The mammalian heart is derived from multiple cell lineages; however, our understanding of when and how the diverse cardiac cell types arise is limited. We mapped the origin of the embryonic mouse heart at single-cell resolution using a combination of transcriptomic, imaging, and genetic lineage labeling approaches. This provided a transcriptional and anatomic definition of cardiac progenitor types. Furthermore, it revealed a cardiac progenitor pool that is anatomically and transcriptionally distinct from currently known cardiac progenitors. Besides contributing to cardiomyocytes, these cells also represent the earliest progenitor of the epicardium, a source of trophic factors and cells during cardiac development and injury. This study provides detailed insights into the formation of early cardiac cell types, with particular relevance to the development of cell-based cardiac regenerative therapies.

The origin of chordates has been debated for more than a century, with one key issue being the emergence of the notochord. In vertebrates, the notochord develops by convergence and extension of the chordamesoderm, a population of midline cells of unique molecular identity. We identify a population of mesodermal cells in a developing invertebrate, the marine annelid Platynereis dumerilii, that converges and extends toward the midline and expresses a notochord-specific combination of genes. These cells differentiate into a longitudinal muscle, the axochord, that is positioned between central nervous system and axial blood vessel and secretes a strong collagenous extracellular matrix. Ancestral state reconstruction suggests that contractile mesodermal midline cells existed in bilaterian ancestors. We propose that these cells, via vacuolization and stiffening, gave rise to the chordate notochord.

Modern applications in the life sciences are frequently based on in vivo imaging of biological specimens, a domain for which light microscopy approaches are typically best suited. Often, quantitative information must be obtained from large multicellular organisms at the cellular or even subcellular level and with a good temporal resolution. However, this usually requires a combination of conflicting features: high imaging speed, low photobleaching and low phototoxicity in the specimen, good three-dimensional (3D) resolution, an excellent signal-to-noise ratio, and multiple-view imaging capability. The latter feature refers to the capability of recording a specimen along multiple directions, which is crucial for the imaging of large specimens with strong light-scattering or light-absorbing tissue properties. An imaging technique that fulfills these requirements is essential for many key applications: For example, studying fast cellular processes over long periods of time, imaging entire embryos throughout development, or reconstructing the formation of morphological defects in mutants. Here, we discuss digital scanned laser light sheet fluorescence microscopy (DSLM) as a novel tool for quantitative in vivo imaging in the post-genomic era and show how this emerging technique relates to the currently most widely applied 3D microscopy techniques in biology: confocal fluorescence microscopy and two-photon microscopy.

Embryonic development is one of the most complex processes encountered in biology. In vertebrates and higher invertebrates, a single cell transforms into a fully functional organism comprising several tens of thousands of cells, arranged in tissues and organs that perform impressive tasks. In vivo observation of this biological process at high spatiotemporal resolution and over long periods of time is crucial for quantitative developmental biology. Importantly, such recordings must be realized without compromising the physiological development of the specimen. In digital scanned laser light-sheet fluorescence microscopy (DSLM), a specimen is rapidly scanned with a thin sheet of light while fluorescence is recorded perpendicular to the axis of illumination with a camera. Combining light-sheet technology and fast laser scanning, DSLM delivers quantitative data for entire embryos at high spatiotemporal resolution. Compared with confocal and two-photon fluorescence microscopy, DSLM exposes the embryo to at least three orders of magnitude less light energy, but still provides up to 50 times faster imaging speeds and a 10–100-fold higher signal-to-noise ratio. By using automated image processing algorithms, DSLM images of embryogenesis can be converted into a digital representation. These digital embryos permit following cells as a function of time, revealing cell fate as well as cell origin. By means of such analyses, developmental building plans of tissues and organs can be determined in a whole-embryo context. This article presents a sample preparation and imaging protocol for studying the development of whole zebrafish and Drosophila embryos using DSLM.

Calcium signaling has long been associated with key events of immunity, including chemotaxis, phagocytosis, and activation. However, imaging and manipulation of calcium flux in motile immune cells in live animals remain challenging. Using light-sheet microscopy for in vivo calcium imaging in zebrafish, we observe characteristic patterns of calcium flux triggered by distinct events, including phagocytosis of pathogenic bacteria and migration of neutrophils toward inflammatory stimuli. In contrast to findings from ex vivo studies, we observe enriched calcium influx at the leading edge of migrating neutrophils. To directly manipulate calcium dynamics in vivo, we have developed transgenic lines with cell-specific expression of the mammalian TRPV1 channel, enabling ligand-gated, reversible, and spatiotemporal control of calcium influx. We find that controlled calcium influx can function to help define the neutrophil's leading edge. Cell-specific TRPV1 expression may have broad utility for precise control of calcium dynamics in other immune cell types and organisms.

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.

Animal development is a complex and dynamic process orchestrated by exquisitely timed cell lineage commitment, divisions, migration, and morphological changes at the single-cell level. In the past decade, extensive genetic, stem cell, and genomic studies provided crucial insights into molecular underpinnings and the functional importance of genetic pathways governing various cellular differentiation processes. However, it is still largely unknown how the precise coordination of these pathways is achieved at the whole-organism level and how the highly regulated spatiotemporal choreography of development is established in turn. Here, we discuss the latest technological advances in imaging and single-cell genomics that hold great promise for advancing our understanding of this intricate process. We propose an integrated approach that combines such methods to quantitatively decipher in vivo cellular dynamic behaviors and their underlying molecular mechanisms at the systems level with single-cell, single-molecule resolution.

Understanding the diversification of mammalian cell lineages is an essential to embryonic development, organ regeneration and tissue engineering. Shortly after implantation in the uterus, the pluripotent cells of the mammalian epiblast generate the three germ layers: ectoderm, mesoderm and endoderm1. Although clonal analyses suggest early specification of epiblast cells towards particular cell lineages2–4, single-cell transcriptomes do not identify lineage-specific markers in the epiblast5–11 and thus, the molecular regulation of such specification remains unknow. Here, we studied the epigenetic landscape of single epiblast cells, which revealed lineage priming towards endoderm, ectoderm or mesoderm. Unexpectedly, epiblast cells with mesodermal priming show a strong signature for the endothelial/endocardial fate, suggesting early specification of this lineage aside from other mesoderm. Through clonal analysis and live imaging, we show that endothelial precursors show early lineage divergence from the rest of mesodermal derivatives. In particular, cardiomyocytes and endocardial cells show limited lineage relationship, despite being temporally and spatially co-recruited during gastrulation. Furthermore, analysing the live tracks of single cells through unsupervised classification of cell migratory activity, we found early behavioral divergence of endothelial precursors shortly after the onset of mesoderm migration towards the cardiogenic area. These results provide a new model for the phenotypically silent specification of mammalian cell lineages in pluripotent cells of the epiblast and modify current knowledge on the sequence and timing of cardiovascular lineages diversification.

Deleterious mutations inevitably emerge in any evolutionary process and are speculated to decisively influence the structure of the genome. Meiosis, which is thought to play a major role in handling mutations on the population level, recombines chromosomes via non-randomly distributed hot spots for meiotic recombination. In many genomes, various types of genetic elements are distributed in patterns that are currently not well understood. In particular, important (essential) genes are arranged in clusters, which often cannot be explained by a functional relationship of the involved genes. Here we show by computer simulation that essential gene (EG) clustering provides a fitness benefit in handling deleterious mutations in sexual populations with variable levels of inbreeding and outbreeding. We find that recessive lethal mutations enforce a selective pressure towards clustered genome architectures. Our simulations correctly predict (i) the evolution of non-random distributions of meiotic crossovers, (ii) the genome-wide anti-correlation of meiotic crossovers and EG clustering, (iii) the evolution of EG enrichment in pericentromeric regions and (iv) the associated absence of meiotic crossovers (cold centromeres). Our results furthermore predict optimal crossover rates for yeast chromosomes, which match the experimentally determined rates. Using a Saccharomyces cerevisiae conditional mutator strain, we show that haploid lethal phenotypes result predominantly from mutation of single loci and generally do not impair mating, which leads to an accumulation of mutational load following meiosis and mating. We hypothesize that purging of deleterious mutations in essential genes constitutes an important factor driving meiotic crossover. Therefore, the increased robustness of populations to deleterious mutations, which arises from clustered genome architectures, may provide a significant selective force shaping crossover distribution. Our analysis reveals a new aspect of the evolution of genome architectures that complements insights about molecular constraints, such as the interference of pericentromeric crossovers with chromosome segregation.

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.