Towards comprehensive cell lineage reconstructions in complex organisms using light-sheet microscopy

Non-invasive single cell aptasensing in live cells and animals

We report a genetically encoded aptamer biosensor platform for non-invasive measurement of drug distribution in cells and animals. We combined the high specificity of aptamer molecular recognition with the easy-to-detect properties of fluorescent proteins. We generated six encoded aptasensors, showcasing the platform versatility. The biosensors display high sensitivity and specificity for detecting their specific drug target over related analogs. We show dose dependent response of biosensor performance reaching saturating drug uptake levels in individual live cells. We designed our platform for integration into animal genomes; thus, we incorporated aptamer biosensors into zebrafish, an important model vertebrate. The biosensors enabled non-invasive drug biodistribution imaging in whole animals across different timepoints. To our knowledge, this is the first example of an aptamer biosensor-expressing transgenic vertebrate that is carried through generations. As such, our encoded platform addresses the need for non-invasive whole animal biosensing ideal for pharmacokinetic-pharmacodynamic analyses that can be expanded to other organisms and to detect diverse molecules of interest.

Effective mechanical potential of cell-cell interaction explains three-dimensional morphologies during early embryogenesis

Mechanical forces are critical for the emergence of diverse three-dimensional morphologies of multicellular systems. However, it remains unclear what kind of mechanical parameters at cellular level substantially contribute to tissue morphologies. This is largely due to technical limitations of live measurements of cellular forces. Here we developed a framework for inferring and modeling mechanical forces of cell-cell interactions. First, by analogy to coarse-grained models in molecular and colloidal sciences, we approximated cells as particles, where mean forces (i.e. effective forces) of pairwise cell-cell interactions are considered. Then, the forces were statistically inferred by fitting the mathematical model to cell tracking data. This method was validated by using synthetic cell tracking data resembling various in vivo situations. Application of our method to the cells in the early embryos of mice and the nematode Caenorhabditis elegans revealed that cell-cell interaction forces can be written as a pairwise potential energy in a manner dependent on cell-cell distances. Importantly, the profiles of the pairwise potentials were quantitatively different among species and embryonic stages, and the quantitative differences correctly described the differences of their morphological features such as spherical vs. distorted cell aggregates, and tightly vs. non-tightly assembled aggregates. We conclude that the effective pairwise potential of cell-cell interactions is a live measurable parameter whose quantitative differences can be a parameter describing three-dimensional tissue morphologies.

Asymmetry in the frequency and position of mitosis in the mouse embryo epiblast at gastrulation

At gastrulation, a subpopulation of epiblast cells constitutes a transient posteriorly located structure called the primitive streak, where cells that undergo epithelial-mesenchymal transition make up the mesoderm and endoderm lineages. Mouse embryo epiblast cells were labelled ubiquitously or in a mosaic fashion. Cell shape, packing, organization and division were recorded through live imaging during primitive streak formation. Posterior epiblast displays a higher frequency of rosettes, some of which associate with a central cell undergoing mitosis. Cells at the primitive streak, in particular delaminating cells, undergo mitosis more frequently than other epiblast cells. In pseudostratified epithelia, mitosis takes place at the apical side of the epithelium. However, mitosis is not restricted to the apical side of the epiblast, particularly on its posterior side. Non-apical mitosis occurs specifically in the streak even when ectopically located. Posterior non-apical mitosis results in one or two daughter cells leaving the epiblast layer. Cell rearrangement associated with mitotic cell rounding in posterior epiblast, in particular when non-apical, might thus facilitate cell ingression and transition to a mesenchymal phenotype.

Embryo-Based Large Fragment Knock-in in Mammals: Why, How and What's Next

Endonuclease-mediated genome editing technologies, most notably CRISPR/Cas9, have revolutionized animal genetics by allowing for precise genome editing directly through embryo manipulations. As endonuclease-mediated model generation became commonplace, large fragment knock-in remained one of the most challenging types of genetic modification. Due to their unique value in biological and biomedical research, however, a diverse range of technological innovations have been developed to achieve efficient large fragment knock-in in mammalian animal model generation, with a particular focus on mice. Here, we first discuss some examples that illustrate the importance of large fragment knock-in animal models and then detail a subset of the recent technological advancements that have allowed for efficient large fragment knock-in. Finally, we envision the future development of even larger fragment knock-ins performed in even larger animal models, the next step in expanding the potential of large fragment knock-in in animal models.

A developmental perspective on the evolution of the nervous system

The evolution of nervous systems in animals has always fascinated biologists, and thus multiple evolutionary scenarios have been proposed to explain the appearance of neurons and complex neuronal centers. However, the absence of a robust phylogenetic framework for animal interrelationships, the lack of a mechanistic understanding of development, and a recapitulative view of animal ontogeny have traditionally limited these scenarios. Only recently, the integration of advanced molecular and morphological studies in a broad range of animals has allowed to trace the evolution of developmental and neuronal characters on a better-resolved animal phylogeny. This has falsified most traditional scenarios for nervous system evolution, paving the way for the emergence of new testable hypotheses. Here we summarize recent progress in studies of nervous system development in major animal lineages and formulate some of the arising questions. In particular, we focus on how lineage analyses of nervous system development and a comparative study of the expression of neural-related genes has influenced our understanding of the evolution of an elaborated central nervous system in Bilateria. We argue that a phylogeny-guided study of neural development combining thorough descriptive and functional analyses is key to establish more robust scenarios for the origin and evolution of animal nervous systems.

Compressed Blind Deconvolution and Denoising for Complementary Beam Subtraction Light-Sheet Fluorescence Microscopy

OBJECTIVE

The side-lobes of a Bessel beam (BB) create a severe out-of-focus background in scanning light-sheet fluorescence microscopy, thereby extremely limiting the axial resolution. The complementary beam subtraction (CBS) method can significantly reduce the out-of-focus background by double scanning a BB and its complementary beam. However, the blurring and noise caused by the system instability during the double scanning and subtraction operations degrade the image quality significantly. Therefore, we propose a compressed blind deconvolution and denoising (CBDD) method that solves this problem.

METHODS

We use a unified formulation that comprehensively takes advantage of multiple compressed sensing reconstructions and blind sparse representation.

RESULTS

The simulations and experiments were performed using the microbeads and model organisms to verify the effectiveness of the proposed method. Compared with the CBS light-sheet method, the proposed CBDD algorithm achieved the gain improvement in the axial and lateral resolution of about 1.81 and 2.22 times, respectively, while the average signal-to-noise ratio (SNR) was increased by about 3 dB.

CONCLUSION

Accordingly, the proposed method can suppress the noise level, enhance the SNR, and recover the degraded resolution simultaneously.

SIGNIFICANCE

The obtained results demonstrate the proposed CBDD algorithm is well suited to improve the imaging performance of the CBS light-sheet fluorescence microscopy.

Rules and Self-Organizing Properties of Post-embryonic Plant Organ Cell Division Patterns

Plants form new organs with patterned tissue organization throughout their lifespan. It is unknown whether this robust post-embryonic organ formation results from stereotypic dynamic processes, in which the arrangement of cells follows rigid rules. Here, we combine modeling with empirical observations of whole-organ development to identify the principles governing lateral root formation in Arabidopsis. Lateral roots derive from a small pool of founder cells in which some take a dominant role as seen by lineage tracing. The first division of the founders is asymmetric, tightly regulated, and determines the formation of a layered structure. Whereas the pattern of subsequent cell divisions is not stereotypic between different samples, it is characterized by a regular switch in division plane orientation. This switch is also necessary for the appearance of patterned layers as a result of the apical growth of the primordium. Our data suggest that lateral root morphogenesis is based on a limited set of rules. They determine cell growth and division orientation. The organ-level coupling of the cell behavior ensures the emergence of the lateral root's characteristic features. We propose that self-organizing, non-deterministic modes of development account for the robustness of plant organ morphogenesis.

Live imaging of Tribolium castaneum embryonic development using light-sheet-based fluorescence microscopy

Tribolium castaneum has become an important insect model organism for evolutionary developmental biology, genetics and biotechnology. However, few protocols for live fluorescence imaging of Tribolium have been reported, and little image data is available. Here we provide a protocol for recording the development of Tribolium embryos with light-sheet-based fluorescence microscopy. The protocol can be completed in 4-7 d and provides procedural details for: embryo collection, microscope configuration, embryo preparation and mounting, noninvasive live imaging for up to 120 h along multiple directions, retrieval of the live embryo once imaging is completed, and image data processing, for which exemplary data is provided. Stringent quality control criteria for developmental biology studies are also discussed. Light-sheet-based fluorescence microscopy complements existing toolkits used to study Tribolium development, can be adapted to other insect species, and requires no advanced imaging or sample preparation skills.

Application of the DNA-specific stain methyl green in the fluorescent labeling of embryos

Methyl green has long been known as a histological stain with a specific affinity for DNA, although its fluorescent properties have remained unexplored until recently. In this article, we illustrate the method for preparing a methyl green aqueous stock solution, that when diluted can be used as a very convenient fluorescent nuclear label for fixed cells and tissues. Easy procedures to label whole zebrafish and chick embryos are detailed, and examples of images obtained shown. Methyl green is maximally excited by red light, at 633 nm, and emits with a relatively sharp spectrum that peaks at 677 nm. It is very inexpensive, non-toxic, highly stable in solution and very resistant to photobleaching when bound to DNA. Its red emission allows for unaltered high resolution scanning confocal imaging of nuclei in thick specimens. Finally, this methyl green staining protocol is compatible with other cell staining procedures, such as antibody labeling, or actin filaments labeling with fluorophore-conjugated phalloidin.

An ensemble-averaged, cell density-based digital model of zebrafish embryo development derived from light-sheet microscopy data with single-cell resolution

A new era in developmental biology has been ushered in by recent advances in the quantitative imaging of all-cell morphogenesis in living organisms. Here we have developed a light-sheet fluorescence microscopy-based framework with single-cell resolution for identification and characterization of subtle phenotypical changes of millimeter-sized organisms. Such a comparative study requires analyses of entire ensembles to be able to distinguish sample-to-sample variations from definitive phenotypical changes. We present a kinetic digital model of zebrafish embryos up to 16 h of development. The model is based on the precise overlay and averaging of data taken on multiple individuals and describes the cell density and its migration direction at every point in time. Quantitative metrics for multi-sample comparative studies have been introduced to analyze developmental variations within the ensemble. The digital model may serve as a canvas on which the behavior of cellular subpopulations can be studied. As an example, we have investigated cellular rearrangements during germ layer formation at the onset of gastrulation. A comparison of the one-eyed pinhead (oep) mutant with the digital model of the wild-type embryo reveals its abnormal development at the onset of gastrulation, many hours before changes are obvious to the eye.

Mathematically guided approaches to distinguish models of periodic patterning

How periodic patterns are generated is an open question. A number of mechanisms have been proposed--most famously, Turing's reaction-diffusion model. However, many theoretical and experimental studies focus on the Turing mechanism while ignoring other possible mechanisms. Here, we use a general model of periodic patterning to show that different types of mechanism (molecular, cellular, mechanical) can generate qualitatively similar final patterns. Observation of final patterns is therefore not sufficient to favour one mechanism over others. However, we propose that a mathematical approach can help to guide the design of experiments that can distinguish between different mechanisms, and illustrate the potential value of this approach with specific biological examples.

Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences

BACKGROUND

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.

RESULTS

We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks.

CONCLUSIONS

By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

Spatial and temporal dynamics of cell generations within an invasion wave: a link to cell lineage tracing

Mathematical models of a cell invasion wave have included both continuum partial differential equation (PDE) approaches and discrete agent-based cellular automata (CA) approaches. Here we are interested in modelling the spatial and temporal dynamics of the number of divisions (generation number) that cells have undergone by any time point within an invasion wave. In the CA framework this is performed from agent lineage tracings, while in the PDE approach a multi-species generalized Fisher equation is derived for the cell density within each generation. Both paradigms exhibit qualitatively similar cell generation densities that are spatially organized, with agents of low generation number rapidly attaining a steady state (with average generation number increasing linearly with distance) behind the moving wave and with evolving high generation number at the wavefront. This regularity in the generation spatial distributions is in contrast to the highly stochastic nature of the underlying lineage dynamics of the population. In addition, we construct a method for determining the lineage tracings of all agents without labelling and tracking the agents, but through either a knowledge of the spatial distribution of the generations or the number of agents in each generation. This involves determining generation-dependent proliferation probabilities and using these to define a generation-dependent Galton-Watson (GDGW) process. Monte-Carlo simulations of the GDGW process are used to determine the individual lineage tracings. The lineages of the GDGW process are analyzed using Lorenz curves and found to be similar to outcomes generated by direct lineage tracing in CA realizations. This analysis provides the basis for a potentially useful technique for deducing cell lineage data when imaging every cell is not feasible.

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.

A fast, low cost, and highly efficient fluorescent DNA labeling method using methyl green

The increasing need for multiple-labeling of cells and whole organisms for fluorescence microscopy has led to the development of hundreds of fluorophores that either directly recognize target molecules or organelles, or are attached to antibodies or other molecular probes. DNA labeling is essential to study nuclear-chromosomal structure, as well as for gel staining, but also as a usual counterstain in immunofluorescence, FISH or cytometry. However, there are currently few reliable red to far-red-emitting DNA stains that can be used. We describe herein an extremely simple, inexpensive and robust method for DNA labeling of cells and electrophoretic gels using the very well-known histological stain methyl green (MG). MG used in very low concentrations at physiological pH proved to have relatively narrow excitation and emission spectra, with peaks at 633 and 677 nm, respectively, and a very high resistance to photobleaching. It can be used in combination with other common DNA stains or antibodies without any visible interference or bleed-through. In electrophoretic gels, MG also labeled DNA in a similar way to ethidium bromide, but, as expected, it did not label RNA. Moreover, we show here that MG fluorescence can be used as a stain for direct measuring of viability by both microscopy and flow cytometry, with full correlation to ethidium bromide staining. MG is thus a very convenient alternative to currently used red-emitting DNA stains.

Cell lineage tracing in the developing enteric nervous system: superstars revealed by experiment and simulation

Cell lineage tracing is a powerful tool for understanding how proliferation and differentiation of individual cells contribute to population behaviour. In the developing enteric nervous system (ENS), enteric neural crest (ENC) cells move and undergo massive population expansion by cell division within self-growing mesenchymal tissue. We show that single ENC cells labelled to follow clonality in the intestine reveal extraordinary and unpredictable variation in number and position of descendant cells, even though ENS development is highly predictable at the population level. We use an agent-based model to simulate ENC colonization and obtain agent lineage tracing data, which we analyse using econometric data analysis tools. In all realizations, a small proportion of identical initial agents accounts for a substantial proportion of the total final agent population. We term these individuals superstars. Their existence is consistent across individual realizations and is robust to changes in model parameters. This inequality of outcome is amplified at elevated proliferation rate. The experiments and model suggest that stochastic competition for resources is an important concept when understanding biological processes which feature high levels of cell proliferation. The results have implications for cell-fate processes in the ENS.