• Reproducible acquisition settings.

• Sufficient detector sensitivity at low excitation intensity.

• Software for calculating pixel-wise temporal mean and variance.

• Calibration using a monomeric fluorescent reference.

N&B does not necessarily appear in LAS X as a dedicated acquisition mode or automated analysis wizard. The STELLARIS can be used to acquire the required photon-counting image series, while the subsequent analysis may be performed using suitable external fluorescence-fluctuation software, ImageJ/Fiji tools or specialized analysis packages.

Consequently, the technique should be presented as an advanced quantitative analysis performed from appropriately acquired STELLARIS datasets, rather than assuming that all calculations are automatically integrated into the microscope software.

Advantages

The main advantages of N&B include:

• Analysis of molecular organization in living cells.

• Spatial mapping of apparent oligomerization states.

• Use of conventional raster-scanned confocal image sequences.

• Simultaneous estimation of apparent particle number and brightness.

• Identification of heterogeneous molecular populations.

• Analysis across large cellular regions rather than at a single point.

• Compatibility with time-lapse and multichannel fluorescence imaging.

• Lower experimental complexity than many single-molecule approaches.

• Ability to compare molecular assembly between cellular compartments or treatments.

Limitations and experimental considerations

N&B measurements are sensitive to both biological and instrumental fluctuations. Sample movement, membrane displacement, organelle transport, changes in cell shape and focal drift may increase temporal variance independently of molecular brightness.

Other important limitations include:

• Photobleaching during acquisition.

• Fluorophore blinking or transitions to dark states.

• Incomplete maturation of fluorescent proteins.

• Detector noise.

• Background fluorescence.

• Pixel saturation.

• Low photon counts.

• Heterogeneous fluorophore environments.

• Differences in fluorescence quantum yield.

• Artificial aggregation caused by overexpression.

• Movement of structures into or out of the focal plane.

• Mixed populations with different oligomerization states.

Detector calibration and correction are especially important. The mathematical treatment differs according to whether acquisition is performed using analog detection or photon counting. The detector mode and analysis model must therefore be matched correctly.

N&B generally works best when the fluorescent molecules or complexes generate detectable temporal fluctuations during the acquisition period. Very slow, completely immobilized or extremely rapidly moving populations may require modification of the scanning conditions or complementary techniques.

The method reports the brightness of independently fluctuating fluorescent entities. A stable complex containing several fluorescent proteins may appear brighter than a monomer, but molecules moving together for reasons other than direct binding may also contribute to increased apparent brightness. N&B therefore provides evidence of molecular assembly or co-mobility but does not, by itself, prove direct physical interaction.

Live-cell imaging considerations

N&B is particularly valuable for live-cell studies because molecular fluctuations and changes in oligomerization can be mapped under physiological or experimentally controlled conditions.

During prolonged measurements, temperature, CO₂ concentration, humidity and culture conditions should be maintained when required. Laser power should be minimized to reduce photobleaching, phototoxicity and cellular stress, while still collecting enough photons for statistically reliable analysis.

A suitable balance must be established between:

• Temporal resolution.

• Spatial resolution.

• Number of frames.

• Photon counts.

• Photobleaching.

• Cell viability.

Main outputs

The main outputs of an N&B experiment include:

• Confocal fluorescence time-series images.

• Mean fluorescence-intensity maps.

• Variance maps.

• Apparent molecular-brightness maps.

• Apparent particle-number maps.

• Histograms and distributions of molecular brightness.

• Pixel-selection maps associated with different brightness populations.

• Relative monomer, dimer or oligomer maps.

• Comparisons of molecular organization between cellular regions.

• Quantitative comparisons between treatments or experimental conditions.

Number and Brightness analysis therefore provides a powerful approach for mapping molecular concentration, aggregation and apparent oligomerization in living biological systems. When combined with sensitive photon-counting confocal imaging and appropriate calibration, it can reveal spatial differences in molecular organization that cannot be obtained from fluorescence intensity alone.

Quantitative colocalization analysis is an image-analysis approach used to determine the degree of spatial association between two or more fluorescent signals in cells, tissues and other biological samples. It is commonly applied to investigate whether labelled proteins, nucleic acids, organelles, microorganisms or cellular structures occupy similar regions within a fluorescence microscopy image.

Unlike simple visual inspection of merged fluorescence channels, quantitative colocalization analysis uses numerical parameters to describe the relationship between the intensity or spatial distribution of different fluorescent markers. This allows objective comparisons between samples, cellular regions, treatments and experimental conditions.

The Leica STELLARIS confocal platform is particularly suitable for generating multichannel datasets for colocalization studies because it combines tunable excitation, spectral detection and sensitive fluorescence detectors. These capabilities facilitate the selection of suitable excitation and detection ranges and help reduce spectral cross-talk between fluorescent channels. Leica specifically identifies colocalization, co-expression and multiparametric analysis among the principal cellular and molecular applications of multicolour confocal imaging.

Principle of the analysis

In a typical experiment, two biological targets are labelled with spectrally distinguishable fluorophores. Separate images are acquired for each fluorescent channel and spatially aligned so that every pixel or voxel represents the same position within the sample.

The analysis then evaluates whether signal from one fluorescent marker occurs at the same spatial positions as signal from the other marker. Depending on the analytical method, colocalization can be assessed from:

• Correlation between pixel intensities.

• Fraction of one fluorescent signal overlapping another.

• Spatial proximity between segmented objects.

• Distance between object centres, surfaces or boundaries.

• Three-dimensional overlap between labelled structures.

• Changes in spatial association over time.

Quantitative analysis is preferable to judging yellow or white regions in a merged image. The apparent colour of a merge can be strongly influenced by display brightness, contrast settings, detector gain, background fluorescence and the selected lookup tables.

Pixel-based colocalization analysis

Pixel-based methods compare the fluorescence intensities recorded in corresponding pixels or voxels from two channels. They are most appropriate when the fluorescent signals form continuous or diffuse distributions rather than clearly separated objects.

Several coefficients can be calculated, each answering a slightly different biological question. Pearson and Manders coefficients are among the most widely used metrics, but their interpretation depends on image quality, thresholding and the spatial distribution of the fluorescence.

Pearson’s correlation coefficient

Pearson’s correlation coefficient measures the linear relationship between the fluorescence intensities of two channels within a selected region.

Values generally range from:

• +1, indicating a strong positive correlation between the two intensity distributions.

• 0, indicating no linear correlation.

• −1, indicating an inverse relationship.

A high positive Pearson coefficient means that pixels with high intensity in one channel tend to have high intensity in the other channel. It therefore evaluates covariation of fluorescence intensities, rather than simply measuring the number of overlapping pixels.

Pearson’s coefficient is relatively independent of the absolute intensity scale, but it can be strongly influenced by background, noise, detector saturation, uneven illumination, sample heterogeneity and the selection of the region of interest.

A coefficient close to zero does not always mean complete spatial separation. For example, two signals may overlap spatially but display unrelated intensity variations. Similarly, a high coefficient may arise from broad spatial gradients or shared background rather than biologically meaningful colocalization.

Manders’ colocalization coefficients

Manders’ coefficients quantify the fraction of the fluorescence signal in one channel that occurs at positions containing signal from the other channel.

Two directional coefficients are normally calculated:

• M1: fraction of the signal in channel 1 that colocalizes with channel 2.

• M2: fraction of the signal in channel 2 that colocalizes with channel 1.

These values generally range from 0 to 1:

• 0 indicates that none of the selected signal overlaps.

• 1 indicates that the complete signal overlaps with the other channel.

M1 and M2 are not necessarily equal. For example, a small organelle-associated protein population may be entirely located within a broadly distributed membrane marker, producing a high value in one direction but a lower value in the opposite direction.

Both coefficients should therefore normally be reported and interpreted according to the biological question. Manders coefficients are especially useful when the objective is to determine what proportion of one marker is associated with another cellular compartment or structure.

Overlap coefficients

Overlap coefficients measure the degree of spatial coincidence between two fluorescence distributions. However, some overlap measurements are strongly affected by differences in fluorescence intensity and may produce high values even when intensity correlation is limited.

For this reason, overlap coefficients should not be used interchangeably with Pearson or Manders coefficients. The selected metric and its biological meaning should be specified in the experimental methodology.

Thresholding

Background fluorescence must be distinguished from true fluorescent labelling before calculating many colocalization parameters. Without appropriate thresholding, background pixels present in both channels may artificially increase the apparent overlap.

Thresholds can be established using:

• Unlabelled control samples.

• Single-labelled controls.

• Background regions within the image.

• Automated statistical procedures.

• Segmentation based on biological structures.

• Consistent experimentally defined intensity limits.

The same thresholding strategy should be applied to all images that are compared. Adjusting thresholds separately to obtain a visually favourable result introduces subjectivity and can invalidate quantitative comparisons.

Automated thresholding methods can improve reproducibility, but no single thresholding method is optimal for every image. The suitability of the selected method should be evaluated using the signal-to-background ratio, labelling pattern and biological structure under investigation. Quantitative colocalization literature emphasizes that coefficient values can change substantially depending on image processing and threshold selection.

Object-based colocalization analysis

Object-based analysis is useful when fluorescent signals appear as identifiable structures, such as vesicles, nuclei, bacteria, organelles, protein aggregates or fluorescent puncta.

Each fluorescence channel is first segmented to identify individual objects. Spatial association can then be evaluated using parameters such as:

• Number or percentage of overlapping objects.

• Distance between object centres.

• Minimum distance between object surfaces.

• Percentage of one object contained within another.

• Contact area between objects.

• Nearest-neighbour distance.

• Number of objects within a defined distance.

• Three-dimensional object overlap.

• Randomization-based spatial-association tests.

Object-based methods can distinguish between complete overlap, partial contact and close spatial proximity. They may therefore provide more biologically meaningful information than pixel-intensity correlation when labelled structures are discrete and well defined.

The result depends strongly on segmentation quality. Incorrect detection, splitting or merging of objects can substantially alter the calculated colocalization parameters.

Two-dimensional and three-dimensional analysis

Colocalization analysis can be performed on individual optical sections, projected images or complete three-dimensional confocal datasets.

Analysis of maximum-intensity projections should be used cautiously because structures located at different depths may appear to overlap after projection even though they are spatially separated in the original z-stack.

Whenever the biological question concerns spatial association throughout a tissue, cell or organelle, three-dimensional voxel- or object-based analysis is preferable. Three-dimensional analysis preserves axial information and allows the calculation of:

• Colocalized volumes.

• Percentage of overlapping voxels.

• Distances between three-dimensional objects.

• Spatial relationships throughout the complete z-stack.

• Colocalization within selected cellular compartments.

Adequate axial sampling and correction of chromatic displacement are particularly important for reliable three-dimensional analysis.

Image acquisition requirements

Reliable quantitative colocalization begins during image acquisition. Poorly acquired images cannot usually be corrected satisfactorily during later analysis.

Important acquisition requirements include:

• Appropriate fluorophore selection.

• Minimal spectral cross-excitation and emission bleed-through.

• Acquisition below detector-saturation levels.

• Adequate signal-to-background ratio.

• Consistent laser power and detector settings.

• Sufficient spatial sampling according to the optical resolution.

• Identical acquisition conditions for comparable samples.

• Appropriate selection of the pinhole and objective.

• Sequential acquisition when required.

• Correction of lateral and axial chromatic shifts.

• Avoidance of unnecessary image processing.

STELLARIS uses a White Light Laser, an Acousto-Optical Beam Splitter and spectral detection to adapt excitation and detection to the fluorophores in a multicolour sample. Its ImageCompass interface also provides information about expected cross-excitation and cross-talk when fluorophore combinations are configured.

Sequential scanning can reduce spectral cross-talk by acquiring the fluorescence channels separately. However, sequential acquisition may introduce temporal displacement when the sample or biological process moves rapidly. Simultaneous acquisition may be preferable for highly dynamic events, provided that the fluorophores can be adequately separated.

Experimental controls

Appropriate controls are essential for distinguishing genuine spatial association from optical or analytical artefacts.

Recommended controls include:

• Unlabelled samples, to determine autofluorescence and background.

• Single-labelled controls, to measure cross-excitation and emission bleed-through.

• Positive colocalization controls, containing markers expected to occupy the same structure.

• Negative controls, containing markers expected to be spatially independent.

• Secondary-antibody controls, when indirect immunofluorescence is used.

• Randomization or image-shift controls, to evaluate whether the measured association exceeds that expected by chance.

• Chromatic-registration controls, using multicolour fluorescent beads.

• Biological replicates, to assess experimental variability.

Single-labelled controls are particularly important because fluorescence detected in both channels may reflect spectral bleed-through rather than true biological colocalization.

Chromatic aberration and image registration

Different wavelengths may be focused or detected at slightly different spatial positions because of chromatic aberration. This displacement can lead to an apparent reduction or increase in colocalization, particularly when analysing small objects close to the resolution limit.

Multicolour fluorescent beads can be imaged using the same objective and optical configuration to measure channel displacement. Where necessary, transformation matrices can be calculated and applied to align the channels before colocalization analysis.

Sample movement between sequentially acquired channels can produce a similar misregistration effect. Image registration may therefore be required for living samples, long time-lapse experiments and datasets acquired sequentially.

Colocalization does not demonstrate direct molecular interaction

A central limitation is that conventional confocal colocalization cannot normally demonstrate that two proteins or molecules are in direct physical contact.

The lateral resolution of conventional confocal microscopy is typically much larger than the dimensions of individual proteins. Two fluorescent signals may therefore appear colocalized even when the labelled molecules are separated by tens or hundreds of nanometres.

Quantitative colocalization supports conclusions such as:

• The markers occupy similar cellular regions.

• One molecule is enriched within a labelled compartment.

• Two labelled structures are spatially associated.

• Their distributions change together under specific conditions.

It does not, by itself, prove:

• Direct protein–protein binding.

• Molecular complex formation.

• Energy transfer between fluorophores.

• A specific biochemical interaction.

Direct or nanoscale molecular interaction should be investigated using complementary approaches such as FRET, FLIM-FRET, FCCS, proximity-ligation assays, biochemical interaction methods or appropriate super-resolution techniques.

Typical applications

Quantitative colocalization analysis can be applied to:

• Protein localization within organelles.

• Association of proteins with membranes.

• Nuclear and cytoplasmic distribution.

• Protein trafficking and translocation.

• Vesicular transport.

• Receptor internalization.

• Association of microorganisms with host-cell structures.

• Analysis of endocytosis and intracellular infection.

• Cytoskeletal organization.

• Localization of nucleic acids and proteins.

• Analysis of protein aggregates and biomolecular condensates.

• Organelle interactions.

• Cell-signalling studies.

• Comparison of molecular distributions between treatments.

• Analysis of plant–microorganism interactions.

• Localization of fluorescent nanoparticles or biomaterials.

• Multichannel analysis of fixed and living samples.

Quantitative workflow

A typical quantitative colocalization workflow includes:

1. Acquisition of multichannel confocal images using optimized and standardized settings.

2. Evaluation of spectral cross-talk using single-labelled controls.

3. Background correction and, where necessary, image registration.

4. Definition of biologically relevant regions of interest.

5. Application of a reproducible thresholding or segmentation method.

6. Calculation of appropriate pixel- or object-based parameters.

7. Evaluation of random or chance colocalization.

8. Statistical comparison across biological replicates.

9. Reporting of acquisition, processing and analysis parameters.

Measurements should be performed on individual biological replicates rather than treating multiple fields from the same sample as completely independent experimental replicates.

Use with the Leica STELLARIS platform

The Leica STELLARIS platform can acquire the multichannel two-dimensional, three-dimensional and time-resolved datasets required for quantitative colocalization analysis. Its spectral detection, tunable excitation and sensitive Power HyD detectors facilitate multicolour imaging and help reduce spectral interference between channels. Leica describes STELLARIS as providing multichannel confocal imaging, spectral detection and photon-counting detection suitable for cellular and molecular multiparametric studies.

Depending on the installed software licenses, initial visualization, channel processing, region selection and some quantitative measurements may be performed in LAS X or associated Leica analysis software. More advanced colocalization analysis can also be carried out using dedicated platforms such as Fiji/ImageJ, Imaris, Aivia or other validated image-analysis software.

The acquisition and analysis protocols should be standardized before offering the method as a quantitative service.

Main outputs

The principal outputs include:

• Multichannel confocal images.

• Two-dimensional and three-dimensional merged images.

• Colocalization maps.

• Pixel-intensity scatter plots.

• Pearson correlation coefficients.

• Manders M1 and M2 coefficients.

• Thresholded colocalization coefficients.

• Percentage of overlapping pixels or voxels.

• Colocalized area or volume.

• Object-to-object distance measurements.

• Percentage of associated or overlapping objects.

• Spatial-randomization statistics.

• Comparisons between cellular regions, treatments or experimental groups.

• Time-dependent colocalization measurements in living samples.

Quantitative colocalization analysis therefore provides an objective method for evaluating the spatial association of fluorescently labelled biological components. Its reliability depends on appropriate fluorophore selection, carefully controlled confocal acquisition, correction of spectral and spatial artefacts and selection of analytical metrics that are appropriate for the biological question.

Fluorescent biosensor imaging is an advanced microscopy approach used to visualize and quantify biochemical activities, molecular signals and physiological changes within living cells, tissues and organisms. Fluorescent biosensors convert a specific biological event into a measurable change in fluorescence, allowing cellular processes to be monitored with high spatial and temporal resolution.

Depending on their design, fluorescent biosensors may respond to changes in ion concentration, pH, redox state, metabolite concentration, membrane potential, enzymatic activity, protein conformation or molecular interactions. The fluorescence response may involve a change in signal intensity, excitation or emission spectrum, fluorescence ratio, energy transfer or fluorescence lifetime.

The Leica STELLARIS confocal platform is suitable for fluorescent biosensor imaging because it combines flexible laser excitation, spectral detection, sensitive Power HyD detectors and time-resolved image acquisition. These capabilities facilitate the detection of weak and rapidly changing fluorescent signals while allowing excitation power to be reduced during live-cell imaging. Leica identifies biochemical sensors as one of the labelling approaches used in fluorescence live-cell microscopy and describes STELLARIS as a platform for sensitive, spectrally resolved and time-resolved imaging.

Principle of fluorescent biosensors

A fluorescent biosensor normally contains a molecular recognition component connected to one or more fluorophores. When the target biological event occurs, the sensor undergoes a change in conformation, chemical state, localization or molecular interaction. This modification alters one or more fluorescence properties that can be detected by the microscope.

The measured fluorescence response may indicate:

• Presence or concentration of a molecule or ion.

• Activation of a signalling pathway.

• Activity of an enzyme.

• Interaction between proteins.

• Changes in intracellular metabolism.

• Variation in the physicochemical environment.

• Movement or translocation of a signalling component.

• Cellular response to a treatment or environmental stimulus.

Fluorescent biosensors may be genetically encoded, chemically synthesized or introduced as fluorescent indicator dyes. Genetically encoded biosensors can be targeted to particular cells, organelles or subcellular compartments, whereas chemical probes may provide rapid labelling without requiring genetic transformation.

Intensity-based biosensors

Intensity-based biosensors produce an increase or decrease in fluorescence intensity in response to the biological parameter being measured. The response may result from changes in fluorophore brightness, conformation, protonation, oxidation state or accessibility to the surrounding environment.

These sensors are relatively simple to acquire because their response can be monitored using a single fluorescence channel. They are commonly used for time-lapse imaging of rapid cellular events.

However, fluorescence intensity can also be influenced by factors unrelated to biosensor activation, including:

• Sensor concentration.

• Differences in expression level.

• Cell or tissue thickness.

• Movement of the sample.

• Changes in focal position.

• Illumination instability.

• Photobleaching.

• Detector settings.

• Uneven distribution of the probe.

Appropriate controls and normalization procedures are therefore required to distinguish genuine biological responses from variations in sensor abundance or image acquisition.

Ratiometric biosensors

Ratiometric biosensors generate two fluorescence signals whose relative intensities change in response to the biological parameter under investigation. The response is quantified by calculating the ratio between two excitation wavelengths, two emission channels or two fluorescent components.

Ratiometric analysis provides partial correction for variations in:

• Biosensor concentration.

• Cell thickness.

• Uneven loading or expression.

• Illumination intensity.

• Sample movement.

• Differences in the amount of labelled material.

Ratiometric sensors are frequently used to measure intracellular ion concentrations, pH and metabolic changes. The flexible spectral excitation and detection available on the STELLARIS platform facilitate the configuration of excitation and emission bands for these measurements. Its White Light Laser and spectral detection system can be adapted to the spectral properties of different probes and multicolour combinations.

The calculated ratio is usually more robust than either fluorescence channel alone, although it does not automatically provide an absolute concentration. Conversion of fluorescence ratios into physical units requires calibration using known concentrations, ionophores, buffers or experimentally validated reference conditions.

FRET-based biosensors

Förster Resonance Energy Transfer biosensors contain a donor and an acceptor fluorophore positioned within the same molecular construct or associated molecular system. Activation of the sensor changes the distance or orientation between the fluorophores, producing a measurable change in FRET efficiency.

FRET biosensors are widely used to monitor:

• Protein–protein interactions.

• Protein conformational changes.

• Kinase and phosphatase activities.

• GTPase activation.

• Cyclic nucleotide signalling.

• Protease activity.

• Calcium signalling.

• Mechanical tension.

• Metabolic and redox changes.

FRET biosensor responses can be measured using donor intensity, acceptor intensity, sensitized emission, acceptor photobleaching or fluorescence lifetime. Ratiometric donor-to-acceptor measurements are commonly used for dynamic live-cell experiments.

Appropriate controls are required to correct for donor bleed-through, direct excitation of the acceptor, unequal sensor expression and photobleaching. Spectral detection and sequential acquisition on the STELLARIS platform can help reduce and characterize these sources of interference.

FLIM-based biosensors

Some fluorescent biosensors produce a change in fluorescence lifetime rather than, or in addition to, a change in fluorescence intensity. Fluorescence lifetime is the average period that a fluorophore remains in its excited state before emitting a photon.

Lifetime-based measurements are comparatively independent of fluorophore concentration, moderate variations in excitation intensity and detector gain. They can therefore provide robust measurements in samples with uneven biosensor expression or variable morphology.

FLIM-based biosensors may be used to investigate:

• Metabolic state.

• Cellular microenvironment.

• pH and ion concentration.

• Protein interactions through FLIM-FRET.

• Molecular conformation.

• Enzyme activation.

• Redox state.

• Changes in viscosity or polarity.

Leica describes TauSense as a set of lifetime-based imaging tools integrated into the STELLARIS platform. More advanced quantitative and rapid FLIM applications, including biosensing and FLIM-FRET, are associated particularly with appropriately configured STELLARIS 8 FALCON systems. Leica specifically describes these systems as enabling biosensors to detect changes in metabolic state and cellular microenvironment.

The availability of quantitative FLIM biosensor imaging therefore depends on the detectors, pulsed excitation, timing electronics, software modules and calibration available in the installed STELLARIS configuration.

Spectral-shift biosensors

Some biosensors respond to their target through a shift in excitation or emission spectrum. These changes can be monitored by acquiring separate spectral channels or complete lambda stacks.

Spectral biosensor imaging may be used to:

• Detect changes in protonation or chemical environment.

• Separate different sensor states.

• Distinguish biosensor fluorescence from autofluorescence.

• Analyse probes with overlapping resting and activated spectra.

• Combine biosensor measurements with additional fluorescent markers.

Spectral imaging and unmixing can be especially valuable in plant tissues and other samples with substantial endogenous fluorescence. The spectral response of the inactive and activated sensor should ideally be characterized using appropriate reference and calibration samples.

Localization-based biosensors

Some fluorescent biosensors report activity through the translocation or redistribution of a fluorescent protein rather than a direct change in fluorescence properties.

For example, activation of a signalling pathway may cause a fluorescent reporter to move:

• From the cytoplasm to the nucleus.

• From the nucleus to the cytoplasm.

• From the cytosol to the plasma membrane.

• Between organelles.

• Into or out of a biomolecular condensate.

The response can be quantified by measuring nuclear-to-cytoplasmic, membrane-to-cytoplasmic or organelle-to-cytoplasmic fluorescence ratios. Confocal optical sectioning is particularly useful for distinguishing fluorescence in closely positioned cellular compartments.

Typical biological parameters

Fluorescent biosensor imaging can be used to study:

• Intracellular calcium and other ions.

• Intracellular and organellar pH.

• Reactive oxygen species.

• Cellular redox state.

• Glutathione balance.

• Membrane potential.

• ATP, ADP and cellular energy status.

• Glucose, lactate and other metabolites.

• NADH/NADPH-related metabolic states.

• Cyclic AMP and cyclic GMP.

• Kinase and phosphatase activity.

• Caspase and protease activity.

• Small GTPase signalling.

• Protein conformation.

• Protein–protein interactions.

• Mechanical forces and membrane tension.

• Hypoxia and oxygen availability.

• Autophagy and organelle physiology.

• Signal transduction during stress or development.

The parameter that can be measured is determined primarily by the selected biosensor. The microscope records the optical response but does not itself confer molecular specificity.

Experimental workflow

A typical fluorescent biosensor experiment includes the following stages:

1. Biosensor selection

The biosensor should be selected according to:

• Biological parameter of interest.

• Expected concentration or activity range.

• Dynamic range.

• Response speed.

• Spectral properties.

• Subcellular localization.

• Reversibility.

• Compatibility with the biological model.

• Available excitation and detection wavelengths.

• Possible interference from autofluorescence.

The sensor response range should match the expected physiological changes. A sensor that is already saturated under resting conditions cannot reliably detect further increases.

2. Sample preparation

Genetically encoded biosensors may be introduced by transient transfection, stable expression, viral delivery, transformation or transgenic approaches. Chemical biosensors may be introduced by incubation, microinjection or other loading procedures.

Expression or loading levels should be optimized. Excessive biosensor concentration can alter cellular physiology, buffer the molecule being measured, increase background fluorescence or produce aggregation.

3. Baseline acquisition

A baseline sequence is recorded before applying the experimental stimulus. The baseline establishes the resting fluorescence level, ratio, localization or lifetime of the biosensor and makes it possible to evaluate signal stability.

Several baseline frames should be collected to identify:

• Spontaneous fluctuations.

• Sample drift.

• Acquisition-induced photobleaching.

• Changes in focus.

• Baseline cellular activity.

• Uneven sensor distribution.

4. Stimulation and time-lapse recording

The sample is exposed to the treatment, stimulus or environmental change under investigation while fluorescence is recorded over time.

The stimulus may involve:

• Addition of a chemical compound.

• Change in nutrient or ion concentration.

• Exposure to oxidative or abiotic stress.

• Activation of a receptor.

• Interaction with another cell or microorganism.

• Mechanical stimulation.

• Light-induced activation.

• Temperature or environmental changes.

The acquisition frequency should match the kinetics of the biosensor. Rapid calcium or voltage responses require much faster imaging than slower transcriptional or metabolic responses.

5. Image processing and quantification

Depending on the biosensor, analysis may include:

• Background subtraction.

• Photobleaching correction.

• Image registration.

• Calculation of fluorescence changes relative to baseline.

• Channel-ratio calculation.

• Donor and acceptor correction for FRET sensors.

• Fluorescence-lifetime analysis.

• Nuclear–cytoplasmic or membrane–cytoplasmic ratios.

• Segmentation of cells, organelles or regions of interest.

• Generation of spatial response maps.

• Extraction of response amplitude, onset and duration.

For intensity-based sensors, fluorescence changes are frequently expressed relative to the initial signal. Ratiometric sensors are analysed by calculating the relationship between the two relevant channels at each time point.

Calibration

Calibration may be required when the objective is to convert the fluorescence response into an absolute or approximate biological measurement.

Calibration strategies depend on the biosensor and may involve:

• Minimum and maximum response conditions.

• Buffers containing known ion concentrations.

• Ionophores or membrane-permeabilizing agents.

• Defined pH solutions.

• Enzyme inhibitors or activators.

• Non-responsive biosensor mutants.

• Purified sensor standards.

• Reference constructs with known FRET efficiency.

Without calibration, many biosensors provide relative rather than absolute measurements. Relative measurements can still be highly informative when acquisition conditions and analysis procedures are standardized across experimental groups.

Multiparametric biosensor imaging

A fluorescent biosensor can be combined with additional markers to relate biochemical activity to cellular structure, organelles or cell identity. For example, a biosensor response may be analysed together with markers for the nucleus, plasma membrane, mitochondria or cytoskeleton.

The spectral flexibility of the STELLARIS platform supports multicolour fluorescence imaging, although the fluorophore combination must be selected carefully to minimize:

• Spectral overlap.

• Cross-excitation.

• Emission bleed-through.

• Direct excitation of FRET acceptors.

• Interference with the biosensor response.

• Excessive light exposure.

Lifetime and spectral information may provide additional separation possibilities in complex multicolour experiments. STELLARIS combines spectrally resolved detection with lifetime-based contrast in configurations supporting TauSense or FLIM.

Live-cell imaging considerations

Most biosensor experiments are performed in living samples because their purpose is to follow dynamic biochemical and physiological events.

Environmental conditions may need to be controlled throughout acquisition, including:

• Temperature.

• CO₂ concentration.

• Humidity.

• Culture-medium composition.

• Osmolarity.

• Oxygen concentration.

Laser power should be minimized to reduce photobleaching, phototoxicity and light-induced activation of cellular stress responses. STELLARIS combines tunable excitation, sensitive Power HyD detection and resonant scanning to support low-light and rapid live-cell acquisition.

A compromise must be established between temporal resolution, spatial resolution, signal-to-noise ratio and sample viability. The highest possible image resolution is not always desirable when recording a rapid or prolonged biosensor response.

Experimental controls

Recommended controls include:

• Cells or samples without the biosensor.

• Biosensor-expressing samples without stimulation.

• Positive and negative response controls.

• Non-responsive or binding-deficient sensor variants.

• Controls for photobleaching and phototoxicity.

• Single-labelled controls for multichannel or FRET imaging.

• Calibration controls when absolute measurements are required.

• Controls for effects caused by sensor expression.

• Independent biological replicates.

The biological specificity of the response should be validated using appropriate agonists, antagonists, inhibitors, mutants or complementary analytical methods.

Advantages

The main advantages of fluorescent biosensor imaging include:

• Direct observation of biochemical events in living samples.

• High spatial and temporal resolution.

• Analysis at cellular and subcellular levels.

• Monitoring of reversible and dynamic responses.

• Quantification of cellular heterogeneity.

• Targeting of sensors to specific organelles.

• Combination with structural fluorescence markers.

• Repeated measurements in the same cell.

• Analysis of response amplitude, kinetics and localization.

• Possibility of two-dimensional, three-dimensional and four-dimensional imaging.

Limitations

Important limitations include:

• Dependence on the specificity and calibration of the biosensor.

• Potential alteration of cellular physiology by sensor expression.

• Limited dynamic range or sensor saturation.

• Changes in fluorescence unrelated to biological activity.

• Photobleaching and phototoxicity.

• Variable expression or loading.

• Spectral interference and autofluorescence.

• Differences in sensor maturation and stability.

• Delayed or irreversible sensor responses.

• Differences between fluorescence response and actual molecular concentration.

• Requirement for specialized analysis in FRET, FLIM or ratiometric experiments.

A fluorescence change should therefore be interpreted according to the validated response mechanism of the specific biosensor rather than as a direct measurement of the biological parameter without appropriate controls.

Use with the Leica STELLARIS platform

The Leica STELLARIS platform can support several forms of fluorescent biosensor imaging, including:

• Single-channel intensity measurements.

• Dual-channel and ratiometric imaging.

• Spectral-shift biosensors.

• Localization and translocation reporters.

• FRET-based biosensors.

• Time-lapse and three-dimensional biosensor imaging.

• Lifetime-based biosensors, when the necessary FLIM configuration is installed.

• Multiparametric imaging combining biosensors and structural markers.

Conventional intensity-based, spectral and ratiometric biosensor experiments can generally be implemented using the confocal, multichannel and time-lapse functions of STELLARIS. Quantitative FLIM biosensing and rapid FLIM-FRET require the appropriate lifetime-capable detectors, excitation, electronics and software, such as a suitable STELLARIS FALCON configuration.

The compatibility of each biosensor must be evaluated according to its excitation and emission spectra, required acquisition speed, expected signal strength and analytical method.

Main outputs

The main outputs of fluorescent biosensor imaging include:

• Fluorescence-intensity time series.

• Relative fluorescence-change curves.

• Excitation or emission ratio maps.

• FRET-ratio and FRET-efficiency maps.

• Fluorescence-lifetime maps.

• Nuclear-to-cytoplasmic or membrane-to-cytoplasmic ratios.

• Spatial maps of ion concentration or biochemical activity.

• Response amplitude, onset and recovery measurements.

• Two-dimensional and three-dimensional biosensor images.

• Four-dimensional datasets showing biochemical changes over time.

• Comparisons between cells, cellular compartments and experimental conditions.

Fluorescent biosensor imaging therefore provides a powerful approach for visualizing and quantifying molecular activities and physiological changes in living biological systems. Its successful application requires an appropriate biosensor, optimized live-cell acquisition, suitable calibration and analytical procedures adapted to the specific fluorescence response.

LIGHTNING is a confocal super-resolution method developed by Leica Microsystems to improve the spatial resolution, contrast and structural detail of fluorescence images acquired with a laser-scanning confocal microscope. It combines optimized confocal acquisition with adaptive image processing to recover fine spatial information that may not be clearly resolved in conventional confocal images. Leica describes LIGHTNING as an automated information-extraction process that adapts its processing to the imaging conditions and specimen.

Unlike STED microscopy, LIGHTNING does not normally require a dedicated depletion laser or specialized STED-compatible fluorophores. Images are acquired using the standard confocal optical pathway and subsequently processed using information about the microscope configuration, objective, wavelength, pinhole and sampling conditions.

LIGHTNING should therefore be considered a form of computational confocal super-resolution, rather than a direct equivalent of optical nanoscopy techniques such as STED. Leica presents both LIGHTNING and STED as super-resolution approaches available within the STELLARIS platform, but they use different physical and computational strategies.

Principle of the technique

Every fluorescence image is affected by diffraction, out-of-focus fluorescence, detector noise and the optical response of the microscope. LIGHTNING estimates the effective spatial response of the imaging system and applies adaptive processing to improve the separation of closely positioned fluorescent structures.

The processing is adapted locally according to signal intensity, noise and image characteristics. This can improve:

• Lateral and axial resolution.

• Definition of fine cellular structures.

• Signal-to-background contrast.

• Separation of adjacent fluorescent objects.

• Visualization of weak structural details.

• Quality of two-dimensional and three-dimensional datasets.

The achievable improvement depends on the objective, numerical aperture, fluorophore wavelength, signal-to-noise ratio, pinhole size, pixel sampling, optical-section spacing and biological specimen.

Multicolour super-resolution

LIGHTNING can be applied to multichannel fluorescence datasets, allowing several labelled structures to be visualized with improved spatial definition. Leica indicates that STELLARIS can combine LIGHTNING with rapid multicolour acquisition and that its spectral detection architecture can collect several fluorescence channels, depending on the installed detector configuration.

Multicolour LIGHTNING imaging can be used to analyse:

• Spatial relationships between proteins and organelles.

• Cytoskeletal organization.

• Membrane domains.

• Vesicles and intracellular compartments.

• Cell–cell junctions.

• Microorganism–host interactions.

• Three-dimensional tissue architecture.

Careful fluorophore selection and correction of chromatic displacement remain essential, particularly when comparing structures separated by distances close to the resolution limit.

Live-cell imaging

Because LIGHTNING uses conventional confocal excitation and does not require a depletion beam, it can be compatible with living samples. Nevertheless, acquisition conditions must still be optimized to reduce phototoxicity and photobleaching.

Laser power, frame rate, pixel dwell time, z-stack depth and acquisition frequency should be adjusted according to the biological process. Leica also documents the combination of LIGHTNING with resonant scanning and signal-enhancement tools for rapid and comparatively gentle multicolour live-cell imaging.

Typical applications

LIGHTNING confocal super-resolution can be applied to:

• Fine cellular and subcellular morphology.

• Cytoskeletal filaments and networks.

• Membrane organization.

• Vesicular and organelle structure.

• Nuclear architecture.

• Protein localization.

• Cell–cell and host–microorganism interactions.

• Multicolour colocalization studies.

• Three-dimensional reconstruction of tissues and cells.

• Live-cell imaging of dynamic structures.

• Analysis of fluorescent nanoparticles and biomaterials.

Experimental considerations

Reliable LIGHTNING processing requires appropriately sampled confocal data. Collecting images with insufficient spatial sampling cannot be completely corrected computationally.

Important variables include:

• Objective magnification and numerical aperture.

• Pixel size and z-step.

• Excitation and emission wavelength.

• Pinhole diameter.

• Signal-to-noise ratio.

• Detector saturation.

• Background fluorescence.

• Sample movement.

• Refractive-index mismatch.

• Photobleaching during acquisition.

Excessive processing should be avoided, particularly in quantitative studies. The original confocal data should be retained, and processing parameters should be standardized when comparing experimental groups.

Main outputs

The principal outputs include:

• Two-dimensional super-resolved confocal images.

• Multichannel LIGHTNING images.

• Processed z-stacks.

• Three-dimensional super-resolution reconstructions.

• Improved visualization of fine cellular structures.

• Comparative conventional-confocal and LIGHTNING datasets.

• Time-lapse sequences with enhanced structural detail.

The availability of LIGHTNING on the CTEM instrument depends on the corresponding LAS X software module or licence being installed and enabled.

Multicolour fluorescence imaging enables several molecular targets, cellular compartments or biological structures to be visualized within the same sample. When combined with confocal optical sectioning, the technique can generate three-dimensional datasets showing the spatial organization and relationships of the labelled components throughout cells, tissues and other biological specimens.

The Leica STELLARIS platform combines tunable laser excitation, an Acousto-Optical Beam Splitter and prism-based spectral detection with adjustable emission bandwidths. This flexibility allows excitation and detection settings to be adapted to the spectral properties of the selected fluorophores.

Multicolour fluorescence imaging

In a multicolour experiment, different biological targets are labelled with spectrally distinguishable fluorescent proteins, antibodies, dyes or chemical probes. Each fluorophore is detected in a separate image channel, and the channels are subsequently combined to show the relative localization of the labelled structures.

The STELLARIS White Light Laser permits excitation wavelengths to be selected according to the fluorophores in the sample. Depending on the STELLARIS model and configuration, the accessible excitation range can extend through much of the visible spectrum and into the near-infrared region.

Multicolour acquisition may be performed:

• Simultaneously, when fluorophores can be separated without significant cross-talk.

• Sequentially between frames.

• Sequentially between lines.

• Using different excitation and detection combinations.

• Through spectral imaging and subsequent unmixing.

• Using fluorescence-lifetime differences when suitable TauSense or FLIM functions are available.

Simultaneous acquisition is advantageous for rapid biological events because the channels are recorded at approximately the same time. Sequential acquisition can reduce cross-excitation and emission bleed-through but may introduce temporal displacement in moving samples.

Three-dimensional imaging

Confocal optical sectioning enables a series of images to be acquired at successive focal depths. This series, known as a z-stack, represents the fluorescence distribution throughout the volume of the specimen.

The optical sections can be processed and reconstructed to produce:

• Orthogonal views.

• Maximum-intensity projections.

• Surface renderings.

• Volume renderings.

• Three-dimensional object models.

• Virtual sections in different orientations.

• Measurements of area, volume and spatial distribution.

Three-dimensional analysis preserves axial information that is lost when a complete z-stack is represented only as a two-dimensional projection.

Typical applications

Multicolour and three-dimensional imaging can be applied to:

• Localization of several proteins within the same cell.

• Visualization of nuclei, membranes, organelles and cytoskeleton.

• Analysis of tissue and organ architecture.

• Cell–cell interactions.

• Host–microorganism interactions.

• Microbial colonization of biological tissues.

• Three-dimensional reconstruction of plant and animal samples.

• Organoids and multicellular models.

• Protein colocalization.

• Intracellular trafficking.

• Distribution of fluorescent nanoparticles.

• Analysis of cellular morphology and volume.

• Reconstruction of vascular, neuronal or root structures.

Spectral separation

As the number of fluorophores increases, spectral overlap becomes an important limitation. The excitation and detection ranges should be selected to minimize direct excitation of other fluorophores and fluorescence bleed-through between channels.

STELLARIS spectral detection and White Light Laser technology provide flexibility for matching the excitation and emission settings to the dyes used in the sample. Leica also demonstrates high-content multicolour acquisition using the STELLARIS platform, although the practical number of separable labels depends on fluorophore properties, detectors, signal intensity, autofluorescence and acquisition strategy.

Spectral imaging, spectral unmixing or lifetime-assisted separation may be used when conventional detection windows are insufficient.

Three-dimensional acquisition requirements

Reliable three-dimensional reconstruction requires appropriate spatial sampling in the x, y and z dimensions. Important variables include:

• Objective numerical aperture.

• Pixel size.

• Optical zoom.

• Pinhole diameter.

• Z-step interval.

• Total imaging depth.

• Refractive index of the mounting medium.

• Signal attenuation with depth.

• Sample transparency.

• Chromatic and spherical aberrations.

The z-step should be selected according to the axial resolution and the requirements of the subsequent analysis. Excessively large intervals may omit structural information, whereas excessively small intervals increase acquisition time, dataset size and light exposure.

Quantitative three-dimensional analysis

After segmentation, three-dimensional datasets can be used to measure:

• Object number.

• Surface area.

• Volume.

• Shape and sphericity.

• Fluorescence intensity.

• Distance between structures.

• Percentage of overlapping volumes.

• Spatial distribution within cells or tissues.

• Branching and network organization.

Image segmentation and quantitative analysis may be performed in LAS X or using suitable external software, depending on the installed licences and analytical requirements.

Experimental controls

Recommended controls include:

• Unlabelled samples for autofluorescence.

• Single-labelled samples for spectral cross-talk.

• Multicolour fluorescent beads for channel registration.

• Samples acquired using identical settings.

• Biological and technical replicates.

• Controls for nonspecific labelling.

Three-dimensional colocalization or distance measurements should account for chromatic displacement and the lower axial resolution of conventional confocal microscopy.

Main outputs

The principal outputs include:

• Multichannel fluorescence images.

• Spectrally separated image channels.

• Z-stacks.

• Orthogonal sections.

• Maximum-intensity projections.

• Three-dimensional surface and volume renderings.

• Quantitative measurements of labelled structures.

• Three-dimensional colocalization maps.

• Movies showing rotation or navigation through reconstructed volumes.

Multicolour and three-dimensional imaging therefore provide a versatile approach for examining the spatial organization of several biological components within complex cellular and tissue environments.

Time-lapse fluorescence imaging is used to monitor biological processes by acquiring a sequence of images from the same specimen at defined time intervals. When three-dimensional z-stacks are repeatedly acquired over time, the resulting dataset is known as four-dimensional imaging, combining the three spatial dimensions with time: x, y, z and t.

The Leica STELLARIS platform supports time-series and volumetric imaging of dynamic biological processes. Leica documents the acquisition of live three-dimensional volumes over time with STELLARIS and the creation of rendered 3D time-lapse movies using LAS X.

Time-lapse imaging

In a conventional time-lapse experiment, a single optical section or selected set of planes is acquired repeatedly. The interval between images may range from very short periods for rapid events to several minutes or hours for slower processes.

Time-lapse imaging can be used to monitor:

• Cell migration.

• Cell division and mitosis.

• Intracellular trafficking.

• Vesicle movement.

• Organelle dynamics.

• Cytoskeletal rearrangement.

• Protein translocation.

• Signal-transduction events.

• Cell death and stress responses.

• Host–microorganism interactions.

• Microbial colonization.

• Developmental processes.

• Responses to chemical or environmental treatments.

Four-dimensional imaging

In 4D imaging, a complete z-stack is acquired at every time point. This makes it possible to follow structures that move not only laterally but also through the depth of the specimen.

Four-dimensional imaging is particularly useful for:

• Cells moving within three-dimensional matrices.

• Organoid development.

• Embryonic and tissue morphogenesis.

• Intracellular structures moving between focal planes.

• Root growth and microorganism colonization.

• Organelle movement throughout complete cells.

• Three-dimensional tracking of particles or microorganisms.

• Changes in tissue volume and architecture over time.

The resulting datasets can be visualized as volume-rendered time-lapse movies. LAS X supports three-dimensional rendering of time-series data during or after acquisition.

Multichannel time-lapse imaging

Time-lapse and 4D imaging can be combined with several fluorescent markers to monitor multiple cellular components simultaneously. The STELLARIS spectral architecture and sensitive detectors facilitate multicolour live imaging, while Leica’s application materials specifically demonstrate dynamic multicolour and volumetric imaging.

Multichannel experiments can relate:

• A biosensor response to cellular morphology.

• Protein movement to organelle position.

• Microorganism behaviour to host-cell responses.

• Cytoskeletal rearrangement to membrane movement.

• Cell division to changes in nuclear and membrane structures.

The number of channels should be balanced against acquisition speed and total light exposure.

Acquisition speed

The temporal resolution must be matched to the biological process. Rapid events require short acquisition intervals, whereas slower processes can be recorded less frequently.

Acquisition speed is influenced by:

• Image dimensions.

• Pixel dwell time.

• Number of fluorescence channels.

• Sequential or simultaneous acquisition.

• Number of z-planes.

• Z-stack depth.

• Line or frame averaging.

• Scanner type.

• Number of sample positions.

Resonant scanning can increase acquisition speed and reduce the dwell time at each pixel, making it useful for rapid live-cell and volumetric imaging. Leica has demonstrated STELLARIS 5 acquisition of complete 3D volumes at multiple volumes per second in suitable experimental conditions.

Long-term live imaging

Long-term experiments require stable environmental and optical conditions. Depending on the biological specimen, control may be needed for:

• Temperature.

• CO₂ concentration.

• Humidity.

• Oxygen concentration.

• Culture-medium composition.

• Evaporation.

• Sample drift.

• Focus stability.

The microscope should be thermally equilibrated before prolonged acquisition. Stage, focus and environmental changes can produce movement that may be mistaken for biological displacement.

Automated focus-maintenance systems, image registration and appropriate sample immobilization can improve the stability of long-term datasets.

Photobleaching and phototoxicity

Repeated acquisition exposes the specimen to substantial cumulative illumination. Photobleaching reduces fluorescence signal over time, while phototoxicity can alter cellular physiology, movement, morphology and viability.

Light exposure can be reduced by:

• Lowering laser power.

• Using sensitive detectors.

• Reducing the number of acquired channels.

• Increasing the time between acquisitions.

• Reducing image dimensions.

• Limiting the number of z-planes.

• Avoiding unnecessary averaging.

• Using resonant scanning where appropriate.

• Selecting bright and photostable fluorophores.

• Restricting imaging to the biologically relevant volume.

The STELLARIS platform uses sensitive photon-counting detection and supports fast-scanning approaches intended for low-light live-cell imaging.

Multi-position imaging

Time-lapse experiments can be performed at several sample positions when an automated motorized stage and suitable acquisition software are available. This allows:

• Several cells or fields to be monitored.

• Different experimental conditions to be recorded.

• Larger sample regions to be sampled.

• Biological variability to be evaluated.

• Control and treated samples to be followed in parallel.

Increasing the number of positions reduces the maximum temporal resolution because the microscope must move between fields and acquire each dataset sequentially.

Image analysis

Time-lapse and 4D datasets can be analysed to measure:

• Fluorescence changes over time.

• Object trajectories.

• Velocity and direction of movement.

• Cell migration.

• Organelle displacement.

• Changes in object number, area or volume.

• Protein translocation.

• Colocalization over time.

• Appearance or disappearance of structures.

• Division, fusion and fragmentation events.

• Response amplitude and duration.

Three-dimensional segmentation and object tracking may be required when structures move through different focal planes.

Experimental considerations

The experimental design should define:

• Total observation period.

• Required temporal resolution.

• Imaging volume.

• Number of fluorescence channels.

• Number of sample positions.

• Spatial resolution.

• Environmental conditions.

• Acceptable light exposure.

• Data-storage requirements.

Four-dimensional datasets can be very large. Data-management, storage and processing requirements should therefore be considered before acquisition.

Main outputs

The principal outputs include:

• Two-dimensional time-lapse image sequences.

• Multichannel time-series datasets.

• Three-dimensional z-stacks acquired over time.

• Four-dimensional x–y–z–t datasets.

• Volume-rendered time-lapse movies.

• Molecular and cellular tracking trajectories.

• Fluorescence-intensity curves.

• Measurements of velocity, displacement and direction.

• Quantitative changes in area, volume and morphology.

• Dynamic colocalization and biosensor maps.

Time-lapse and four-dimensional imaging therefore enable biological processes to be visualized and quantified in their spatial and temporal context, providing information that cannot be obtained from fixed samples or isolated imaging time points.