Multispectral fingerprinting for improved in vivo cell dynamics analysis

Background: Tracing cell dynamics in the embryo becomes tremendously difficult when cell trajectories cross in space and time and tissue density obscure individual cell borders. Here, we used the chick neural crest (NC) as a model to test multicolor cell labeling and multispectral confocal imaging strategies to overcome these roadblocks. Results: We found that multicolor nuclear cell labeling and multispectral imaging led to improved resolution of in vivo NC cell identification by providing a unique spectral identity for each cell. NC cell spectral identity allowed for more accurate cell tracking and was consistent during short term time-lapse imaging sessions. Computer model simulations predicted significantly better object counting for increasing cell densities in 3-color compared to 1-color nuclear cell labeling. To better resolve cell contacts, we show that a combination of 2-color membrane and 1-color nuclear cell labeling dramatically improved the semi-automated analysis of NC cell interactions, yet preserved the ability to track cell movements. We also found channel versus lambda scanning of multicolor labeled embryos significantly reduced the time and effort of image acquisition and analysis of large 3D volume data sets. Conclusions: Our results reveal that multicolor cell labeling and multispectral imaging provide a cellular fingerprint that may uniquely determine a cell's position within the embryo. Together, these methods offer a spectral toolbox to resolve in vivo cell dynamics in unprecedented detail

Learning to Detect and Track Cells for Quantitative Analysis of Time-Lapse Microscopic Image Sequences

© 2015 IEEE.Studying the behaviour of cells using time-lapse microscopic imaging requires automated processing pipelines that enable quantitative analysis of a large number of cells. We propose a pipeline based on state-of-the-art methods for background motion compensation, cell detection, and tracking which are integrated into a novel semi-automated, learning based analysis tool. Motion compensation is performed by employing an efficient nonlinear registration method based on powerful discrete graph optimisation. Robust detection and tracking of cells is based on classifier learning which only requires a small number of manual annotations. Cell motion trajectories are generated using a recent global data association method and linear programming. Our approach is robust to the presence of significant motion and imaging artifacts. Promising results are presented on different sets of in-vivo fluorescent microscopic image sequences

Advanced optical imaging in living embryos

Developmental biology investigations have evolved from static studies of embryo anatomy and into dynamic studies of the genetic and cellular mechanisms responsible for shaping the embryo anatomy. With the advancement of fluorescent protein fusions, the ability to visualize and comprehend how thousands to millions of cells interact with one another to form tissues and organs in three dimensions (xyz) over time (t) is just beginning to be realized and exploited. In this review, we explore recent advances utilizing confocal and multi-photon time-lapse microscopy to capture gene expression, cell behavior, and embryo development. From choosing the appropriate fluorophore, to labeling strategy, to experimental set-up, and data pipeline handling, this review covers the various aspects related to acquiring and analyzing multi-dimensional data sets. These innovative techniques in multi-dimensional imaging and analysis can be applied across a number of fields in time and space including protein dynamics to cell biology to morphogenesis

Evaluation of Dynamic Cell Processes and Behavior Using Video Bioinformatics Tools

Just as body language can reveal a person’s state of well-being, dynamic changes in cell behavior and morphology can be used to monitor processes in cultured cells. This chapter discusses how CL-Quant software, a commercially available video bioinformatics tool, can be used to extract quantitative data on: (1) growth/proliferation, (2) cell and colony migration, (3) reactive oxygen species (ROS) production, and (4) neural differentiation. Protocols created using CL-Quant were used to analyze both single cells and colonies. Time-lapse experiments in which different cell types were subjected to various chemical exposures were done using Nikon BioStations. Proliferation rate was measured in human embryonic stem cell colonies by quantifying colony area (pixels) and in single cells by measuring confluency (pixels). Colony and single cell migration were studied by measuring total displacement (distance between the starting and ending points) and total distance traveled by the colonies/cells. To quantify ROS production, cells were pre-loaded with MitoSOX Red™, a mitochondrial ROS (superoxide) indicator, treated with various chemicals, then total intensity of the red fluorescence was measured in each frame. Lastly, neural stem cells were incubated in differentiation medium for 12 days, and time lapse images were collected daily. Differentiation of neural stem cells was quantified using a protocol that detects young neurons. CLQuant software can be used to evaluate biological processes in living cells, and the protocols developed in this project can be applied to basic research and toxicological studies, or to monitor quality control in culture facilities

Live imaging of whole mouse embryos during gastrulation : migration analyses of epiblast and mesodermal cells

During gastrulation in the mouse embryo, dynamic cell movements including epiblast invagination and mesodermal layer expansion lead to the establishment of the three-layered body plan. The precise details of these movements, however, are sometimes elusive, because of the limitations in live imaging. To overcome this problem, we developed techniques to enable observation of living mouse embryos with digital scanned light sheet microscope (DSLM). The achieved deep and high time-resolution images of GFP-expressing nuclei and following 3D tracking analysis revealed the following findings: (i) Interkinetic nuclear migration (INM) occurs in the epiblast at embryonic day (E)6 and 6.5. (ii) INM-like migration occurs in the E5.5 embryo, when the epiblast is a monolayer and not yet pseudostratified. (iii) Primary driving force for INM at E6.5 is not pressure from neighboring nuclei. (iv) Mesodermal cells migrate not as a sheet but as individual cells without coordination

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application

Myxococcus xanthus gliding motors are elastically coupled to the substrate as predicted by the focal adhesion model of gliding motility

Myxococcus xanthus is a model organism for studying bacterial social behaviors due to its ability to form complex multi-cellular structures. Knowledge of M. xanthus surface gliding motility and the mechanisms that coordinate it are critically important to our understanding of collective cell behaviors. Although the mechanism of gliding motility is still under investigation, recent experiments suggest that there are two possible mechanisms underlying force production for cell motility: the focal adhesion mechanism and the helical rotor mechanism which differ in the biophysics of the cell-substrate interactions. Whereas the focal adhesion model predicts an elastic coupling, the helical rotor model predicts a viscous coupling. Using a combination of computational modeling, imaging, and force microscopy, we find evidence for elastic coupling in support of the focal adhesion model. Using a biophysical model of the M. xanthus cell, we investigated how the mechanical interactions between cells are affected by interactions with the substrate. Comparison of modeling results with experimental data for cell-cell collision events pointed to a strong, elastic attachment between the cell and substrate. These results are robust to variations in the mechanical and geometrical parameters of the model. We then directly measured the motor-substrate coupling by monitoring the motion of optically trapped beads and find that motor velocity decreases exponentially with opposing load. At high loads, motor velocity approaches zero velocity asymptotically and motors remain bound to beads indicating a strong, elastic attachment

Automated single-cell motility analysis on a chip using lensfree microscopy.

Quantitative cell motility studies are necessary for understanding biophysical processes, developing models for cell locomotion and for drug discovery. Such studies are typically performed by controlling environmental conditions around a lens-based microscope, requiring costly instruments while still remaining limited in field-of-view. Here we present a compact cell monitoring platform utilizing a wide-field (24 mm(2)) lensless holographic microscope that enables automated single-cell tracking of large populations that is compatible with a standard laboratory incubator. We used this platform to track NIH 3T3 cells on polyacrylamide gels over 20 hrs. We report that, over an order of magnitude of stiffness values, collagen IV surfaces lead to enhanced motility compared to fibronectin, in agreement with biological uses of these structural proteins. The increased throughput associated with lensfree on-chip imaging enables higher statistical significance in observed cell behavior and may facilitate rapid screening of drugs and genes that affect cell motility