Automatic fluorescent tag detection in 3D with super-resolution: application to the analysis of chromosome movement

Kinetochore tracking in 3D from lattice light sheet imaging data with KiT

Motivation

Lattice light-sheet microscopy (LLSM) is revolutionizing cell biology since it enables fast, high-resolution extended imaging in three dimensions combined with a drastic reduction in photo-toxicity and bleaching. However, analysis of such datasets still remains a major challenge.

Results

Automated tracking of kinetochores, the protein complex facilitating and controlling microtubule attachment of the chromosomes within the mitotic spindle, provides quantitative assessment of chromosome dynamics in mitosis. Here, we extend existing open-source kinetochore tracking software (KiT) to track (and pair) kinetochores throughout prometaphase to anaphase in LLSM data. One of the key improvements is a regularization term in the objective function to enforce biological information about the number of kinetochores in a human mitotic cell, as well as improved diagnostic tools. This software provides quantitative insights into how kinetochores robustly ensure congression and segregation of chromosomes during mitosis.

Availability and implementation

KiT is free, open-source software implemented in MATLAB and can be downloaded as a package from https://github.com/cmcb-warwick/KiT. The source repository is available at https://bitbucket.org/jarmond/kit (tag v2.4.0) and under continuing development.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analysis of conditional colocalization relationships and hierarchies in three-color microscopy images

Colocalization analysis of multicolor microscopy images is a cornerstone approach in cell biology. It provides information on the localization of molecules within subcellular compartments and allows the interrogation of known molecular interactions in their cellular context. However, almost all colocalization analyses are designed for two-color images, limiting the type of information that they reveal. Here, we describe an approach, termed "conditional colocalization analysis," for analyzing the colocalization relationships between three molecular entities in three-color microscopy images. Going beyond the question of whether colocalization is present or not, it addresses the question of whether the colocalization between two entities is influenced, positively or negatively, by their colocalization with a third entity. We benchmark the approach and showcase its application to investigate receptor-downstream adaptor colocalization relationships in the context of functionally relevant plasma membrane locations. The software for conditional colocalization analysis is available at https://github.com/kjaqaman/conditionalColoc.

Diffusion and distal linkages govern interchromosomal dynamics during meiotic prophase

SignificanceEssential for sexual reproduction, meiosis is a specialized cell division required for the production of haploid gametes. Critical to this process are the pairing, recombination, and segregation of homologous chromosomes (homologs). While pairing and recombination are linked, it is not known how many linkages are sufficient to hold homologs in proximity. Here, we reveal that random diffusion and the placement of a small number of linkages are sufficient to establish the apparent "pairing" of homologs. We also show that colocalization between any two loci is more dynamic than anticipated. Our study provides observations of live interchromosomal dynamics during meiosis and illustrates the power of combining single-cell measurements with theoretical polymer modeling.

Automated tracking of S. pombe spindle elongation dynamics

The mitotic spindle is a microtubule-based machine that pulls the two identical sets of chromosomes to opposite ends of the cell during cell division. The fission yeast Schizosaccharomyces pombe is an important model organism for studying mitosis due to its simple, stereotyped spindle structure and well-established genetic toolset. S. pombe spindle length is a useful metric for mitotic progression, but manually tracking spindle ends in each frame to measure spindle length over time is laborious and can limit experimental throughput. We have developed an ImageJ plugin that can automatically track S. pombe spindle length over time and replace manual or semi-automated tracking of spindle elongation dynamics. Using an algorithm that detects the principal axis of the spindle and then finds its ends, we reliably track the length of the spindle as the cell divides. The plugin integrates with existing ImageJ features, exports its data for further analysis outside of ImageJ and does not require any programming by the user. Thus, the plugin provides an accessible tool for quantification of S. pombe spindle length that will allow automatic analysis of large microscopy data sets and facilitate screening for effects of cell biological perturbations on mitotic progression.

PunctaSpecks: A tool for automated detection, tracking, and analysis of multiple types of fluorescently labeled biomolecules

Recent advances in imaging technology and fluorescent probes have made it possible to gain information about the dynamics of subcellular processes at unprecedented spatiotemporal scales. Unfortunately, a lack of automated tools to efficiently process the resulting imaging data encoding fine details of the biological processes remains a major bottleneck in utilizing the full potential of these powerful experimental techniques. Here we present a computational tool, called PunctaSpecks, that can characterize fluorescence signals arising from a wide range of biological molecules under normal and pathological conditions. Among other things, the program can calculate the number, areas, life-times, and amplitudes of fluorescence signals arising from multiple sources, track diffusing fluorescence sources like moving mitochondria, and determine the overlap probability of two processes or organelles imaged using indicator dyes of different colors. We have tested PunctaSpecks on synthetic time-lapse movies containing mobile fluorescence objects of various sizes, mimicking the activity of biomolecules. The robustness of the software is tested by varying the level of noise along with random but known pattern of appearing, disappearing, and movement of these objects. Next, we use PunctaSpecks to characterize protein-protein interaction involved in store-operated Ca2+ entry through the formation and activation of plasma membrane-bound ORAI1 channel and endoplasmic reticulum membrane-bound stromal interaction molecule (STIM), the evolution of inositol 1,4,5-trisphosphate (IP3)-induced Ca2+ signals from sub-micrometer size local events into global waves in human cortical neurons, and the activity of Alzheimer's disease-associated β amyloid pores in the plasma membrane. The tool can also be used to study other dynamical processes imaged through fluorescence molecules. The open source algorithm allows for extending the program to analyze more than two types of biomolecules visualized using markers of different colors.

SMART-Q: An Integrative Pipeline Quantifying Cell Type-Specific RNA Transcription

Accurate RNA quantification at the single-cell level is critical for understanding the dynamics of gene expression and regulation across space and time. Single molecule FISH (smFISH), such as RNAscope, provides spatial and quantitative measurements of individual transcripts, therefore, can be used to explore differential gene expression among a heterogeneous cell population if combined with cell identify information. However, such analysis is not straightforward, and existing image analysis pipelines cannot integrate both RNA transcripts and cellular staining information to automatically output cell type-specific gene expression. We developed an efficient and customizable analysis method, Single-Molecule Automatic RNA Transcription Quantification (SMART-Q), to enable the analysis of gene transcripts in a cell type-specific manner. SMART-Q efficiently infers cell identity information from multiplexed immuno-staining and quantifies cell type-specific transcripts using a 3D Gaussian fitting algorithm. Furthermore, we have optimized SMART-Q for user experiences, such as flexible parameters specification, batch data outputs, and visualization of analysis results. SMART-Q meets the demands for efficient quantification of single-molecule RNA and can be widely used for cell type-specific RNA transcript analysis.

Spatiotemporal dynamics of multi-vesicular release is determined by heterogeneity of release sites within central synapses

A synaptic active zone (AZ) can release multiple vesicles in response to an action potential. This multi-vesicular release (MVR) occurs at most synapses, but its spatiotemporal properties are unknown. Nanoscale-resolution detection of individual release events in hippocampal synapses revealed unprecedented heterogeneity among vesicle release sites within a single AZ, with a gradient of release probability decreasing from AZ center to periphery. Parallel to this organization, MVR events preferentially overlap with uni-vesicular release (UVR) events at sites closer to an AZ center. Pairs of fusion events comprising MVR are also not perfectly synchronized, and the earlier event tends to occur closer to AZ center. The spatial features of release sites and MVR events are similarly tightened by buffering intracellular calcium. These observations revealed a marked heterogeneity of release site properties within individual AZs, which determines the spatiotemporal features of MVR events and is controlled, in part, by non-uniform calcium elevation across the AZ.

Interferometric fluorescence cross correlation spectroscopy

Measuring transport properties like diffusion and directional flow is essential for understanding dynamics within heterogeneous systems including living cells and novel materials. Fluorescent molecules traveling within these inhomogeneous environments under the forces of Brownian motion and flow exhibit fluctuations in their concentration, which are directly linked to the transport properties. We present a method utilizing single photon interference and fluorescence correlation spectroscopy (FCS) to simultaneously measure transport of fluorescent molecules within aqueous samples. Our method, within seconds, measures transport in thousands of homogenous voxels (100 nm)³ and under certain conditions, eliminates photo-physical artifacts associated with blinking of fluorescent molecules. A comprehensive theoretical framework is presented and validated by measuring transport of quantum dots, associated with VSV-G receptor along cellular membranes as well as within viscous gels.

H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry

Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called "cell astronomy" systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.

Remote control of microtubule plus-end dynamics and function from the minus-end

In eukaryotes, the organization and function of the microtubule cytoskeleton depend on the allocation of different roles to individual microtubules. For example, many asymmetrically dividing cells differentially specify microtubule behavior at old and new centrosomes. Here we show that yeast spindle pole bodies (SPBs, yeast centrosomes) differentially control the plus-end dynamics and cargoes of their astral microtubules, remotely from the minus-end. The old SPB recruits the kinesin motor protein Kip2, which then translocates to the plus-end of the emanating microtubules, promotes their extension and delivers dynein into the bud. Kip2 recruitment at the SPB depends on Bub2 and Bfa1, and phosphorylation of cytoplasmic Kip2 prevents random lattice binding. Releasing Kip2 of its control by SPBs equalizes its distribution, the length of microtubules and dynein distribution between the mother cell and its bud. These observations reveal that microtubule organizing centers use minus to plus-end directed remote control to individualize microtubule function.

Role of Dilution Rate and Nutrient Availability in the Formation of Microbial Biofilms

We revisited the mathematical model of the chemostat and examined consequences of considerably decreasing the concentration of limiting nutrient in the inflow for the growth of both the planktonic and biofilm cells in the chemostat tank (fermenter). The model predicts a substantially lower steady-state biomass of planktonic cells in response to decreasing inflowing nutrient concentration. Contrarily, the steady-state concentration of nutrient inside the fermenter is expected to remain the same, as long as the inflowing concentration does not fall below its value. This allows the biofilm cells to grow at a rate regulated only by the exchange rate of the medium (dilution rate). We maintained a strain of Enterococcus faecalis in a chemostat of our own design with limiting nutrient in the inflow set near saturation constant at three dilution rates (0.09, 0.28, and 0.81 h^-1). The highest dilution rate was near the critical rate calculated by the model. The one-day total biofilm buildup was 21× larger and its estimated growth rate 2.4× higher at highest dilution rate than at the lowest one. This increased biofilm formation with increased dilution rates is in agreement with previously published data on pure and mixed continuous flow cultures.

Mitotic live-cell imaging at different timescales

Mitosis is a highly dynamic and choreographed process in which chromosomes are captured by the mitotic spindle and physically segregated into the two daughter cells to ensure faithful transmission of the genetic material. Live-cell fluorescence microscopy enables these dynamics to be analyzed over diverse temporal scales. Here we present the methodologies to study chromosome segregation at three timescales: we first show how automated tracking of kinetochores enables investigation of mitotic spindle and chromosome dynamics in the seconds-to-minutes timescale; next we highlight how new DNA live dyes allow the study of chromosome segregation over a period of several hours in any cell line; finally, we demonstrate how image sequences acquired over several days can reveal the fate of whole cell populations over several consecutive cell divisions.

Co-storage and release of insulin-like peptide-5, glucagon-like peptide-1 and peptideYY from murine and human colonic enteroendocrine cells

OBJECTIVE

Insulin-like peptide-5 (INSL5) is an orexigenic gut hormone found in a subset of colonic and rectal enteroendocrine L-cells together with the anorexigenic hormones glucagon-like peptide-1 (GLP-1) and peptideYY (PYY). Unlike GLP-1 and PYY, INSL5 levels are elevated by calorie restriction, raising questions about how these hormones respond to different stimuli when they arise from the same cell type. The aim of the current study was to identify whether and how INSL5, GLP-1 and PYY are co-secreted or differentially secreted from colonic L-cells.

METHODS

An inducible reporter mouse (Insl5-rtTA) was created to enable selective characterisation of Insl5-expressing cells. Expression profiling and Ca²⁺-dynamics were assessed using TET-reporter mice. Secretion of INSL5, PYY, and GLP-1 from murine and human colonic crypt cultures was quantified by tandem mass spectrometry. Vesicular co-localisation of the three hormones was analysed in 3D-SIM images of immunofluorescently-labelled murine colonic primary cultures and tissue sections.

RESULTS

INSL5-producing cells expressed a range of G-protein coupled receptors previously identified in GLP-1 expressing L-cells, including Ffar1, Gpbar1, and Agtr1a. Pharmacological or physiological agonists for these receptors triggered Ca²⁺ transients in INSL5-producing cells and stimulated INSL5 secretion. INSL5 secretory responses strongly correlated with those of PYY and GLP-1 across a range of stimuli. The majority (>80%) of secretory vesicles co-labelled for INSL5, PYY and GLP-1.

CONCLUSIONS

INSL5 is largely co-stored with PYY and GLP-1 and all three hormones are co-secreted when INSL5-positive cells are stimulated. Opposing hormonal profiles observed in vivo likely reflect differential stimulation of L-cells in the proximal and distal gut.

Kv2.1 Clustering Contributes to Insulin Exocytosis and Rescues Human β-Cell Dysfunction

Insulin exocytosis is regulated by ion channels that control excitability and Ca²⁺ influx. Channels also play an increasingly appreciated role in microdomain structure. In this study, we examine the mechanism by which the voltage-dependent K⁺ (Kv) channel Kv2.1 (KCNB1) facilitates depolarization-induced exocytosis in INS 832/13 cells and β-cells from human donors with and without type 2 diabetes (T2D). We find that Kv2.1, but not Kv2.2 (KCNB2), forms clusters of 6-12 tetrameric channels at the plasma membrane and facilitates insulin exocytosis. Knockdown of Kv2.1 expression reduces secretory granule targeting to the plasma membrane. Expression of the full-length channel (Kv2.1-wild-type) supports the glucose-dependent recruitment of secretory granules. However, a truncated channel (Kv2.1-ΔC318) that retains electrical function and syntaxin 1A binding, but lacks the ability to form clusters, does not enhance granule recruitment or exocytosis. Expression of KCNB1 appears reduced in T2D islets, and further knockdown of KCNB1 does not inhibit Kv current in T2D β-cells. Upregulation of Kv2.1-wild-type, but not Kv2.1-ΔC318, rescues the exocytotic phenotype in T2D β-cells and increases insulin secretion from T2D islets. Thus, the ability of Kv2.1 to directly facilitate insulin exocytosis depends on channel clustering. Loss of this structural role for the channel might contribute to impaired insulin secretion in diabetes.

High resolution microscopy reveals the nuclear shape of budding yeast during cell cycle and in various biological states

How spatial organization of the genome depends on nuclear shape is unknown, mostly because accurate nuclear size and shape measurement is technically challenging. In large cell populations of the yeast Saccharomyces cerevisiae, we assessed the geometry (size and shape) of nuclei in three dimensions with a resolution of 30 nm. We improved an automated fluorescence localization method by implementing a post-acquisition correction of the spherical microscopic aberration along the z-axis, to detect the three dimensional (3D) positions of nuclear pore complexes (NPCs) in the nuclear envelope. Here, we used a method called NucQuant to accurately estimate the geometry of nuclei in 3D throughout the cell cycle. To increase the robustness of the statistics, we aggregated thousands of detected NPCs from a cell population in a single representation using the nucleolus or the spindle pole body (SPB) as references to align nuclei along the same axis. We could detect asymmetric changes of the nucleus associated with modification of nucleolar size. Stereotypical modification of the nucleus toward the nucleolus further confirmed the asymmetric properties of the nuclear envelope.

Summary: This novel method to explore 3D geometry of the nuclear envelope with enhanced resolution and post-acquisition correction of z-axis aberration revealed increased NPC density near the SPB and the nucleolus.

Ligand-induced growth and compaction of CD36 nanoclusters enriched in Fyn induces Fyn signaling

Nanoclustering is an emerging organizational principle for membrane-associated proteins. The functional consequences of nanoclustering for receptor signaling remain largely unknown. Here, we applied quantitative multi-channel high- and super-resolution imaging to analyze the endothelial cell surface receptor CD36, the clustering of which upon binding to multivalent ligands, such as the anti-angiogenic factor thrombospondin-1 (TSP-1), is thought to be crucial for signaling. We found that a substantial fraction of unligated CD36 exists in nanoclusters, which not only promote TSP-1 binding but are also enriched with the downstream effector Fyn. Exposure to multivalent ligands (TSP-1 or anti-CD36 IgM) that result in larger and denser CD36 clusters activates Fyn. Conversely, pharmacological perturbations that prevent the enhancement of CD36 clustering by TSP-1 abrogate Fyn activation. In both cases, there is no detectable change in Fyn enrichment at CD36 nanoclusters. These observations reveal a crucial role for the basal organization of a receptor into nanoclusters that are enriched with the signal-transducing downstream effectors of that receptor, such that enhancement of clustering by multivalent ligands is necessary and sufficient to activate the downstream effector without the need for its de novo recruitment.

A single-molecule view of transcription reveals convoys of RNA polymerases and multi-scale bursting

Live-cell imaging has revealed unexpected features of gene expression. Here using improved single-molecule RNA microscopy, we show that synthesis of HIV-1 RNA is achieved by groups of closely spaced polymerases, termed convoys, as opposed to single isolated enzymes. Convoys arise by a Mediator-dependent reinitiation mechanism, which generates a transient but rapid succession of polymerases initiating and escaping the promoter. During elongation, polymerases are spaced by few hundred nucleotides, and physical modelling suggests that DNA torsional stress may maintain polymerase spacing. We additionally observe that the HIV-1 promoter displays stochastic fluctuations on two time scales, which we refer to as multi-scale bursting. Each time scale is regulated independently: Mediator controls minute-scale fluctuation (convoys), while TBP-TATA-box interaction controls sub-hour fluctuations (long permissive/non-permissive periods). A cellular promoter also produces polymerase convoys and displays multi-scale bursting. We propose that slow, TBP-dependent fluctuations are important for phenotypic variability of single cells.

HIV-1 viral gene expression stochastically switches between active and inactive states. Here, using improved single molecule RNA microscopy, the authors show that HIV-1 RNA stochastic transcription is achieved by groups of closely spaced polymerases, and is regulated by Mediator and TBP at different time scales.

Kinesin-8 effects on mitotic microtubule dynamics contribute to spindle function in fission yeast

Kinesin-8 motor proteins destabilize microtubules and increase chromosome loss in mitosis. In fission yeast, aberrant microtubule-driven kinetochore pushing movements, tripolar mitotic spindles, and fluctuations in metaphase spindle length occurred in kinesin-8–deletion mutants. A mathematical model can explain these results.

Kinesin-8 motor proteins destabilize microtubules. Their absence during cell division is associated with disorganized mitotic chromosome movements and chromosome loss. Despite recent work studying effects of kinesin-8s on microtubule dynamics, it remains unclear whether the kinesin-8 mitotic phenotypes are consequences of their effect on microtubule dynamics, their well-established motor activity, or additional, unknown functions. To better understand the role of kinesin-8 proteins in mitosis, we studied the effects of deletion of the fission yeast kinesin-8 proteins Klp5 and Klp6 on chromosome movements and spindle length dynamics. Aberrant microtubule-driven kinetochore pushing movements and tripolar mitotic spindles occurred in cells lacking Klp5 but not Klp6. Kinesin-8–deletion strains showed large fluctuations in metaphase spindle length, suggesting a disruption of spindle length stabilization. Comparison of our results from light microscopy with a mathematical model suggests that kinesin-8–induced effects on microtubule dynamics, kinetochore attachment stability, and sliding force in the spindle can explain the aberrant chromosome movements and spindle length fluctuations seen.

Detection and tracking of overlapping cell nuclei for large scale mitosis analyses

Background

Cell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods.

Results

Here, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with two and three components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We use it to identify with high precision the very first frame containing three cells. This allows in our case to measure a cell division angle on each video and to construct division angle distributions for each tested condition. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters.

Conclusions

The proposed approach enables the high throughput analysis of video sequences of isolated cell clusters obtained using micropatterns. It relies only on two parameters that can be set robustly as they reduce to the average cell size and intensity.

KiT: a MATLAB package for kinetochore tracking

Summary: During mitosis, chromosomes are attached to the mitotic spindle via large protein complexes called kinetochores. The motion of kinetochores throughout mitosis is intricate and automated quantitative tracking of their motion has already revealed many surprising facets of their behaviour. Here, we present ‘KiT’ (Kinetochore Tracking)—an easy-to-use, open-source software package for tracking kinetochores from live-cell fluorescent movies. KiT supports 2D, 3D and multi-colour movies, quantification of fluorescence, integrated deconvolution, parallel execution and multiple algorithms for particle localization.

Availability and implementation: KiT is free, open-source software implemented in MATLAB and runs on all MATLAB supported platforms. KiT can be downloaded as a package from http://www.mechanochemistry.org/mcainsh/software.php. The source repository is available at https://bitbucket.org/jarmond/kit and under continuing development.

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact:jonathan.armond@warwick.ac.uk

Super-resolution kinetochore tracking reveals the mechanisms of human sister kinetochore directional switching

The congression of chromosomes to the spindle equator involves the directed motility of bi-orientated sister kinetochores. Sister kinetochores bind bundles of dynamic microtubules and are physically connected through centromeric chromatin. A crucial question is to understand how sister kinetochores are coordinated to generate motility and directional switches. Here, we combine super-resolution tracking of kinetochores with automated switching-point detection to analyse sister switching dynamics over thousands of events. We discover that switching is initiated by both the leading (microtubules depolymerising) or trailing (microtubules polymerising) kinetochore. Surprisingly, trail-driven switching generates an overstretch of the chromatin that relaxes over the following half-period. This rules out the involvement of a tension sensor, the central premise of the long-standing tension-model. Instead, our data support a model in which clocks set the intrinsic-switching time of the two kinetochore-attached microtubule fibres, with the centromeric spring tension operating as a feedback to slow or accelerate the clocks.

Lifetime estimation of moving subcellular objects in frequency-domain fluorescence lifetime imaging microscopy

Fluorescence lifetime is usually defined as the average nanosecond-scale delay between excitation and emission of fluorescence. It has been established that lifetime measurements yield numerous indications on cellular processes such as interprotein and intraprotein mechanisms through fluorescent tagging and Förster resonance energy transfer. In this area, frequency-domain fluorescence lifetime imaging microscopy is particularly appropriate to probe a sample noninvasively and quantify these interactions in living cells. The aim is then to measure the fluorescence lifetime in the sample at each location in space from fluorescence variations observed in a temporal sequence of images obtained by phase modulation of the detection signal. This leads to a sensitivity of lifetime determination to other sources of fluorescence variations such as intracellular motion. In this paper, we propose a robust statistical method for lifetime estimation for both background and small moving structures with a focus on intracellular vesicle trafficking.

Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy

Biological studies of intracellular dynamic processes commonly require motion analysis of large numbers of particles in live-cell time-lapse fluorescence microscopy imaging data. Many particle tracking methods have been developed in the past years as a first step toward fully automating this task and enabling high-throughput data processing. Two crucial aspects of any particle tracking method are the detection of relevant particles in the image frames and their linking or association from frame to frame to reconstruct the trajectories. The performance of detection techniques as well as specific combinations of detection and linking techniques for particle tracking have been extensively evaluated in recent studies. Comprehensive evaluations of linking techniques per se, on the other hand, are lacking in the literature. Here we present the results of a quantitative comparison of data association techniques for solving the linking problem in biological particle tracking applications. Nine multiframe and two more traditional two-frame techniques are evaluated as a function of the level of missing and spurious detections in various scenarios. The results indicate that linking techniques are generally more negatively affected by missing detections than by spurious detections. If misdetections can be avoided, there appears to be no need to use sophisticated multiframe linking techniques. However, in the practically likely case of imperfect detections, the latter are a safer choice. Our study provides users and developers with novel information to select the right linking technique for their applications, given a detection technique of known quality.

Performance and sensitivity evaluation of 3D spot detection methods in confocal microscopy

Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive.

CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

Fluorescence and bioluminescence time-lapse imaging allows to investigate a vast range of cellular processes at single-cell or even subcellular resolution. In particular, time-lapse imaging can provide uniquely detailed information on the fine kinetics of transcription, as well as on biological oscillations such as the circadian and cell cycles. However, we face a paucity of automated methods to quantify time-lapse imaging data with single-cell precision, notably throughout multiple cell cycles. We developed CAST (Cell Automated Segmentation and Tracking platform) to automatically and robustly detect the position and size of cells or nuclei, quantify the corresponding light signals, while taking into account both cell divisions (lineage tracking) and migration events. We present here how CAST analyzes bioluminescence data from a short-lived transcriptional luciferase reporter. However, our flexible and modular implementation makes it easily adaptable to a wide variety of time-lapse recordings. We exemplify how CAST efficiently quantifies single-cell gene expression over multiple cell cycles using mouse NIH3T3 culture cells with a luminescence expression driven by the Bmal1 promoter, a central gene of the circadian oscillator. We further illustrate how such data can be used to quantify transcriptional bursting in conditions of lengthened circadian period, revealing thereby remarkably similar bursting signature compared to the endogenous circadian condition despite marked period lengthening. In summary, we establish CAST as novel tool for the efficient segmentation, signal quantification, and tracking of time-lapse images from mammalian cell culture.

BRCA2 diffuses as oligomeric clusters with RAD51 and changes mobility after DNA damage in live cells

Nuclear BRCA2 is oligomeric and associated with RAD51, possibly sequestering it until it is delivered to DNA damage sites.

Genome maintenance by homologous recombination depends on coordinating many proteins in time and space to assemble at DNA break sites. To understand this process, we followed the mobility of BRCA2, a critical recombination mediator, in live cells at the single-molecule level using both single-particle tracking and fluorescence correlation spectroscopy. BRCA2-GFP and -YFP were compared to distinguish diffusion from fluorophore behavior. Diffusive behavior of fluorescent RAD51 and RAD54 was determined for comparison. All fluorescent proteins were expressed from endogenous loci. We found that nuclear BRCA2 existed in oligomeric clusters, and exhibited heterogeneous mobility. DNA damage increased BRCA2 transient binding, presumably including binding to damaged sites. Despite its very different size, RAD51 displayed mobility similar to BRCA2, which indicates physical interaction between these proteins both before and after induction of DNA damage. We propose that BRCA2-mediated sequestration of nuclear RAD51 serves to prevent inappropriate DNA interactions and that all RAD51 is delivered to DNA damage sites in association with BRCA2.

DENND2B activates Rab13 at the leading edge of migrating cells and promotes metastatic behavior

DENND2B, in a complex with the Rab13 effector MICAL-L2, activates Rab13 at the cell periphery, promoting the dynamic remodeling of the cell’s leading edge during tumor cell migration both in vitro and in vivo.

The small guanosine triphosphatase Rab13 functions in exocytic vesicle trafficking in epithelial cells. Alterations in Rab13 activity have been observed in human cancers, yet the mechanism of Rab13 activation and its role in cancer progression remain unclear. In this paper, we identify the DENN domain protein DENND2B as the guanine nucleotide exchange factor for Rab13 and develop a novel Förster resonance energy transfer–based Rab biosensor to reveal activation of Rab13 by DENND2B at the leading edge of migrating cells. DENND2B interacts with the Rab13 effector MICAL-L2 at the cell periphery, and this interaction is required for the dynamic remodeling of the cell’s leading edge. Disruption of Rab13-mediated trafficking dramatically limits the invasive behavior of epithelial cells in vitro and the growth and migration of highly invasive cancer cells in vivo. Thus, blocking Rab13 activation by DENND2B may provide a novel target to limit the spread of epithelial cancers.

Background fluorescence estimation and vesicle segmentation in live cell imaging with conditional random fields

Image analysis applied to fluorescence live cell microscopy has become a key tool in molecular biology since it enables to characterize biological processes in space and time at the subcellular level. In fluorescence microscopy imaging, the moving tagged structures of interest, such as vesicles, appear as bright spots over a static or nonstatic background. In this paper, we consider the problem of vesicle segmentation and time-varying background estimation at the cellular scale. The main idea is to formulate the joint segmentation-estimation problem in the general conditional random field framework. Furthermore, segmentation of vesicles and background estimation are alternatively performed by energy minimization using a min cut-max flow algorithm. The proposed approach relies on a detection measure computed from intensity contrasts between neighboring blocks in fluorescence microscopy images. This approach permits analysis of either 2D + time or 3D + time data. We demonstrate the performance of the so-called C-CRAFT through an experimental comparison with the state-of-the-art methods in fluorescence video-microscopy. We also use this method to characterize the spatial and temporal distribution of Rab6 transport carriers at the cell periphery for two different specific adhesion geometries.

Spatial information in large-scale neural recordings

To record from a given neuron, a recording technology must be able to separate the activity of that neuron from the activity of its neighbors. Here, we develop a Fisher information based framework to determine the conditions under which this is feasible for a given technology. This framework combines measurable point spread functions with measurable noise distributions to produce theoretical bounds on the precision with which a recording technology can localize neural activities. If there is sufficient information to uniquely localize neural activities, then a technology will, from an information theoretic perspective, be able to record from these neurons. We (1) describe this framework, and (2) demonstrate its application in model experiments. This method generalizes to many recording devices that resolve objects in space and should be useful in the design of next-generation scalable neural recording systems.

Statistical analysis of molecule colocalization in bioimaging

The quantitative analysis of molecule interactions in bioimaging is key for understanding the molecular orchestration of cellular processes and is generally achieved through the study of the spatial colocalization between the different populations of molecules. Colocalization methods are traditionally divided into pixel-based methods that measure global correlation coefficients from the overlap between pixel intensities in different color channels, and object-based methods that first segment molecule spots and then analyze their spatial distributions with second-order statistics. Here, we present a review of such colocalization methods and give a quantitative comparison of their relative merits in different types of biological applications and contexts. We show on synthetic and biological images that object-based methods are more robust statistically than pixel-based methods, and allow moreover to quantify accurately the number of colocalized molecules.

Characterizing the composition of molecular motors on moving axonal cargo using "cargo mapping" analysis

Understanding the mechanisms by which molecular motors coordinate their activities to transport vesicular cargoes within neurons requires the quantitative analysis of motor/cargo associations at the single vesicle level. The goal of this protocol is to use quantitative fluorescence microscopy to correlate ("map") the position and directionality of movement of live cargo to the composition and relative amounts of motors associated with the same cargo. "Cargo mapping" consists of live imaging of fluorescently labeled cargoes moving in axons cultured on microfluidic devices, followed by chemical fixation during recording of live movement, and subsequent immunofluorescence (IF) staining of the exact same axonal regions with antibodies against motors. Colocalization between cargoes and their associated motors is assessed by assigning sub-pixel position coordinates to motor and cargo channels, by fitting Gaussian functions to the diffraction-limited point spread functions representing individual fluorescent point sources. Fixed cargo and motor images are subsequently superimposed to plots of cargo movement, to "map" them to their tracked trajectories. The strength of this protocol is the combination of live and IF data to record both the transport of vesicular cargoes in live cells and to determine the motors associated to these exact same vesicles. This technique overcomes previous challenges that use biochemical methods to determine the average motor composition of purified heterogeneous bulk vesicle populations, as these methods do not reveal compositions on single moving cargoes. Furthermore, this protocol can be adapted for the analysis of other transport and/or trafficking pathways in other cell types to correlate the movement of individual intracellular structures with their protein composition. Limitations of this protocol are the relatively low throughput due to low transfection efficiencies of cultured primary neurons and a limited field of view available for high-resolution imaging. Future applications could include methods to increase the number of neurons expressing fluorescently labeled cargoes.

Automated detection and tracking of many cells by using 4D live-cell imaging data

Motivation: Automated fluorescence microscopes produce massive amounts of images observing cells, often in four dimensions of space and time. This study addresses two tasks of time-lapse imaging analyses; detection and tracking of the many imaged cells, and it is especially intended for 4D live-cell imaging of neuronal nuclei of Caenorhabditis elegans. The cells of interest appear as slightly deformed ellipsoidal forms. They are densely distributed, and move rapidly in a series of 3D images. Thus, existing tracking methods often fail because more than one tracker will follow the same target or a tracker transits from one to other of different targets during rapid moves.

Results: The present method begins by performing the kernel density estimation in order to convert each 3D image into a smooth, continuous function. The cell bodies in the image are assumed to lie in the regions near the multiple local maxima of the density function. The tasks of detecting and tracking the cells are then addressed with two hill-climbing algorithms. The positions of the trackers are initialized by applying the cell-detection method to an image in the first frame. The tracking method keeps attacking them to near the local maxima in each subsequent image. To prevent the tracker from following multiple cells, we use a Markov random field (MRF) to model the spatial and temporal covariation of the cells and to maximize the image forces and the MRF-induced constraint on the trackers. The tracking procedure is demonstrated with dynamic 3D images that each contain >100 neurons of C.elegans.

Availability:http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014

Supplementary information:Supplementary data are available at http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014

Contact:yoshidar@ism.ac.jp

Pericentromere tension is self-regulated by spindle structure in metaphase

Pericentromere tension in yeast is substantial and is tightly self-regulated by the metaphase mitotic spindle through adjustments in spindle structure.

During cell division, a mitotic spindle is built by the cell and acts to align and stretch duplicated sister chromosomes before their ultimate segregation into daughter cells. Stretching of the pericentromeric chromatin during metaphase is thought to generate a tension-based signal that promotes proper chromosome segregation. However, it is not known whether the mitotic spindle actively maintains a set point tension magnitude for properly attached sister chromosomes to facilitate robust mechanochemical checkpoint signaling. By imaging and tracking the thermal movements of pericentromeric fluorescent markers in Saccharomyces cerevisiae, we measured pericentromere stiffness and then used the stiffness measurements to quantitatively evaluate the tension generated by pericentromere stretch during metaphase in wild-type cells and in mutants with disrupted chromosome structure. We found that pericentromere tension in yeast is substantial (4–6 pN) and is tightly self-regulated by the mitotic spindle: through adjustments in spindle structure, the cell maintains wild-type tension magnitudes even when pericentromere stiffness is disrupted.

A novel multiple hypothesis based particle tracking method for clathrin mediated endocytosis analysis using fluorescence microscopy

In order to quantitatively analyze biological images and study underlying mechanisms of the cellular and subcellular processes, it is often required to track a large number of particles involved in these processes. Manual tracking can be performed by the biologists, but the workload is very heavy. In this paper, we present an automatic particle tracking method for analyzing an essential subcellular process, namely clathrin mediated endocytosis. The framework of the tracking method is an extension of the classical multiple hypothesis tracking (MHT), and it is designed to manage trajectories, solve data association problems, and handle pseudo-splitting/merging events. In the extended MHT framework, particle tracking becomes evaluating two types of hypotheses. The first one is the trajectory-related hypothesis, to test whether a recovered trajectory is correct, and the second one is the observation-related hypothesis, to test whether an observation from an image belongs to a real particle. Here, an observation refers to a detected particle and its feature vector. To detect the particles in 2D fluorescence images taken using total internal reflection microscopy, the images are segmented into regions, and the features of the particles are obtained by fitting Gaussian mixture models into each of the image regions. Specific models are developed according to the properties of the particles. The proposed tracking method is demonstrated on synthetic data under different scenarios and applied to real data.

Bending the rules: widefield microscopy and the Abbe limit of resolution

One of the most fundamental concepts of microscopy is that of resolution-the ability to clearly distinguish two objects as separate. Recent advances such as structured illumination microscopy (SIM) and point localization techniques including photoactivated localization microscopy (PALM), and stochastic optical reconstruction microscopy (STORM) strive to overcome the inherent limits of resolution of the modern light microscope. These techniques, however, are not always feasible or optimal for live cell imaging. Thus, in this review, we explore three techniques for extracting high resolution data from images acquired on a widefield microscope-deconvolution, model convolution, and Gaussian fitting. Deconvolution is a powerful tool for restoring a blurred image using knowledge of the point spread function (PSF) describing the blurring of light by the microscope, although care must be taken to ensure accuracy of subsequent quantitative analysis. The process of model convolution also requires knowledge of the PSF to blur a simulated image which can then be compared to the experimentally acquired data to reach conclusions regarding its geometry and fluorophore distribution. Gaussian fitting is the basis for point localization microscopy, and can also be applied to tracking spot motion over time or measuring spot shape and size. All together, these three methods serve as powerful tools for high-resolution imaging using widefield microscopy.

S. cerevisiae chromosomes biorient via gradual resolution of syntely between S phase and anaphase

Following DNA replication, eukaryotic cells must biorient all sister chromatids prior to cohesion cleavage at anaphase. In animal cells, sister chromatids gradually biorient during prometaphase, but current models of mitosis in S. cerevisiae assume that biorientation is established shortly after S phase. This assumption is based on the observation of a bilobed distribution of yeast kinetochores early in mitosis and suggests fundamental differences between yeast mitosis and mitosis in animal cells. By applying super-resolution imaging methods, we show that yeast and animal cells share the key property of gradual and stochastic chromosome biorientation. The characteristic bilobed distribution of yeast kinetochores, hitherto considered synonymous for biorientation, arises from kinetochores in mixed attachment states to microtubules, the length of which discriminates bioriented from syntelic attachments. Our results offer a revised view of mitotic progression in S. cerevisiae that augments the relevance of mechanistic information obtained in this powerful genetic system for mammalian mitosis.

Multiple hypothesis tracking for cluttered biological image sequences

In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, which is of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unified probabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particle motion and existence and of fluorescence image features. For the track extraction process per se, the very cluttered conditions motivate the adoption of a multiframe approach that enforces tracking decision robustness to poor imaging conditions and to random target movements. We tackle the large-scale nature of the problem by adapting the multiple hypothesis tracking algorithm to the proposed framework, resulting in a method with a favorable tradeoff between the model complexity and the computational cost of the tracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to be the only method providing high-quality results despite the critically poor imaging conditions and the dense target presence. We thus demonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biological processes, which is promising for further developments in this domain.

Automatic particle detection in microscopy using temporal correlations

One of the fundamental problems in the analysis of single particle tracking data is the detection of individual particle positions from microscopy images. Distinguishing true particles from noise with a minimum of false positives and false negatives is an important step that will have substantial impact on all further analysis of the data. A common approach is to obtain a plausible set of particles from a larger set of candidate particles by filtering using manually selected threshold values for intensity, size, shape, and other parameters describing a particle. This introduces subjectivity into the analysis and hinders reproducibility. In this paper, we introduce a method for automatic selection of these threshold values based on maximizing temporal correlations in particle count time series. We use Markov Chain Monte Carlo to find the threshold values corresponding to the maximum correlation, and we study several experimental data sets to assess the performance of the method in practice by comparing manually selected threshold values from several independent experts with automatically selected threshold values. We conclude that the method produces useful results, reducing subjectivity and the need for manual intervention, a great benefit being its easy integratability into many already existing particle detection algorithms.

Quantitative fluorescent speckle microscopy (QFSM) to measure actin dynamics

Quantitative fluorescent speckle microscopy (QFSM) is a live-cell imaging method to analyze the dynamics of macromolecular assemblies with high spatial and temporal resolution. Its greatest successes were in the analysis of actin filament and adhesion dynamics in the context of cell migration and microtubule dynamics in interphase and the meiotic/mitotic spindle. Here, focus is on the former application to illustrate the procedures of FSM imaging and the computational image processing that extracts quantitative information from these experiments. QFSM is advantageous over other methods because it measures the movement and turnover kinetics of the actin filament (F-actin) network in living cells across the entire field of view. Experiments begin with the microinjection of fluorophore-labeled actin into cells, which generate a low ratio of fluorescently labeled to endogenously unlabeled actin monomers. Spinning disk confocal or wide-field imaging then visualizes fluorophore clusters (two to eight actin monomers) within the assembled F-actin network as speckles. QFSM software identifies and computationally tracks and utilizes the location, appearance, and disappearance of speckles to derive network flows and maps of the rate of filament assembly and disassembly.

Improved synapse detection for mGRASP-assisted brain connectivity mapping

Motivation: A new technique, mammalian green fluorescence protein (GFP) reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy.

Results: We developed a fully automatic method for detecting mGRASP-labeled synapse puncta. By modeling each punctum as a Gaussian distribution, our method enables accurate detection even when puncta of varying size and shape partially overlap. The method consists of three stages: blob detection by global thresholding; blob separation by watershed; and punctum modeling by a variational Bayesian Gaussian mixture models. Extensive testing shows that the three-stage method improved detection accuracy markedly, and especially reduces under-segmentation. The method provides a goodness-of-fit score for each detected punctum, allowing efficient error detection. We applied this advantage to also develop an efficient interactive method for correcting errors.

Availability: The software is available on http://jinny.kist.re.kr

Contact:tingzhao@gmail.com; kimj@kist.re.kr

Quantum dot-based single-molecule microscopy for the study of protein dynamics

Real-time microscopic visualization of single molecules in living cells provides a molecular perspective of cellular dynamics, which is difficult to be observed by conventional ensemble techniques. Among various classes of fluorescent tags used in single-molecule tracking, quantum dots are particularly useful due to their unique photophysical properties. This chapter provides an overview of single quantum dot tracking for protein dynamic studies. First, we review the fundamental diffraction limit of conventional optical systems and recent developments in single-molecule detection beyond the diffraction barrier. Second, we describe methods to prepare water-soluble quantum dots for biological labeling and single-molecule microscopy experimental design. Third, we provide detailed methods to perform quantum dot-based single-molecule microscopy. This technical section covers three protocols including (1) imaging system calibration using spin-coated single quantum dots, (2) single quantum dot labeling in living cells, and (3) tracking algorithms for single-molecule analysis.

A multiple hypothesis based method for particle tracking and its extension for cell segmentation

In biological studies, it is often required to track thousands of small particles in microscopic images to analyze underlying mechanisms of cellular and subcellular processes which may lead to better understanding of some disease processes. In this paper, we present an automatic particle tracking method and apply it for analyzing an essential subcellular process, namely clathrin mediated endocytosis using total internal reflection microscopy. Particles are detected by using image filters and subsequently Gaussian mixture models are fitted to achieve sub-pixel resolution. A multiple hypothesis based framework is designed to solve data association problems and handle splitting/merging events. The tracking method is demonstrated on synthetic data under different scenarios and applied to real data. We also show that, by equipping with a cell detection module, the method can be extended straightforwardly for segmenting cell images taken by two-photon excitation microscopy.

Single quantum dot imaging in living cells

Direct visualization of biological processes at single-molecule level provides a detailed perspective which conventional bulk measurements are hard to achieve. Among various classes of fluorescent tags used in single-molecule tracking, quantum dots are particularly useful due to their unique photophysical properties. In this chapter, we describe the principles, methodologies, and experimental protocols for qdot-based single-molecule imaging. The first half provides an overview of fluorescent microscopy and advances in single-molecule tracking using quantum dots. The remainder of this chapter describes methods to carry out qdot-based single-molecule experiments. Detailed protocols including qdot labeling, microscopy setup, and single-molecule analysis using appropriate computational programs are given.

Myo1c binding to submembrane actin mediates insulin-induced tethering of GLUT4 vesicles

GLUT4-containing vesicles cycle between the plasma membrane and intracellular compartments. Insulin promotes GLUT4 exocytosis by regulating GLUT4 vesicle arrival at the cell periphery and its subsequent tethering, docking, and fusion with the plasma membrane. The molecular machinery involved in GLUT4 vesicle tethering is unknown. We show here that Myo1c, an actin-based motor protein that associates with membranes and actin filaments, is required for insulin-induced vesicle tethering in muscle cells. Myo1c was found to associate with both mobile and tethered GLUT4 vesicles and to be required for vesicle capture in the total internal reflection fluorescence (TIRF) zone beneath the plasma membrane. Myo1c knockdown or overexpression of an actin binding-deficient Myo1c mutant abolished insulin-induced vesicle immobilization, increased GLUT4 vesicle velocity in the TIRF zone, and prevented their externalization. Conversely, Myo1c overexpression immobilized GLUT4 vesicles in the TIRF zone and promoted insulin-induced GLUT4 exposure to the extracellular milieu. Myo1c also contributed to insulin-dependent actin filament remodeling. Thus we propose that interaction of vesicular Myo1c with cortical actin filaments is required for insulin-mediated tethering of GLUT4 vesicles and for efficient GLUT4 surface delivery in muscle cells.

Subpixel colocalization reveals amyloid precursor protein-dependent kinesin-1 and dynein association with axonal vesicles

Intracellular transport of vesicles and organelles along microtubules is powered by kinesin and cytoplasmic dynein molecular motors. Both motors can attach to the same cargo and thus must be coordinated to ensure proper distribution of intracellular materials. Although a number of hypotheses have been proposed to explain how these motors are coordinated, considerable uncertainty remains, in part because of the absence of methods for assessing motor subunit composition on individual vesicular cargos. We developed a robust quantitative immunofluorescence method based on subpixel colocalization to elucidate relative kinesin-1 and cytoplasmic dynein motor subunit composition of individual, endogenous amyloid precursor protein (APP) vesicles in mouse hippocampal cells. The resulting method and data allow us to test a key in vivo prediction of the hypothesis that APP can recruit kinesin-1 to APP vesicles in neuronal axons. We found that APP levels are well-correlated with the amount of the light chain of kinesin-1 (KLC1) and the heavy chain of cytoplasmic dynein (DHC1) on vesicles. In addition, genetic reduction of APP diminishes KLC1 and DHC1 levels on APP cargos. Finally, our data reveal that reduction of KLC1 leads to decreased levels of DHC1 on APP vesicles, suggesting that KLC1 is necessary for the association of DHC1 to these cargos, and help to explain previously reported retrograde transport defects generated when kinesin-1 is reduced.

Predictive-focus illumination for reducing photodamage in live-cell microscopy

Due to photobleaching and phototoxicity induced by high-intensity excitation light, the number of fluorescence images that can be obtained in live cells is always limited. This limitation becomes particularly prominent in multidimensional recordings when multiple Z-planes are captured at every time point. Here we present a simple technique, termed predictive-focus illumination (PFI), which helps to minimize cells' exposure to light by decreasing the number of Z-planes that need to be captured in live-cell 3D time-lapse recordings. PFI utilizes computer tracking to predict positions of objects of interest (OOIs) and restricts image acquisition to small dynamic Z-regions centred on each OOI. Importantly, PFI does not require hardware modifications and it can be easily implemented on standard wide-field and spinning-disc confocal microscopes.

Differential motion dynamics of synaptic vesicles undergoing spontaneous and activity-evoked endocytosis

Synaptic vesicle exo- and endocytosis are usually driven by neuronal activity but can also occur spontaneously. The identity and differences between vesicles supporting evoked and spontaneous neurotransmission remain highly debated. Here we combined nanometer-resolution imaging with a transient motion analysis approach to examine the dynamics of individual synaptic vesicles in hippocampal terminals under physiological conditions. We found that vesicles undergoing spontaneous and stimulated endocytosis differ in their dynamic behavior, particularly in the ability to engage in directed motion. Our data indicate that such motional differences depend on the myosin family of motor proteins, particularly myosin II. Analysis of synaptic transmission in the presence of myosin II inhibitor confirmed a specific role for myosin II in evoked, but not spontaneous, neurotransmission and also suggested a functional role of myosin II-mediated vesicle motion in supporting vesicle mobilization during neural activity.