From imaging to understanding: Frontiers in Live Cell Imaging, Bethesda, MD, April 19-21, 2006

MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.

Quantitative approaches to uncover physical mechanisms of tissue morphogenesis

Morphogenesis, the creation of tissue and organ architecture, is a series of complex and dynamic processes driven by genetic programs, microenvironmental cues, and intercellular interactions. Elucidating the physical mechanisms that generate tissue form is key to understanding development, disease, and the strategies needed for regenerative therapies. Advancements in imaging technologies, genetic recombination techniques, laser ablation, and microfabricated tissue models have enabled quantitative descriptions of the cellular motions and tissue deformations and stresses with unprecedented temporal and spatial resolution. Using these data synergistically with increasingly more sophisticated physical, mathematical, and computational models will unveil the physical mechanisms that drive morphogenesis.

Automated live cell imaging systems reveal dynamic cell behavior

Automated time-lapsed microscopy provides unique research opportunities to visualize cells and subcellular components in experiments with time-dependent parameters. As accessibility to these systems is increasing, we review here their use in cell science with a focus on stem cell research. Although the use of time-lapsed imaging to answer biological questions dates back nearly 150 years, only recently have the use of an environmentally controlled chamber and robotic stage controllers allowed for high-throughput continuous imaging over long periods at the cell and subcellular levels. Numerous automated imaging systems are now available from both companies that specialize in live cell imaging and from major microscope manufacturers. We discuss the key components of robots used for time-lapsed live microscopic imaging, and the unique data that can be obtained from image analysis. We show how automated features enhance experimentation by providing examples of uniquely quantified proliferation and migration live cell imaging data. In addition to providing an efficient system that drastically reduces man-hours and consumes fewer laboratory resources, this technology greatly enhances cell science by providing a unique dataset of temporal changes in cell activity.

Super-resolution imaging and statistical analysis of CdSe/CdS Core/Shell semiconductor nanocrystals

Here we present a multifunctional algorithm. Firstly a super-resolution method is presented for optically imaging the spatial distribution of semiconductor nanocrystals with nanometre localisation. Secondly highly resolved multiple photoluminescence trajectories of hundreds of single semiconductor nanocrystals are obtained simultaneously.

Deciphering subcellular processes in live imaging datasets via dynamic probabilistic networks

MOTIVATION

Designing mathematical tools that can formally describe the dynamics of complex intracellular processes remains a challenge. Live cell imaging reveals changes in the cellular states, but current simple approaches extract only minimal information of a static snapshot.

RESULTS

We implemented a novel approach for analyzing organelle behavior in live cell imaging data based on hidden Markov models (HMMs) and showed that it can determine the number and evolution of distinct cellular states involved in a biological process. We analyzed insulin-mediated exocytosis of single Glut4-vesicles, a process critical for blood glucose homeostasis and impaired in type II diabetes, by using total internal reflection fluorescence microscopy (TIRFM). HMM analyses of movie sequences of living cells reveal that insulin controls spatial and temporal dynamics of exocytosis via the exocyst, a putative tethering protein complex. Our studies have validated the proof-of-principle of HMM for cellular imaging and provided direct evidence for the existence of complex spatial-temporal regulation of exocytosis in non-polarized cells. We independently confirmed insulin-dependent spatial regulation by using static spatial statistics methods.

CONCLUSION

We propose that HMM-based approach can be exploited in a wide avenue of cellular processes, especially those where the changes of cellular states in space and time may be highly complex and non-obvious, such as in cell polarization, signaling and developmental processes.

The metabolic component of cellular refractivity and its importance for optical cytometry

Initially, it has been shown that the phase thickness and refractivity (the latter interpreted as the difference of the refractivity indices of an object and surrounding medium) depend on the functional state of mitochondria. The refractivity of various objects decreased in response to energy depletion. This dependence was then demonstrated for other biological objects such as cyanobacteria, chloroplasts and human cells. This general response brought about the hypothesis of a certain "universal" factor that links the variable (or metabolic) component of refractivity with the object's functional state. However, the origin of this phenomenon remains unknown. Our hypothesis is founded on the dependence of polarization of bound water molecules and the activity of metabolic processes. Here, we show the results of measurements of metabolic component of refractivity different bio-objects (mitochondria, chloroplasts, spores, cancer cells) obtained using the Coherent Phase Microscope "Airyscan". Estimations indicated high (up to n approximately = 1.41-1.45) values for the equivalent refractive index of structured water in cells.

Comparing the growth kinetics of cell populations in two and three dimensions

We study the kinetics of growing cell populations by means of a kinetic Monte Carlo method. By applying the same growth mechanism to a two-dimensional (2D) and a three-dimensional (3D) model, and making direct comparison with experimental studies, we show that both models exhibit similar behavior. Based on this we propose a method for establishment of a mapping between the 2D and 3D results. Additionally, we present an analytic approach to obtain the time evolution, and show in case of the 3D model how synchronization effects can influence the growth kinetics. Finally, we compare the results of our models to experimental data of the growth kinetics of 2D monolayers and 3D NIH3T3 xenografts in mice.

Multilayer three-dimensional super resolution imaging of thick biological samples

Recent advances in optical microscopy have enabled biological imaging beyond the diffraction limit at nanometer resolution. A general feature of most of the techniques based on photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM) has been the use of thin biological samples in combination with total internal reflection, thus limiting the imaging depth to a fraction of an optical wavelength. However, to study whole cells or organelles that are typically up to 15 microm deep into the cell, the extension of these methods to a three-dimensional (3D) super resolution technique is required. Here, we report an advance in optical microscopy that enables imaging of protein distributions in cells with a lateral localization precision better than 50 nm at multiple imaging planes deep in biological samples. The approach is based on combining the lateral super resolution provided by PALM with two-photon temporal focusing that provides optical sectioning. We have generated super-resolution images over an axial range of approximately 10 microm in both mitochondrially labeled fixed cells, and in the membranes of living S2 Drosophila cells.

Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa cells

The empirical characterization of nuclear shape distributions is an important unsolved problem with many applications in biology and medicine. Numerous genetic diseases and cancers have alterations in nuclear morphology, and methods for characterization of morphology could aid in both diagnoses and fundamental understanding of these disorders. Automated approaches have been used to measure features related to the size and shape of the cell nucleus, and statistical analysis of these features has often been performed assuming an underlying Euclidean (linear) vector space. We discuss the difficulties associated with the analysis of nuclear shape in light of the fact that shape spaces are nonlinear, and demonstrate methods for characterizing nuclear shapes and shape distributions based on spatial transformations that map one nucleus to another. By combining large deformation metric mapping with multidimensional scaling we offer a flexible approach for elucidating the intrinsic nonlinear degrees of freedom of a distribution of nuclear shapes. More specifically, we demonstrate approaches for nuclear shape interpolation and computation of mean nuclear shape. We also provide a method for estimating the number of free parameters that contribute to shape as well as an approach for visualizing most representative shape variations within a distribution of nuclei. The proposed methodology can be completely automated, is independent of the dimensionality of the images, and can handle complex shapes. Results obtained by analyzing two sets of images of HeLa cells are shown. In addition to identifying the modes of variation in normal HeLa nuclei, the effects of lamin A/C on nuclear morphology are quantitatively described.

Dual-color superresolution imaging of genetically expressed probes within individual adhesion complexes

Accurate determination of the relative positions of proteins within localized regions of the cell is essential for understanding their biological function. Although fluorescent fusion proteins are targeted with molecular precision, the position of these genetically expressed reporters is usually known only to the resolution of conventional optics ( approximately 200 nm). Here, we report the use of two-color photoactivated localization microscopy (PALM) to determine the ultrastructural relationship between different proteins fused to spectrally distinct photoactivatable fluorescent proteins (PA-FPs). The nonperturbative incorporation of these endogenous tags facilitates an imaging resolution in whole, fixed cells of approximately 20-30 nm at acquisition times of 5-30 min. We apply the technique to image different pairs of proteins assembled in adhesion complexes, the central attachment points between the cytoskeleton and the substrate in migrating cells. For several pairs, we find that proteins that seem colocalized when viewed by conventional optics are resolved as distinct interlocking nano-aggregates when imaged via PALM. The simplicity, minimal invasiveness, resolution, and speed of the technique all suggest its potential to directly visualize molecular interactions within cellular structures at the nanometer scale.