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

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

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

Methods for Spatio-Temporal Analysis of Embryo Cleavage In Vitro

Automated or semiautomated time-lapse analysis of early stage embryo images during the cleavage stage can give insight into the timing of mitosis, regularity of both division timing and pattern, as well as cell lineage. Simultaneous monitoring of molecular processes enables the study of connections between genetic expression and cell physiology and development. The study of live embryos poses not only new requirements on the hardware and embryo-holding equipment but also indirectly on analytical software and data analysis as four-dimensional video sequencing of embryos easily creates high quantities of data. The ability to continuously film and automatically analyze growing embryos gives new insights into temporal embryo development by studying morphokinetics as well as morphology. Until recently, this was not possible unless by a tedious manual process. In recent years, several methods have been developed that enable this dynamic monitoring of live embryos. Here we describe three methods with variations in hardware and software analysis and give examples of the outcomes. Together, these methods open a window to new information in developmental embryology, as embryo division pattern and lineage are studied in vivo

Cell sorting in a Petri dish controlled by computer vision.

Fluorescence-activated cell sorting (FACS) applying flow cytometry to separate cells on a molecular basis is a widespread method. We demonstrate that both fluorescent and unlabeled live cells in a Petri dish observed with a microscope can be automatically recognized by computer vision and picked up by a computer-controlled micropipette. This method can be routinely applied as a FACS down to the single cell level with a very high selectivity. Sorting resolution, i.e., the minimum distance between two cells from which one could be selectively removed was 50-70 micrometers. Survival rate with a low number of 3T3 mouse fibroblasts and NE-4C neuroectodermal mouse stem cells was 66 +/- 12% and 88 +/- 16%, respectively. Purity of sorted cultures and rate of survival using NE-4C/NE-GFP-4C co-cultures were 95 +/- 2% and 62 +/- 7%, respectively. Hydrodynamic simulations confirmed the experimental sorting efficiency and a cell damage risk similar to that of normal FACS

Accurate cell segmentation in microscopy images using membrane patterns

Motivation: Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology via optical microscopy. Although a plethora of segmentation methods exists, accurate segmentation is challenging and usually requires problem-specific tailoring of algorithms. In addition, most current segmentation algorithms rely on a few basic approaches that use the gradient field of the image to detect cell boundaries. However, many microscopy protocols can generate images with characteristic intensity profiles at the cell membrane. This has not yet been algorithmically exploited to establish more general segmentation methods. Results: We present an automatic cell segmentation method that decodes the information across the cell membrane and guarantees optimal detection of the cell boundaries on a per-cell basis. Graph cuts account for the information of the cell boundaries through directional cross-correlations, and they automatically incorporate spatial constraints. The method accurately segments images of various cell types grown in dense cultures that are acquired with different microscopy techniques. In quantitative benchmarks and comparisons with established methods on synthetic and real images, we demonstrate significantly improved segmentation performance despite cell-shape irregularity, cell-to-cell variability and image noise. As a proof of concept, we monitor the internalization of green fluorescent protein-tagged plasma membrane transporters in single yeast cells. Availability and implementation: Matlab code and examples are available at http://www.csb.ethz.ch/tools/cellSegmPackage.zip. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

3D imaging and quantitative analysis of intact tissues and organs

Embryonic development and tumor growth are highly complex and dynamic processes that exist in both time and space. To fully understand the molecular mechanisms that control these processes, it is crucial to study RNA expression and protein translation with single-cell spatiotemporal resolution. This is feasible by microscopic imaging that enables multidimensional assessments of cells, tissues, and organs. Here, a time-lapse calcium imaging and three-dimensional imaging was used to study physiological development of the brain or pathological development of cancer, respectively. In Paper I, spatiotemporal calcium imaging revealed a new mechanism of neurogenesis during brain development. In Paper II, a new clearing method of clinically stored specimens, DIPCO (diagnosing immunolabeled paraffin-embedded cleared organs), was developed that allows better characterization and staging of intact human tumors. In Paper III, the DIPCO method was applied to determine tumor stage and characterize the microlymphatic system in bladder cancer. In Paper IV, a novel method for RNA labeling of volumetric specimens, DIIFCO (diagnosing in situ and immunofluorescence-labeled cleared onco-sample) was developed to study RNAs expression and localization in intact tumors. Overall, the aim of the thesis was to demonstrate that multidimensional imaging extends the understanding of both physiological and pathological biological developmental processes

Quantification of the morphological characteristics of hESC colonies

The maintenance of the undifferentiated state in human embryonic stem cells (hESCs) is critical for further application in regenerative medicine, drug testing and studies of fundamental biology. Currently, the selection of the best quality cells and colonies for propagation is typically performed by eye, in terms of the displayed morphological features, such as prominent/abundant nucleoli and a colony with a tightly packed appearance and a well-defined edge. Using image analysis and computational tools, we precisely quantify these properties using phase-contrast images of hESC colonies of different sizes (0.1–1.1 mm2) during days 2, 3 and 4 after plating. Our analyses reveal noticeable differences in their structure influenced directly by the colony area A. Large colonies (A > 0.6 mm2) have cells with smaller nuclei and a short intercellular distance when compared with small colonies (A  0.6 mm2) due to the proliferation of the cells in the bulk. This increases the colony density and the number of nearest neighbours. We also detect the self-organisation of cells in the colonies where newly divided (smallest) cells cluster together in patches, separated from larger cells at the final stages of the cell cycle. This might influence directly cell-to-cell interactions and the community effects within the colonies since the segregation induced by size differences allows the interchange of neighbours as the cells proliferate and the colony grows. Our findings are relevant to efforts to determine the quality of hESC colonies and establish colony characteristics database

Differentiating Human Embryonic Stem Cells in Micropatterns to Study Cell Fate Specification and Morphogenetic Events During Gastrulation

During mammalian embryogenesis, the first major lineage segregation occurs when embryonic epiblast, and extraembryonic trophectoderm and hypoblast arise in the blastocyst. In the next fundamental and conserved phase of animal embryogenesis known as gastrulation, extraembryonic cells provide signals to epiblast to instruct embryonic patterning, and epiblast gives rise to germ layers ectoderm, mesoderm, and endoderm, that will establish all embryonic tissues. Proper specification and morphogenesis of germ layers during gastrulation is vital for correct embryonic development. Due to ethical and legal restrictions limiting human embryo studies, human gastrulation is poorly understood. Our knowledge of human gastrulation has largely been derived from studies in model organisms, including mouse and more recently, cynomolgus monkey. However, interspecies differences underscore the need for alternative human gastrulation models. In this regard, human and mouse embryonic stem cells have been shown to recapitulate aspects of in vivo gastrulation including germ layer specification, and internalization and elongation morphogenesis. These in vitro systems represent powerful models of gastrulation due to the ease of genetic manipulations and the ability to finely control experimental factors. Human embryonic stem cells, treated with BMP4 for 44 hours in spatially confined micro-discs of extracellular matrix, have been shown to differentiate into 2D micro-colonies termed gastruloids. These gastruloids display highly reproducible differentiation of germ layers and extraembryonic cell types in a radial arrangement. We used combinatorial single-cell RNA sequencing and immunofluorescence imaging to characterize these BMP4-treated 2D gastruloids, and showed the formation in gastruloids of seven cell types, including epiblast, prospective ectoderm, two populations of mesoderm, and endoderm, as well as previously undescribed cell types in 2D gastruloids, primordial germ cell-like cells, and extraembryonic cells that are transcriptionally similar to trophectoderm and amnion. Comparative transcriptomic analyses with human, mouse, and cynomolgus monkey gastrulae support the notion that 2D gastruloid differentiation recapitulates formation of cell types relevant to and models an early-mid stage of in vivo gastrulation. Time course scRNA-seq and immunofluorescence analyses of 2D gastruloid differentiation after 12, 24, and 44 hours of BMP4 treatment showed that germ layer emergence in gastruloids follows the temporal sequence of in vivo gastrulation, with epiblast and ectoderm precursors forming at 12 hour, mesendoderm precursors arising from epiblast at 24 hour to give rise to nascent mesoderm and endoderm at 44 hour, when primordial germ cell-like cells also form. Comparison with human gastrula also showed similarity in transcriptomes and differentiation trajectories of gastruloid cells to their in vivo counterparts. Dynamic changes in transcripts encoding components of key signaling pathways support a BMP, WNT and Nodal hierarchy underlying germ layer specification conserved across mammals, with FGF and HIPPO signaling being active throughout the time course of 2D micropattern gastruloid differentiation. To probe morphogenetic properties of gastruloid cells, differentiated gastruloids treated with BMP4 for 44 hours were dissociated and re-seeded onto extracellular matrix micro-discs. The reseeded cells were highly motile and tended to form aggregates with the same but segregate from or mix with distinct cell types, supporting that 2D gastruloid system exhibits evolutionarily conserved sorting behaviors. In particular, ectodermal cells segregated from endodermal and extraembryonic cells but mixed with mesodermal cells. These results demonstrate that 2D gastruloid system models specification of germ layers and extraembryonic cell types, temporal order and differentiation trajectories of germ layer emergence, and signaling interactions found in early-mid in vivo gastrulation. Dissociated and reseeded gastruloid cells also exhibit conserved cell sorting behaviors. Lastly, this work provides a resource for mining genes and pathways expressed in a stereotyped 2D gastruloid model, common with other species or unique to human gastrulation