A modular chemigenetic calcium indicator enables in vivo functional imaging with near-infrared light.

Genetically encoded fluorescent calcium indicators have revolutionized neuroscience and other biological fields by allowing cellular-resolution recording of physiology during behavior. However, we currently lack bright, genetically targetable indicators in the near infrared that can be used in animals. Here, we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains that can be genetically targeted to specific cell populations. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several dye-ligands that efficiently label the central nervous system in animals. When bound to a near-infrared dye-ligand, WHaloCaMP1a is more than twice as bright as jGCaMP8s, and shows a 7× increase in fluorescence intensity and a 2.1 ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a with near-infrared fluorescence emission to image Ca2+ responses in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae, and to quantitate calcium concentration using fluorescence lifetime imaging microscopy (FLIM).

Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

In vivo glucose imaging in multiple model organisms with an engineered single-wavelength sensor.

Glucose is arguably the most important molecule in metabolism, and its dysregulation underlies diabetes. We describe a family of single-wavelength genetically encoded glucose sensors with a high signal-to-noise ratio, fast kinetics, and affinities varying over four orders of magnitude (1 µM to 10 mM). The sensors allow mechanistic characterization of glucose transporters expressed in cultured cells with high spatial and temporal resolution. Imaging of neuron/glia co-cultures revealed ∼3-fold faster glucose changes in astrocytes. In larval Drosophila central nervous system explants, intracellular neuronal glucose fluxes suggested a rostro-caudal transport pathway in the ventral nerve cord neuropil. In zebrafish, expected glucose-related physiological sequelae of insulin and epinephrine treatments were directly visualized. Additionally, spontaneous muscle twitches induced glucose uptake in muscle, and sensory and pharmacological perturbations produced large changes in the brain. These sensors will enable rapid, high-resolution imaging of glucose influx, efflux, and metabolism in behaving animals.

Live-cell imaging in the era of too many microscopes.

At the time of this writing, searching Google Scholar for 'light-sheet microscopy' returns almost 8500 results; over three-quarters of which were published in the last 5 years alone. Searching for other advanced imaging methods in the last 5 years yields similar results: 'super-resolution microscopy' (>16 000), 'single-molecule imaging' (almost 10 000), SPIM (Single Plane Illumination Microscopy, 5000), and 'lattice light-sheet' (1300). The explosion of new imaging methods has also produced a dizzying menagerie of acronyms, with over 100 different species of 'light-sheet' alone, from SPIM to UM (Ultra microscopy) to SiMView (Simultaneous MultiView) to iSPIM (inclined SPIM, not to be confused with iSPIM, inverted SPIM). How then is the average biologist, without an advanced degree in physics, optics, or computer science supposed to make heads or tails of which method is best suited for their needs? Let us also not forget the plight of the optical physicist, who at best might need help with obtaining healthy samples and keeping them that way, or at worst may not realize the impact their newest technique could have for biologists. This review will not attempt to solve all these problems, but instead highlight some of the most recent, successful mergers between biology and advanced imaging technologies, as well as hopefully provide some guidance for anyone interested in journeying into the world of live-cell imaging.

Metabolic Regulation of Developmental Cell Cycles and Zygotic Transcription.

The thirteen nuclear cleavages that give rise to the Drosophila blastoderm are some of the fastest known cell cycles [1]. Surprisingly, the fertilized egg is provided with at most one-third of the dNTPs needed to complete the thirteen rounds of DNA replication [2]. The rest must be synthesized by the embryo, concurrent with cleavage divisions. What is the reason for the limited supply of DNA building blocks? We propose that frugal control of dNTP synthesis contributes to the well-characterized deceleration of the cleavage cycles and is needed for robust accumulation of zygotic gene products. In support of this model, we demonstrate that when the levels of dNTPs are abnormally high, nuclear cleavages fail to sufficiently decelerate, the levels of zygotic transcription are dramatically reduced, and the embryo catastrophically fails early in gastrulation. Our work reveals a direct connection between metabolism, the cell cycle, and zygotic transcription.

A practical guide to adaptive light-sheet microscopy.

We describe the implementation and use of an adaptive imaging framework for optimizing spatial resolution and signal strength in a light-sheet microscope. The framework, termed AutoPilot, comprises hardware and software modules for automatically measuring and compensating for mismatches between light-sheet and detection focal planes in living specimens. Our protocol enables researchers to introduce adaptive imaging capabilities in an existing light-sheet microscope or use our SiMView microscope blueprint to set up a new adaptive multiview light-sheet microscope. The protocol describes (i) the mechano-optical implementation of the adaptive imaging hardware, including technical drawings for all custom microscope components; (ii) the algorithms and software library for automated adaptive imaging, including the pseudocode and annotated source code for all software modules; and (iii) the execution of adaptive imaging experiments, as well as the configuration and practical use of the AutoPilot framework. Setup of the adaptive imaging hardware and software takes 1-2 weeks each. Previous experience with light-sheet microscopy and some familiarity with software engineering and building of optical instruments are recommended. Successful implementation of the protocol recovers near diffraction-limited performance in many parts of typical multicellular organisms studied with light-sheet microscopy, such as fruit fly and zebrafish embryos, for which resolution and signal strength are improved two- to fivefold.

A Preferred Curvature-Based Continuum Mechanics Framework for Modeling Embryogenesis.

Mechanics plays a key role in the development of higher organisms. However, understanding this relationship is complicated by the difficulty of modeling the link between local forces generated at the subcellular level and deformations observed at the tissue and whole-embryo levels. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation by cytoskeletal elements enters a continuum mechanics formulation for the full system in the form of local changes in preferred curvature. This allows us to express and solve the system using only tissue strains. Locations of preferred curvature are simply related to products of gene expression. A solution, in that context, means relaxing the system's mechanical energy to yield global morphogenetic predictions that accommodate a tendency toward the local preferred curvature, without a need to explicitly model force-generation mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a 3D spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.

A general method to fine-tune fluorophores for live-cell and in vivo imaging.

Pushing the frontier of fluorescence microscopy requires the design of enhanced fluorophores with finely tuned properties. We recently discovered that incorporation of four-membered azetidine rings into classic fluorophore structures elicits substantial increases in brightness and photostability, resulting in the Janelia Fluor (JF) series of dyes. We refined and extended this strategy, finding that incorporation of 3-substituted azetidine groups allows rational tuning of the spectral and chemical properties of rhodamine dyes with unprecedented precision. This strategy allowed us to establish principles for fine-tuning the properties of fluorophores and to develop a palette of new fluorescent and fluorogenic labels with excitation ranging from blue to the far-red. Our results demonstrate the versatility of these new dyes in cells, tissues and animals.

Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms.

Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.

Real-Time Three-Dimensional Cell Segmentation in Large-Scale Microscopy Data of Developing Embryos.

We present the Real-time Accurate Cell-shape Extractor (RACE), a high-throughput image analysis framework for automated three-dimensional cell segmentation in large-scale images. RACE is 55-330 times faster and 2-5 times more accurate than state-of-the-art methods. We demonstrate the generality of RACE by extracting cell-shape information from entire Drosophila, zebrafish, and mouse embryos imaged with confocal and light-sheet microscopes. Using RACE, we automatically reconstructed cellular-resolution tissue anisotropy maps across developing Drosophila embryos and quantified differences in cell-shape dynamics in wild-type and mutant embryos. We furthermore integrated RACE with our framework for automated cell lineaging and performed joint segmentation and cell tracking in entire Drosophila embryos. RACE processed these terabyte-sized datasets on a single computer within 1.4 days. RACE is easy to use, as it requires adjustment of only three parameters, takes full advantage of state-of-the-art multi-core processors and graphics cards, and is available as open-source software for Windows, Linux, and Mac OS.

Whole-animal functional and developmental imaging with isotropic spatial resolution.

Imaging fast cellular dynamics across large specimens requires high resolution in all dimensions, high imaging speeds, good physical coverage and low photo-damage. To meet these requirements, we developed isotropic multiview (IsoView) light-sheet microscopy, which rapidly images large specimens via simultaneous light-sheet illumination and fluorescence detection along four orthogonal directions. Combining these four views by means of high-throughput multiview deconvolution yields images with high resolution in all three dimensions. We demonstrate whole-animal functional imaging of Drosophila larvae at a spatial resolution of 1.1-2.5 µm and temporal resolution of 2 Hz for several hours. We also present spatially isotropic whole-brain functional imaging in Danio rerio larvae and spatially isotropic multicolor imaging of fast cellular dynamics across gastrulating Drosophila embryos. Compared with conventional light-sheet microscopy, IsoView microscopy improves spatial resolution at least sevenfold and decreases resolution anisotropy at least threefold. Compared with existing high-resolution light-sheet techniques, IsoView microscopy effectively doubles the penetration depth and provides subsecond temporal resolution for specimens 400-fold larger than could previously be imaged.

Efficient processing and analysis of large-scale light-sheet microscopy data.

Light-sheet microscopy is a powerful method for imaging the development and function of complex biological systems at high spatiotemporal resolution and over long time scales. Such experiments typically generate terabytes of multidimensional image data, and thus they demand efficient computational solutions for data management, processing and analysis. We present protocols and software to tackle these steps, focusing on the imaging-based study of animal development. Our protocols facilitate (i) high-speed lossless data compression and content-based multiview image fusion optimized for multicore CPU architectures, reducing image data size 30-500-fold; (ii) automated large-scale cell tracking and segmentation; and (iii) visualization, editing and annotation of multiterabyte image data and cell-lineage reconstructions with tens of millions of data points. These software modules are open source. They provide high data throughput using a single computer workstation and are readily applicable to a wide spectrum of biological model systems.

Whole-central nervous system functional imaging in larval Drosophila.

Understanding how the brain works in tight concert with the rest of the central nervous system (CNS) hinges upon knowledge of coordinated activity patterns across the whole CNS. We present a method for measuring activity in an entire, non-transparent CNS with high spatiotemporal resolution. We combine a light-sheet microscope capable of simultaneous multi-view imaging at volumetric speeds 25-fold faster than the state-of-the-art, a whole-CNS imaging assay for the isolated Drosophila larval CNS and a computational framework for analysing multi-view, whole-CNS calcium imaging data. We image both brain and ventral nerve cord, covering the entire CNS at 2 or 5 Hz with two- or one-photon excitation, respectively. By mapping network activity during fictive behaviours and quantitatively comparing high-resolution whole-CNS activity maps across individuals, we predict functional connections between CNS regions and reveal neurons in the brain that identify type and temporal state of motor programs executed in the ventral nerve cord.

Live imaging of nervous system development and function using light-sheet microscopy.

In vivo imaging applications typically require carefully balancing conflicting parameters. Often it is necessary to achieve high imaging speed, low photo-bleaching, and photo-toxicity, good three-dimensional resolution, high signal-to-noise ratio, and excellent physical coverage at the same time. Light-sheet microscopy provides good performance in all of these categories, and is thus emerging as a particularly powerful live imaging method for the life sciences. We see an outstanding potential for applying light-sheet microscopy to the study of development and function of the early nervous system in vertebrates and higher invertebrates. Here, we review state-of-the-art approaches to live imaging of early development, and show how the unique capabilities of light-sheet microscopy can further advance our understanding of the development and function of the nervous system. We discuss key considerations in the design of light-sheet microscopy experiments, including sample preparation and fluorescent marker strategies, and provide an outlook for future directions in the field.

Light sheet-based imaging and analysis of early embryogenesis in the fruit fly.

The fruit fly is an excellent model system for investigating the sequence of epithelial tissue invaginations constituting the process of gastrulation. By combining recent advancements in light sheet fluorescence microscopy (LSFM) and image processing, the three-dimensional fly embryo morphology and relevant gene expression patterns can be accurately recorded throughout the entire process of embryogenesis. LSFM provides exceptionally high imaging speed, high signal-to-noise ratio, low level of photoinduced damage, and good optical penetration depth. This powerful combination of capabilities makes LSFM particularly suitable for live imaging of the fly embryo.The resulting high-information-content image data are subsequently processed to obtain the outlines of cells and cell nuclei, as well as the geometry of the whole embryo tissue by image segmentation. Furthermore, morphodynamics information is extracted by computationally tracking objects in the image. Towards that goal we describe the successful implementation of a fast fitting strategy of Gaussian mixture models.The data obtained by image processing is well-suited for hypothesis testing of the detailed biomechanics of the gastrulating embryo. Typically this involves constructing computational mechanics models that consist of an objective function providing an estimate of strain energy for a given morphological configuration of the tissue, and a numerical minimization mechanism of this energy, achieved by varying morphological parameters.In this chapter, we provide an overview of in vivo imaging of fruit fly embryos using LSFM, computational tools suitable for processing the resulting images, and examples of computational biomechanical simulations of fly embryo gastrulation.

Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data.

The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for the segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (i) generality by reconstructing cell lineages in four-dimensional, terabyte-sized image data sets of fruit fly, zebrafish and mouse embryos acquired with three types of fluorescence microscopes, (ii) scalability by analyzing advanced stages of development with up to 20,000 cells per time point at 26,000 cells min(-1) on a single computer workstation and (iii) ease of use by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0% linkage accuracy across all species and imaging modalities. Using our system, we performed the first cell lineage reconstruction of early Drosophila melanogaster nervous system development, revealing neuroblast dynamics throughout an entire embryo.

Regulation of branching dynamics by axon-intrinsic asymmetries in Tyrosine Kinase Receptor signaling.

Axonal branching allows a neuron to connect to several targets, increasing neuronal circuit complexity. While axonal branching is well described, the mechanisms that control it remain largely unknown. We find that in the Drosophila CNS branches develop through a process of excessive growth followed by pruning. In vivo high-resolution live imaging of developing brains as well as loss and gain of function experiments show that activation of Epidermal Growth Factor Receptor (EGFR) is necessary for branch dynamics and the final branching pattern. Live imaging also reveals that intrinsic asymmetry in EGFR localization regulates the balance between dynamic and static filopodia. Elimination of signaling asymmetry by either loss or gain of EGFR function results in reduced dynamics leading to excessive branch formation. In summary, we propose that the dynamic process of axon branch development is mediated by differential local distribution of signaling receptors. DOI: http://dx.doi.org/10.7554/eLife.01699.001.

Use of normalization methods for analysis of microarrays containing a high degree of gene effects.

BACKGROUND: High-throughput microarrays are widely used to study gene expression across tissues and developmental stages. Analysis of gene expression data is challenging in these experiments due to the presence of significant percentages of differentially expressed genes (DEG) observed between tissues and developmental stages. Data normalization methods that are widely used today are not designed for data with a large proportion of tissue or gene effects. RESULTS: In our current study, we describe a novel two-dimensional nonparametric normalization method for analyzing microarray data which functions well in the absence or presence of large numbers of gene effects. Rather than relying on an assumption of low variability among most genes, the method implements a unique peak selection strategy to distinguish DEG from genes that are invariant in expression, prior to nonlinear curve fitting. We compared the method under simulated and experimental conditions with five alternative nonlinear normalization approaches: quantile, lowess, robust lowess, invariant set, and cross-correlation (Xcorr). Simulations included various percentages of simulated DEG and the experimental data used is from publicly available datasets known to be difficult to analyze due to the presence of approximately 34% DEG. CONCLUSION: We have demonstrated that the new method provides considerable improvement in the accuracy of data normalization when large proportions of gene effects are present. The performance improvement is mostly attributed to its variable selection component, which is designed to separate expression invariant genes from DEG. Adding this key component of the new method to alternative normalization approaches rescues the most of the sensitivity of these methods to gene effects. The results indicate that our method may be used without prior knowledge of or assumptions about housekeeping genes to normalize microarrays that are quite different.

A gene expression signature predicts survival of patients with stage I non-small cell lung cancer.

BACKGROUND: Lung cancer is the leading cause of cancer-related death in the United States. Nearly 50% of patients with stages I and II non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection. No reliable clinical or molecular predictors are currently available for identifying those at high risk for developing recurrent disease. As a consequence, it is not possible to select those high-risk patients for more aggressive therapies and assign less aggressive treatments to patients at low risk for recurrence. METHODS AND FINDINGS: In this study, we applied a meta-analysis of datasets from seven different microarray studies on NSCLC for differentially expressed genes related to survival time (under 2 y and over 5 y). A consensus set of 4,905 genes from these studies was selected, and systematic bias adjustment in the datasets was performed by distance-weighted discrimination (DWD). We identified a gene expression signature consisting of 64 genes that is highly predictive of which stage I lung cancer patients may benefit from more aggressive therapy. Kaplan-Meier analysis of the overall survival of stage I NSCLC patients with the 64-gene expression signature demonstrated that the high- and low-risk groups are significantly different in their overall survival. Of the 64 genes, 11 are related to cancer metastasis (APC, CDH8, IL8RB, LY6D, PCDHGA12, DSP, NID, ENPP2, CCR2, CASP8, and CASP10) and eight are involved in apoptosis (CASP8, CASP10, PIK3R1, BCL2, SON, INHA, PSEN1, and BIK). CONCLUSIONS: Our results indicate that gene expression signatures from several datasets can be reconciled. The resulting signature is useful in predicting survival of stage I NSCLC and might be useful in informing treatment decisions.

Efficacy of the farnesyltransferase inhibitor R115777 in a rat mammary tumor model: role of Ha-ras mutations and use of microarray analysis in identifying potential targets.

Rats treated with the alkylating agent methylnitrosourea (MNU) develop multiple, hormonally dependent mammary tumors. Roughly 50% of the tumors have Ha-ras mutation, whereas 50% do not. The MNU-induced rat mammary tumor model was employed to examine the therapeutic efficacy of the farnesyltransferase inhibitor (FTI), R115777, and to examine the use of genomics in identifying susceptible tumors as well as identifying genes whose expression are modulated by FTI treatment. In animals bearing palpable mammary tumors (< 7 mm diameter), we performed a surgical biopsy, and 3 days following the biopsy, rats were treated with R115777 (50 mg/kg body wt/day) by gavage. Tumors with Ha-ras mutations underwent profound regression, with nearly 90% showing complete regressions within 4 weeks. In contrast, the non-Ha-ras mutation-bearing tumors yielded a more variable response, although roughly half of the non-Ha-ras mutation tumors underwent significant regression. These results show that although all tumors appear to respond to the FTI inhibitor the tumors with Ha-ras mutations were exquisitely sensitive. We employed a microarray approach to define potential targets and the mechanism of action of R115777 in Ha-ras mutant or wildtype tumors following treatment with FTI. In addition, we determined whether gene expression prior to FTI treatment can be used to differentiate highly sensitive tumors (Ha-ras mutant) and tumors with variable sensitivity (Ha-ras wildtype). Untreated or FTI-treated (4 days at 50 mg/kg body wt) tumors (Ha-ras mutant or wildtype) were examined using oligonucleotide arrays. A significant number of genes were differentially expressed in control rat mammary tumors with or without an activated Ha-ras mutation, suggesting that a microarray analysis might differentiate highly sensitive and variably sensitive tumors. Most of the genes whose expressions were modulated by FTI in tumors were independent of Ha-ras status and were presumably modulated by effects on farnesylation of proteins other than Ha-ras. However, treatment of Ha-ras-mutated mammary tumors with R155777 results in preferential modulation of genes involved in ras-MAP kinase signal transduction pathway and in decreased expression of many genes involved with cell proliferation. In contrast, several classes of genes are altered in rat mammary tumors without a mutated Ha-ras, suggesting that non-ras targets are involved. Ras pathway related genes, p53, WT1 and PCNA, were preferentially modulated in Ha-ras-mutated tumors, whereas modulation of genes in the G-protein pathway, various cytochrome p450s and RB1 are involved in Ha-ras wildtype tumors. Elucidation of gene expression changes in FTI-treated or control rat mammary adenocarcinomas will help in identifying potential pharmacodynamic markers of FTI treatment as well as potential molecular targets of R115777 and other FTIs.