Morphogenesis, the development of the shape of an organism, is a dynamic process on a multitude of scales, from fast subcellular rearrangements and cell movements to slow structural changes at the whole-organism level. Live-imaging approaches based on light microscopy reveal the intricate dynamics of this process and are thus indispensable for investigating the underlying mechanisms. This Review discusses emerging imaging techniques that can record morphogenesis at temporal scales from seconds to days and at spatial scales from hundreds of nanometers to several millimeters. To unlock their full potential, these methods need to be matched with new computational approaches and physical models that help convert highly complex image data sets into biological insights.

Science Profile: A Research Career in Focus

Brain function relies on communication between large populations of neurons across multiple brain areas, a full understanding of which would require knowledge of the time-varying activity of all neurons in the central nervous system. Here we use light-sheet microscopy to record activity, reported through the genetically encoded calcium indicator GCaMP5G, from the entire volume of the brain of the larval zebrafish in vivo at 0.8 Hz, capturing more than 80% of all neurons at single-cell resolution. Demonstrating how this technique can be used to reveal functionally defined circuits across the brain, we identify two populations of neurons with correlated activity patterns. One circuit consists of hindbrain neurons functionally coupled to spinal cord neuropil. The other consists of an anatomically symmetric population in the anterior hindbrain, with activity in the left and right halves oscillating in antiphase, on a timescale of 20 s, and coupled to equally slow oscillations in the inferior olive.

Object detection and classification are key tasks in computer vision that can facilitate high-throughput image analysis of microscopy data. We present a set of local image descriptors for three-dimensional (3D) microscopy datasets inspired by the well-known Haar wavelet framework. We add orientation, illumination and scale information by assuming that the neighborhood surrounding points of interests in the image can be described with ellipsoids, and we increase discriminative power by incorporating edge and shape information into the features. The calculation of the local image descriptors is implemented in a Graphics Processing Unit (GPU) in order to reduce computation time to 1 millisecond per object of interest. We present results for cell division detection in 3D time-lapse fluorescence microscopy with 97.6% accuracy.

Understanding the development of complex multicellular organisms as a function of the underlying cell behavior is one of the most fundamental goals of developmental biology. The ability to quantitatively follow cell dynamics in entire developing embryos is an indispensable step towards such a system-level understanding. In recent years, light-sheet fluorescence microscopy has emerged as a particularly promising strategy for recording the in vivo data required to realize this goal. Using light-sheet fluorescence microscopy, entire complex organisms can be rapidly imaged in three dimensions at sub-cellular resolution, achieving high temporal sampling and excellent signal-to-noise ratio without damaging the living specimen or bleaching fluorescent markers. The resulting datasets allow following individual cells in vertebrate and higher invertebrate embryos over up to several days of development. However, the complexity and size of these multi-terabyte recordings typically preclude comprehensive manual analyses. Thus, new computational approaches are required to automatically segment cell morphologies, accurately track cell identities and systematically analyze cell behavior throughout embryonic development. We review current efforts in light-sheet microscopy and bioimage informatics towards this goal, and argue that comprehensive cell lineage reconstructions are finally within reach for many key model organisms, including fruit fly, zebrafish and mouse.

Visualization of cellular and molecular processes is an indispensable tool for cell biologists, and innovations in microscopy methods unfailingly lead to new biological discoveries. Today, light microscopy (LM) provides ever-higher spatial and temporal resolution and visualization of biological process over enormous ranges. Electron microscopy (EM) is moving into the atomic resolution regime and allowing cellular analyses that are more physiological and sophisticated in scope. Importantly, much is being gained by combining multiple approaches, (e.g., LM and EM) to take advantage of their complementary strengths. The advent of high-throughput microscopies has led to a common need for sophisticated computational methods to quantitatively analyze huge amounts of data and translate images into new biological insights.

Optical flow is a key method used for quantitative motion estimation of biological structures in light microscopy. It has also been used as a key module in segmentation and tracking systems and is considered a mature technology in the field of computer vision. However, most of the research focused on 2D natural images, which are small in size and rich in edges and texture information. In contrast, 3D time-lapse recordings of biological specimens comprise up to several terabytes of image data and often exhibit complex object dynamics as well as blurring due to the point-spread-function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data. We solve optical flow in large 3D time-lapse microscopy datasets by defining a Markov random field (MRF) over super-voxels in the foreground and applying motion smoothness constraints between super-voxels instead of voxel-wise. This model is tailored to the specific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D time-lapse datasets of Drosophila and zebrafish development by analyzing cell motion patterns. We show that our approach is, on average, 10 x faster than commonly used optical flow implementations in the Insight Tool-Kit (ITK) and reduces the average flow end point error by 50% in regions with complex dynamic processes, such as cell divisions.

Light sheet-based fluorescence microscopy (LSFM) is emerging as a powerful imaging technique for the life sciences. LSFM provides an exceptionally high imaging speed, high signal-to-noise ratio, low level of photo-bleaching, and good optical penetration depth. This unique combination of capabilities makes light sheet-based microscopes highly suitable for live imaging applications. Here, we provide an overview of light sheet-based microscopy assays for in vitro and in vivo imaging of biological samples, including cell extracts, soft gels, and large multicellular organisms. We furthermore describe computational tools for basic image processing and data inspection.

The functional state of a cell is largely determined by the spatiotemporal organization of its proteome. Technologies exist for measuring particular aspects of protein turnover and localization, but comprehensive analysis of protein dynamics across different scales is possible only by combining several methods. Here we describe tandem fluorescent protein timers (tFTs), fusions of two single-color fluorescent proteins that mature with different kinetics, which we use to analyze protein turnover and mobility in living cells. We fuse tFTs to proteins in yeast to study the longevity, segregation and inheritance of cellular components and the mobility of proteins between subcellular compartments; to measure protein degradation kinetics without the need for time-course measurements; and to conduct high-throughput screens for regulators of protein turnover. Our experiments reveal the stable nature and asymmetric inheritance of nuclear pore complexes and identify regulators of N-end rule–mediated protein degradation.

Live imaging of large biological specimens is fundamentally limited by the short optical penetration depth of light microscopes. To maximize physical coverage, we developed the SiMView technology framework for high-speed in vivo imaging, which records multiple views of the specimen simultaneously. SiMView consists of a light-sheet microscope with four synchronized optical arms, real-time electronics for long-term sCMOS-based image acquisition at 175 million voxels per second, and computational modules for high-throughput image registration, segmentation, tracking and real-time management of the terabytes of multiview data recorded per specimen. We developed one-photon and multiphoton SiMView implementations and recorded cellular dynamics in entire Drosophila melanogaster embryos with 30-s temporal resolution throughout development. We furthermore performed high-resolution long-term imaging of the developing nervous system and followed neuroblast cell lineages in vivo. SiMView data sets provide quantitative morphological information even for fast global processes and enable accurate automated cell tracking in the entire early embryo.

High-resolution movies in the Digital Embryo repository
Nature News: "Fruitfly development, cell by cell" by Lauren Gravitz
Nature Methods Technology Feature: "Faster frames, clearer pictures" by Monya Baker
Andor Insight Awards: Life Sciences Winner

Light sheet microscopy is a versatile imaging technique with a unique combination of capabilities. It provides high imaging speed, high signal-to-noise ratio and low levels of photobleaching and phototoxic effects. These properties are crucial in a wide range of applications in the life sciences, from live imaging of fast dynamic processes in single cells to long-term observation of developmental dynamics in entire large organisms. When combined with tissue clearing methods, light sheet microscopy furthermore allows rapid imaging of large specimens with excellent coverage and high spatial resolution. Even samples up to the size of entire mammalian brains can be efficiently recorded and quantitatively analyzed. Here, we provide an overview of the history of light sheet microscopy, review the development of tissue clearing methods, and discuss recent technical breakthroughs that have the potential to influence the future direction of the field.