Time-lapse Movies Research Articles

Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.

Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using deep learning. Hence, we develop a pipeline of deep learning models that accurately identify dividing cells in time-lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within the Drosophila pupal wing epithelium, both as it undergoes developmental morphogenesis and as it repairs following laser wounding. We show that the division axis is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) burst of cell divisions back from the leading edge.

Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair

Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using deep learning. Hence, we develop a pipeline of deep learning models that accurately identify dividing cells in time-lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within the Drosophila pupal wing epithelium, both as it undergoes developmental morphogenesis and as it repairs following laser wounding. We show that the division axis is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) burst of cell divisions back from the leading edge.

Drug resistant pancreatic cancer cells exhibit altered biophysical interactions with stromal fibroblasts in imaging studies of 3D co-culture models

Interactions between tumor and stromal cells are well known to play prominent roles in progression of pancreatic ductal adenocarcinoma (PDAC). As knowledge of stromal crosstalk in PDAC has evolved, it has become clear that cancer associated fibroblasts can play both tumor promoting and tumor suppressive roles through a combination of paracrine crosstalk and juxtacrine interactions involving direct physical contact. Another major contributor to dismal survival statistics for PDAC is development of resistance to chemotherapy drugs, though less is known about how the acquisition of chemoresistance impacts upon tumor-stromal crosstalk. Here, we use time lapse imaging and image analysis to study how co-culture geometry impacts interactions between epithelial and stromal cells. We show that extracellular matrix (ECM) overlay cultures in which stromal cells (pancreatic stellate cells, or normal human fibroblasts) are placed adjacent to PDAC cells (PANC1) result in direct heterotypic cell adhesions accompanied by dramatic fibroblast contractility. We analyze these interactions in co-cultures using particle image velocimetry (PIV) analysis to quantify cell velocities over the course of time lapse movie sequences. We further contrast co-cultures of PANC1 with those containing a drug resistant subline (PANC1-OR) previously established in our lab and find that heterotypic cell–cell interactions are suppressed in the latter relative to the parental line. We use RNA-seq and bioinformatics analysis to identify differential gene expression in PANC1 and PANC1-OR, which shows that negative regulation of cell adhesion molecules, consistent with increased epithelial mesenchymal transition (EMT), is also correlated with reduction in the hetrotypic cell–cell contact necessary for the contractile behavior observed in drug naïve cultures. Overall these findings elucidate the role of drug-resistance in inhibiting an avenue of stromal crosstalk which is associated with tumor suppression and also help to establish cell culture conditions useful for further mechanistic investigation.

Drug resistant pancreatic cancer cells exhibit altered biophysical interactions with stromal fibroblasts in imaging studies of 3D co-culture models.

Interactions between tumor and stromal cells are well known to play a prominent roles in progression of pancreatic ductal adenocarcinoma (PDAC). As knowledge of stromal crosstalk in PDAC has evolved, it has become clear that cancer associated fibroblasts can play both tumor promoting and tumor suppressive roles through a combination of paracrine crosstalk and juxtacrine interactions involving direct physical contact. Another major contributor to dismal survival statistics for PDAC is development of resistance to chemotherapy drugs. Though less is known about how the acquisition of chemoresistance impacts upon tumor-stromal crosstalk. Here, we use 3D co-culture geometries to recapitulate juxtacrine interactions between epithelial and stromal cells. In particular, extracellular matrix (ECM) overlay cultures in which stromal cells (pancreatic stellate cells, or normal human fibroblasts) are placed adjacent to PDAC cells (PANC1), result in direct heterotypic cell adhesions accompanied by dramatic fibroblast contractility which leads to highly condensed macroscopic multicellular aggregates as detected using particle image velocimetry (PIV) analysis to quantify cell velocities over the course of time lapse movie sequences. To investigate how drug resistance impacts these juxtacrine interactions we contrast cultures in which PANC1 are substituted with a drug resistant subline (PANC1-OR) previously established in our lab. We find that heterotypic cell-cell interactions are highly suppressed in drug-resistant cells relative to the parental PANC1 cells. To investigate further we conduct RNA-seq and bioinformatics analysis to identify differential gene expression in PANC1 and PANC1-OR, which shows that negative regulation of cell adhesion molecules, consistent with increased epithelial mesenchymal transition (EMT), is also consistent with loss of hetrotypic cell-cell contact necessary for the contractile behavior observed in drug naïve cultures. Overall these findings elucidate the role of drug-resistance in inhibiting an avenue of stromal crosstalk which is associated with tumor suppression and also help to establish cell culture conditions useful for further mechanistic investigation.

Enhanced Cell Tracking Using A GAN-based Super-Resolution Video-to-Video Time-Lapse Microscopy Generative Model.

Cells are among the most dynamic entities, constantly undergoing various processes such as growth, division, movement, and interaction with other cells as well as the environment. Time-lapse microscopy is central to capturing these dynamic behaviors, providing detailed temporal and spatial information that allows biologists to observe and analyze cellular activities in real-time. The analysis of time-lapse microscopy data relies on two fundamental tasks: cell segmentation and cell tracking. Integrating deep learning into bioimage analysis has revolutionized cell segmentation, producing models with high precision across a wide range of biological images. However, developing generalizable deep-learning models for tracking cells over time remains challenging due to the scarcity of large, diverse annotated datasets of time-lapse movies of cells. To address this bottleneck, we propose a GAN-based time-lapse microscopy generator, termed tGAN, designed to significantly enhance the quality and diversity of synthetic annotated time-lapse microscopy data. Our model features a dual-resolution architecture that adeptly synthesizes both low and high-resolution images, uniquely capturing the intricate dynamics of cellular processes essential for accurate tracking. We demonstrate the performance of tGAN in generating high-quality, realistic, annotated time-lapse videos. Our findings indicate that tGAN decreases dependency on extensive manual annotation to enhance the precision of cell tracking models for time-lapse microscopy.

Smart lattice light-sheet microscopy for imaging rare and complex cellular events.

Light-sheet microscopes enable rapid high-resolution imaging of biological specimens; however, biological processes span spatiotemporal scales. Moreover, long-term phenotypes are often instigated by rare or fleeting biological events that are difficult to capture with a single imaging modality. Here, to overcome this limitation, we present smartLLSM, a microscope that incorporates artificial intelligence-based instrument control to autonomously switch between epifluorescent inverted imaging and lattice light-sheet microscopy (LLSM). We apply this approach to two unique processes: cell division and immune synapse formation. In each context, smartLLSM provides population-level statistics across thousands of cells and autonomously captures multicolor three-dimensional datasets or four-dimensional time-lapse movies of rare events at rates that dramatically exceed human capabilities. From this, we quantify the effects of Taxol dose on spindle structure and kinetochore dynamics in dividing cells and of antigen strength on cytotoxic T lymphocyte engagement and lytic granule polarization at the immune synapse. Overall, smartLLSM efficiently detects rare events within heterogeneous cell populations and records these processes with high spatiotemporal four-dimensional imaging over statistically significant replicates.

Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics.

Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as 3D object segmentation and tracking difficult. Here, we present SpinX, a framework for reconstructing gaps between successive image frames by combining deep learning and mathematical object modeling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbor-cell information, non-uniform illumination, and variable fluorophore marker intensities. The automation and continuity introduced here allows the precise 3D tracking and analysis of spindle movements with respect to the cell cortex for the first time. We demonstrate the utility of SpinX using distinct spindle markers, cell lines, microscopes, and drug treatments. In summary, SpinX provides an exciting opportunity to study spindle dynamics in a sophisticated way, creating a framework for step changes in studies using time-lapse microscopy.

Cost-efficient boundary-free surface patterning achieves high effective-throughput of time-lapse microscopy experiments.

Time-lapse microscopy plays critical roles in the studies of cellular dynamics. However, setting up a time-lapse movie experiments is not only laborious but also with low output, mainly due to the cell-losing problem (i.e., cells moving out of limited field of view), especially in a long-time recording. To overcome this issue, we have designed a cost-efficient way that enables cell patterning on the imaging surfaces without any physical boundaries. Using mouse embryonic stem cells as an example system, we have demonstrated that our boundary-free patterned surface solves the cell-losing problem without disturbing their cellular phenotype. Statistically, the presented system increases the effective-throughput of time-lapse microscopy experiments by an order of magnitude.

Front-like expansion and arrest of programmed cell death in brown banana spots

The spot patterns on bananas are a striking case of biological pattern formation and—as a qualitative ripeness indicator—linked to 50 million tons of wasted food per year. Ripening bananas develop these senescent spots as phenolic compounds are enzymatically oxidized and cellular integrity is lost. We characterize the dynamics of the spot expansion and their nucleation rates based on time-lapse movies. Spots nucleate for about 2 days yielding a typical density of 8 spots/cm2. The expansion is initially diffusion controlled and the effective diffusion coefficient decreases with nucleation time from 1.3 to 0.4 mm2 d−1. During and after expansion, the browning fronts maintain a steep and constant intensity gradient. We quantitatively reproduce these features by a reaction–diffusion model that considers the local oxygen concentration and browning degree of the peel. All model parameters are based on measurements and front stalling is explained by decreasing oxygen levels in the nucleation sites.

Non-invasive measurement of nuclear relative stiffness from quantitative analysis of microscopy data

The connection between the properties of a cell tissue and those of the single constituent cells remains to be elucidated. At the purely mechanical level, the degree of rigidity of different cellular components, such as the nucleus and the cytoplasm, modulates the interplay between the cell inner processes and the external environment, while simultaneously mediating the mechanical interactions between neighboring cells. Being able to quantify the correlation between single-cell and tissue properties would improve our mechanobiological understanding of cell tissues. Here we develop a methodology to quantitatively extract a set of structural and motility parameters from the analysis of time-lapse movies of nuclei belonging to jammed and flocking cell monolayers. We then study in detail the correlation between the dynamical state of the tissue and the deformation of the nuclei. We observe that the nuclear deformation rate linearly correlates with the local divergence of the velocity field, which leads to a non-invasive estimate of the elastic modulus of the nucleus relative to the one of the cytoplasm. We also find that nuclei belonging to flocking monolayers, subjected to larger mechanical perturbations, are about two time stiffer than nuclei belonging to dynamically arrested monolayers, in agreement with atomic force microscopy results. Our results demonstrate a non-invasive route to the determination of nuclear relative stiffness for cells in a monolayer.Graphic abstract

Edinger-Westphal peptidergic neurons enable maternal preparatory nesting.

SummaryOptimizing reproductive fitness in mammalians requires behavioral adaptations during pregnancy. Maternal preparatory nesting is an essential behavior for the survival of the upcoming litter. Brain-wide immediate early gene mapping in mice evoked by nesting sequences revealed that phases of nest construction strongly activate peptidergic neurons of the Edinger-Westphal nucleus in pregnant mice. Genetic ablation, bidirectional neuromodulation, and in vitro and in vivo activity recordings demonstrated that these neurons are essential to modulate arousal before sleep to promote nesting specifically. We show that these neurons enable the behavioral effects of progesterone on preparatory nesting by modulating a broad network of downstream targets. Our study deciphers the role of midbrain CART+ neurons in behavioral adaptations during pregnancy vital for reproductive fitness.

Honeybee clusters seethe to keep cool as air temperatures rise

Packing up to relocate is always daunting, but imagine uprooting an entire community and setting forth with no final destination in mind. This is the prospect faced by swarming honeybees as they depart in search of pastures new. Having located a nearby tree, the swarm settles while scouts venture off in search of a new home, yet the cluster continually seethes and churns to maintain a stable temperature within. However, no one had detailed the precise changes that a swarm makes as air temperatures rise and fall. To learn more, Jacob Peters and L. Mahadevan from Harvard University, USA, with Orit Peleg from the University of Colorado, USA, secured a box containing a queen bee to an inverted weighing scale and encouraged the colony to swarm around her, to find out how they adapted as the temperature rose and fell.However, the team quickly found that they were best conducting their experiments at night. ‘On warm sunny days the bees would fly off as if to a new nest site’, recalls Peters. So he and Peleg warmed the clusters from 15°C to 30°C at different rates before keeping the insects warm for 5 h. During that time, the duo took 360 deg snapshots of the clusters every 4 min by swinging a camera around the cluster, taking a picture every 9 deg, to build a 3D profile. Then the duo cooled the air around each cluster at the same rate, to find out how the bees reacted. In addition, James MacArthur from Harvard University designed a bespoke temperature sensor for the bees to cluster around that recorded the temperatures experienced within the colony as it churned in response to the changing air temperatures.Reconstructing time-lapse movies of the clusters, the team could see them become longer and broader as the temperature rose, almost doubling their volume at the highest temperature. ‘A quick look at the time lapse showed dramatic and predictable changes’, says Peters. However, even after 5 h at 30°C, the swarm never achieved a stable structure, continuing to ripple and pulse until Peters and Peleg turned the thermostat down. Yet, the time-lapse movies of the cooling colonies appearing to deflate weren't simple rewinds of the warming expansions. ‘The cluster shrinks quickly in response to cooling but expands sluggishly to its original size as it warms back up’, says Peters.The team also noticed that at one point during the heating process the cluster stopped lengthening and began shortening instead, even though the base continued to broaden, probably because there is a stage when the honeybees within are at full stretch and can no longer cling on to each other if the cluster continues growing longer to avoid overheating. ‘This [manoeuvre] allows them to increase their surface area without a dramatic decrease in density’, says Peters. And, when the temperature around the colony fell, the colony shortened more rapidly than it narrowed, probably because it is easier for bees to climb up and down chains of their sisters than it is for the insects to move inward carrying their colony mates suspended below. The colonies’ pulsating manoeuvres also successfully maintained relatively warm temperatures within (30–36°C), while remaining cooler at the periphery.So, honeybees are able to regulate the temperature within a cluster by spreading apart as air temperatures rise, but they can only go so far before the swarm is in danger of disintegrating. Peters also warns that if environmental temperatures continue to rise, the forces within expanding clusters may be too great for resting swarms to withstand shattering as they attempt to avoid overheating.

Automated phenol-chloroform extraction of high molecular weight genomic DNA for use in long-read single-molecule sequencing.

Background: Automation has increasingly become more commonplace in the research laboratory workspace. The introduction of articulated robotic arms allows the researcher more flexibility in the tasks a single piece of automated machinery can perform. We set out to incorporate automation in processing of genomic DNA organic extractions to increase throughput and limit researchers to the exposure of organic solvents. Methods: In order to automate the genome sequencing pipeline in our laboratory, we programmed a dual-arm anthropomorphic robot, the Robotic Biology Institute's Maholo LabDroid, to perform organic solvent-based genomic DNA extraction from cell lysates. To the best of our knowledge, this is the first time that automation of phenol-chloroform extraction has been reported. Results: We achieved routine extraction of high molecular weight genomic DNA (>100 kb) from diverse biological samples including algae cultured in sea water, bacteria, whole insects, and human cell lines. The results of pulse-field electrophoresis size analysis and the N50 sequencing metrics of reads obtained from Nanopore MinION runs verified the presence of intact DNA suitable for direct sequencing. Conclusions: We present the workflow that can be used to program similar robots and discuss the problems and solutions we encountered in developing the workflow. The protocol can be adapted to analogous methods such as RNA extraction, and there is ongoing work to incorporate further post-extraction steps such as library construction. This work shows the potential for automated robotic workflows to free molecular biological researchers from manual interventions in routine experimental work. A time-lapse movie of the entire automated run is included in this report.

Stain-free detection of embryo polarization using deep learning

Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, impermissible in the in vitro fertilization clinic. Here, we report the use of artificial intelligence to detect polarization from unstained time-lapse movies of mouse embryos. We assembled a dataset of bright-field movie frames from 8-cell-stage embryos, side-by-side with corresponding images of fluorescent markers of cell polarization. We then used an ensemble learning model to detect whether any bright-field frame showed an embryo before or after onset of polarization. Our resulting model has an accuracy of 85% for detecting polarization, significantly outperforming human volunteers trained on the same data (61% accuracy). We discovered that our self-learning model focuses upon the angle between cells as one known cue for compaction, which precedes polarization, but it outperforms the use of this cue alone. By compressing three-dimensional time-lapsed image data into two-dimensions, we are able to reduce data to an easily manageable size for deep learning processing. In conclusion, we describe a method for detecting a key developmental feature of embryo development that avoids clinically impermissible fluorescence staining.

LABKIT: Labeling and Segmentation Toolkit for Big Image Data

We present LABKIT, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. LABKIT is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as well as a memory efficient and fast implementation of the random forest based pixel classification algorithm as the foundation of our software. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. LABKIT is easy to install on virtually all laptops and workstations. Additionally, LABKIT is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in LABKIT via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Finally, LABKIT comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use LABKIT in a number of practical real-world use-cases.

The cyanotoxin cylindrospermopsin slows down cell cycle progression and extends metaphase duration in immortalised human airway epithelial cells

Cylindrospermopsin (CYN) is a cyanobacterial toxin that occurs worldwide in aquatic environments. It is known for its delayed effects upon oral uptake in animals and humans. A less well studied route of CYN internalisation is the inhalation of CYN-contaminated aerosols. We analyzed potential effects of different CYN concentrations (1, 2.5 and 5 μmol/l) on cultures of immortalised human bronchial epithelial cells (16HBE14o−). Impedance, a proxi for cell attachment to the culture support, cell spreading, cell growth and cell proliferation, was measured using an Acea iCELLigence device. Cell division rate and metaphase duration were determined using time lapse movies (Nikon Biostation II) of CYN-exposed cell cultures. Western blot studies were used to determine expression levels of cell cycle regulator proteins, the cyclins B1, D1 and A2. Our investigations revealed that exposure of cells to CYN concentrations of 1 μmol/l or higher led to a concentration- and time-dependent attenuation of impedance development as well as cell proliferation rate, and an extension of the metaphase of the cell cycle. CYN-mediated downregulation of cyclins B1 and D1 may be part of the underlying cell physiological mechanism. These results indicate that exposure of airways in humans and animals to aerosolised CYN over longer periods may be harmful.

SynActJ: Easy-to-Use Automated Analysis of Synaptic Activity

Neuronal synapses are highly dynamic communication hubs that mediate chemical neurotransmission via the exocytic fusion and subsequent endocytic recycling of neurotransmitter-containing synaptic vesicles (SVs). Functional imaging tools allow for the direct visualization of synaptic activity by detecting action potentials, pre- or postsynaptic calcium influx, SV exo- and endocytosis, and glutamate release. Fluorescent organic dyes or synapse-targeted genetic molecular reporters, such as calcium, voltage or neurotransmitter sensors and synapto-pHluorins reveal synaptic activity by undergoing rapid changes in their fluorescence intensity upon neuronal activity on timescales of milliseconds to seconds, which typically are recorded by fast and sensitive widefield live cell microscopy. The analysis of the resulting time-lapse movies in the past has been performed by either manually picking individual structures, custom scripts that have not been made widely available to the scientific community, or advanced software toolboxes that are complicated to use. For the precise, unbiased and reproducible measurement of synaptic activity, it is key that the research community has access to bio-image analysis tools that are easy-to-apply and allow the automated detection of fluorescent intensity changes in active synapses. Here we present SynActJ (Synaptic Activity in ImageJ), an easy-to-use fully open-source workflow that enables automated image and data analysis of synaptic activity. The workflow consists of a Fiji plugin performing the automated image analysis of active synapses in time-lapse movies via an interactive seeded watershed segmentation that can be easily adjusted and applied to a dataset in batch mode. The extracted intensity traces of each synaptic bouton are automatically processed, analyzed, and plotted using an R Shiny workflow. We validate the workflow on time-lapse images of stimulated synapses expressing the SV exo-/endocytosis reporter Synaptophysin-pHluorin or a synapse-targeted calcium sensor, Synaptophysin-RGECO. We compare the automatic workflow to manual analysis and compute calcium-influx and SV exo-/endocytosis kinetics and other parameters for synaptic vesicle recycling under different conditions. We predict SynActJ to become an important tool for the analysis of synaptic activity and synapse properties.

Time to Upgrade: A New OpenSPIM Guide to Build and Operate Advanced OpenSPIM Configurations.

OpenSPIM is an Open Access platform for Selective Plane Illumination Microscopy (SPIM) and allows hundreds of laboratories around the world to generate and process light-sheet data in a cost-effective way due to open-source hardware and software. While setting up a basic OpenSPIM configuration can be achieved expeditiously, correctly assembling and operating more complex OpenSPIM configurations can be challenging for routine standard OpenSPIM users. Detailed instructions on how to equip an OpenSPIM with two illumination sides and two detection axes (X-OpenSPIM) are provided, and a solution is also provided on how the temperature can be controlled in the sample chamber. Additionally, it is demonstrated how to operate it by implementing an ArduinoUNO microcontroller and introducing μOpenSPIM, a new software plugin for OpenSPIM, to facilitate image acquisition. The new software works on any OpenSPIM configuration comes with drift correction functionality, on-the-fly image processing, and gives users more options in the way time-lapse movies are initially set up and saved. Step-by-step guides are also provided within the Supporting Information and on the website on how to align the lasers, configure the hardware, and acquire images using μOpenSPIM. With this, current OpenSPIM users are empowered in various ways, and newcomers striving to use more advanced OpenSPIM systems are helped.

Use of timelapse photography to determine flower opening time and pattern in banana (Musa spp.) for efficient hand pollination

Sterility and low seed set in bananas is the main challenge to their conventional genetic improvement. The first step to seed set in a banana breeding program depends on pollination at the right time to ensure effective fertilization. This study aimed at determining bract opening time (BOT) to enhance efficient pollination and seed set in bananas. A Nikon D810 digital camera was set-up to take pictures of growing banana inflorescences at five-minute intervals and time-lapse movies were developed at a speed of 30 frames per second to allow real-time monitoring of BOT. Genotypes studied included wild banana (1), Mchare (2), Matooke (4), Matooke hybrid (1), and plantain (1). Events of bract opening initiated by bract lift for female flowers (P < 0.01) started at 16:32 h and at 18:54 h for male flowers. Start of bract rolling was at 18:51 h among female flowers (P < 0.001) and 20:48 h for male flowers. Bracts ended rolling at 02:33 h and 01:16 h for female and flowers respectively (P < 0.05). Total time of bract opening (from lift to end of rolling) for female flowers was significantly longer than that of male flowers (P < 0.001). On average, the number of bracts subtending female flowers opening increased from one on the first day, to between one and four on the fourth day. The number regressed to one bract on day eight before start of opening of bracts subtending male flowers. There was a longer opening interval between bracts subtending female and male flowers constituting spatial and temporal separation. Bract rolling increased from partial to complete rolling from proximal to the distal end of the inflorescence among female flower. On the other hand, bracts subtending male flowers completely rolled. Differences in BOT of genotypes with the same reference time of assessment may be partly responsible for variable fertility. Hand pollination time between 07:00 and 10:00 h is slightly late thus an early feasible time should be tried.

Red- and Far-Red-Emitting Zinc Probes with Minimal Phototoxicity for Multiplexed Recording of Orchestrated Insulin Secretion.

Zinc biology, featuring intertwining signaling networks and critical importance to human health, witnesses exciting opportunities in the big data era of physiology. Here, we report a class of red- and far-red-emitting Zn2+ probes with Kd values ranging from 190 nM to 74 μM, which are particularly suitable for real-time monitoring the high concentration of Zn2+ co-released with insulin during vesicular secretory events. Compared to the prototypical rhodamine-based Zn2+ probes, the new class exploits morpholino auxochromes which eliminates phototoxicity during long-term live recording of isolated islets. A Si-rhodamine-based Zn2+ probe with high turn-on ratio (>100), whose synthesis was enabled by a new route featuring late-stage N-alkylation, allowed simultaneous recording of Ca2+ influx, mitochondrial signal, and insulin secretion in isolated mouse islets. The time-lapse multicolor fluorescence movies and their analysis, enabled by red-shifted Zn2+ and other orthogonal physiological probes, highlight the potential impact of biocompatible fluorophores on the fields of islet endocrinology and system biology.