A system to analyze the initiation of random X-chromosome inactivation using time-lapse imaging of single cells.

In female eutherian mammal development, X-chromosome inactivation (XCI) of one of the two X chromosomes is initiated early. Understanding the relationship between the initiation of XCI and cell fate is critical for understanding early female development and requires a system that can monitor XCI in single living cells. Traditional embryonic stem cells (ESCs) used for XCI studies often lose X chromosomes spontaneously during culture and differentiation, making accurate monitoring difficult. Additionally, most XCI assessment methods necessitate cell disruption, hindering cell fate tracking. We developed the Momiji (version 2) ESC line to address these difficulties, enabling real-time monitoring of X-chromosome activity via fluorescence. We inserted green and red fluorescent reporter genes and neomycin and puromycin resistance genes into the two X chromosomes of PGK12.1 ESCs, creating a female ESC line that retains two X chromosomes more faithfully during differentiation. Momiji (version 2) ESCs exhibit a more stable XX karyotype than other ESC lines, including the parental PGK12.1 line. This new tool offers valuable insights into the relationship between XCI and cell fate, improving our understanding of early female development.

Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging.

Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver operating characteristic curve of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists' manual scores. While not a replacement for preimplantation genetic testing for aneuploidy, BELA exemplifies how such models can streamline the embryo evaluation process.

Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?

Purpose: With the rapid advancement of time-lapse culture and artificial intelligence (AI) technologies for embryo screening, pregnancy rates in assisted reproductive technology (ART) have significantly improved. However, clinical pregnancy rates in fresh cycles remain dependent on the number and type of embryos transferred. The selection of embryos with the highest implantation potential is critical for embryologists and influences transfer strategies in fertility centers. The superiority of AI over traditional morphological scoring for ranking cleavage-stage embryos based on their implantation potential remains controversial. Methods: This retrospective study analyzed 105 fresh embryo transfer cycles at the Centre for Reproductive Medicine from August 2023 to March 2024, following IVF/ICSI treatment at the cleavage stage. All embryos were cultured using time-lapse technology and scored using an automated AI model (iDAScore V2.0). Embryos were categorized into three groups based on the iDAScore V2.0: Group A (8 cells, iDA: 1.0-5.7); Group B (8 cells, iDA: 5.8-8.0); and Group C (>8 cells, iDA: 5.8-8.0). Clinical treatment outcomes, embryonic development, and pregnancy outcomes were analyzed and compared across the groups. Results: Baseline characteristics such as patient age, AMH levels, AFC, and basal sex hormones showed no significant differences among the three groups (p > 0.05). The iDAscores were significantly higher in Group C (7.3 ± 0.5) compared to Group B (6.7 ± 0.5) and the iDAscores were significantly higher in Group B (6.7 ± 0.5) compared to Group A (4.8 ± 1.0) (p < 0.001).The mean number of high-quality embryos was highest in Group C (4.7 ± 3.0), followed by Group B (3.6 ± 1.7) and Group A (2.1 ± 1.2) (p < 0.001). There was no statistical difference (p = 0.392) in the ongoing pregnancy rate for single cleavage-stage transfers between Group B (54.5%, 30/55) and Group A (38.1%, 8/21), although there was a tendency for Group B to be higher. Conclusion: Combining time-lapse culture with AI scoring may enhance ongoing pregnancy rates in single cleavage-stage fresh transfer cycles.

Visualizing metabolic regulation using metabolic biosensors during sea urchin embryogenesis.

Growing evidence suggests that metabolic regulation directly influences cellular function and development and thus may be more dynamic than previously expected. In vivo and in real-time analysis of metabolite activities during development is crucial to test this idea directly. In this study, we employ two metabolic biosensors to track the dynamics of pyruvate and oxidative phosphorylation (Oxphos) during the early embryogenesis of the sea urchin. A pyruvate sensor, PyronicSF, shows the signal enrichment on the mitotic apparatus, which is consistent with the localization patterns of the corresponding enzyme, pyruvate kinase (PKM). The addition of pyruvate increases the PyronicSF signal, while PKM knockdown decreases its signal, responding to the pyruvate level in the cell. Similarly, a ratio-metric sensor, Grx-roGFP, that reads the redox potential of the cell responds to DTT and H2O2, the known reducer and inducer of Oxphos. These observations suggest that these metabolic biosensors faithfully reflect the metabolic status in the cell during embryogenesis. The time-lapse imaging of these biosensors suggests that pyruvate and Oxphos levels change both spatially and temporarily during embryonic development. Pyruvate level is increased first in micromeres compared to other blastomeres at the 16-cell stage and remains high in ectoderm while decreasing in endomesoderm during gastrulation. In contrast, the Oxphos signal first decreases in micromeres at the 16-cell stage, while it increases in the endomesoderm during gastrulation, showing the opposite trend of the pyruvate signal. These results suggest that metabolic regulation is indeed both temporally and spatially dynamic during embryogenesis, and these biosensors are a valuable tool to monitor metabolic activities in real-time in developing embryos.

Deep learning large-scale drug discovery and repurposing.

Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing.

Ex Vivo Live Imaging in Brain Slices for Analysis of Neurite Formation in Combination with In Utero Electroporation.

The use of time-lapse live imaging enables us to track the dynamic changes in neurites during their formation. Ex vivo live imaging with acute brain slices provides a more physiological environment than cultured cells. To accomplish this, a certain method of labeling is necessary to visualize and identify neurite morphology. To understand the dynamics of neurite structure at early stages of neurite formation, we describe in this chapter ex vivo live imaging using a confocal microscope at P0 in combination with in utero electroporation (IUE).

Measuring Retrograde Actin Flow in Neuronal Growth Cones.

Actin flow refers to the motion of the F-actin cytoskeleton and has been observed in many different cell types, especially in motile cells including neuronal growth cones. The direction of the actin flow is generally retrograde from the periphery toward the center of the cell. Actin flow can be harnessed for forward movement of the cell through substrate-cytoskeletal coupling; thus, a key function of actin flow is in cell locomotion. In this chapter, we illustrate three different methods of quantifying retrograde F-actin flow in growth cones derived from cultured Aplysia bag cell neurons. These methods include tracking the movement of surface marker beads as well as kymograph analysis of time-lapse sequences acquired by differential interference contrast (DIC) imaging or fluorescent speckle microscopy (FSM). Due to their large size, Aplysia neuronal growth cones are uniquely suited for these methods; however, they can also be applied to any other growth cones with clear F-actin-rich peripheral domains.

A Live Imaging Assay for Regenerating Peripheral Neurons.

The cell intrinsic mechanisms directing peripheral nerve regeneration have remained largely understudied, thus limiting our understanding of these processes and constraining the advancement of novel clinical therapeutics. The use of primary adult rat dorsal root ganglion (DRG) neurons cultured in vitro is well established. Despite this, these cells can be challenging to culture and have so far not been amenable to robust transfection or live-cell imaging. The ability to transfect these cells with fluorescent plasmid constructs to label subcellular structures, combined with high resolution time-lapse imaging has the potential to provide invaluable insight into how peripheral neurons coordinate their regenerative response, and which specific cellular structures are involved in this process. Here we describe a protocol that facilitates transfection and subsequent live-imaging of adult rat DRG neurons.

Analysis of Microtubule Polymerization During Axon Outgrowth Using Fluorescently Labeled Microtubule Plus-End Tracking Proteins.

The study of microtubules arrangements and dynamics during axon outgrowth and pathfinding has gained scientific interest during the last decade, and numerous technical resources for its visualization and analysis have been implemented. In this chapter, we describe the cell culture protocols of embryonic cortical and retinal neurons, the methods for transfecting them with fluorescent reporters of microtubule polymerization, and the procedures for time-lapse imaging and quantification in order to study microtubule dynamics during axon morphogenesis.

Chemotaxis Assay of Bone Marrow-Derived Macrophages.

Immune responses rely on efficient and coordinated migration of immune cells to the site of infection or injury. To reach the site of immunological threat often requires long-range navigation of immune cells through complex tissue and vascular networks. Chemotaxis, cell migration steered by gradients of cell-attractive chemicals that bind sensory receptors, is central to this response. Chemoattractant receptors mostly belong to the G-protein-coupled receptor (GPCR) family, but the way attractant-receptor signaling directs cell migration is not fully understood. Direct-viewing chemotaxis chambers combined with time-lapse microscopy give a powerful tool to study the dynamic details of cells' responses to different attractant landscapes. Here, we describe the application of one such chamber (the Dunn chamber) to study bone marrow-derived macrophage chemotaxis to gradients of complement C5a.

A Hands-on Guide to AmoePy - a Python-Based Software Package to Analyze Cell Migration Data.

Amoeboid cell motility is fundamental for a multitude of biological processes such as embryogenesis, immune responses, wound healing, and cancer metastasis. It is characterized by specific cell shape changes: the extension and retraction of membrane protrusions, known as pseudopodia. A common approach to investigate the mechanisms underlying this type of cell motility is to study phenotypic differences in the locomotion of mutant cell lines. To characterize such differences, methods are required to quantify the contour dynamics of migrating cells. AmoePy is a Python-based software package that provides tools for cell segmentation, contour detection as well as analyzing and simulating contour dynamics. First, a digital representation of the cell contour as a chain of nodes is extracted from each frame of a time-lapse microscopy recording of a moving cell. Then, the dynamics of these nodes-referred to as virtual markers-are tracked as the cell contour evolves over time. From these data, various quantities can be calculated that characterize the contour dynamics, such as the displacement of the virtual markers or the local stretching rate of the marker chain. Their dynamics is typically visualized in space-time plots, the so-called kymographs, where the temporal evolution is displayed for the different locations along the cell contour. Using AmoePy, you can straightforwardly create kymograph plots and videos from stacks of experimental bright-field or fluorescent images of motile cells. A hands-on guide on how to install and use AmoePy is provided in this chapter.

Quantifying Protein Dynamics by Kymograph Analysis.

Time-lapse imaging of the subcellular localization and dynamic behavior of proteins is critical to understand their biological functions in cells. With the advent of various methodologies and computational tools, the precise tracking and quantification of protein spatiotemporal dynamics have become feasible. Kymograph analysis, in particular, has been extensively adopted for the quantitative assessment of proteins, vesicles, and organelle movements. However, conventional kymograph analysis, which is based on a single linear trajectory, may not comprehensively capture the complexity of proteins that alter their course during intracellular transport and activity. In this chapter, we introduced an advanced protocol for whole-cell kymograph analysis that allows for three-dimensional (3D) tracking of protein dynamics. This method was validated through the analysis of tip-focused endocytosis and exocytosis processes in growing tobacco pollen tubes by employing both the advanced whole-cell and classical kymograph methods. In addition, we enhanced this method by integrating pseudo-colored kymographs that enables the direct visualization of changes in protein fluorescence intensity with fluorescence recovery after photobleaching to advance our understanding of protein localization and dynamics. This comprehensive method offers a novel insight into the intricate dynamics of protein activity within the cellular context.

Microscopy Analysis of Autophagosomal Structures in Plant Cells.

Macroautophagy, hereafter autophagy, plays a crucial role in the degradation of harmful or unwanted cellular components through a double-membrane autophagosome. Upon autophagosome fusion with the vacuole, the degraded materials are subsequently recycled to generate macromolecules, contributing to cellular homeostasis, metabolism, and stress tolerance in plants. A hallmark during autophagy is the formation of isolation membrane structure named as phagophore, which undergoes multiple steps to become as a complete double-membrane autophagosome. Methodologies have been developed in recent years to observe and quantify the autophagic process, which greatly advance knowledge of autophagosome biogenesis in plant cells. In this chapter, we will introduce two methods to dissect the autophagosome-related structures in the Arabidopsis plant cells, including the correlative light and electron microscopy, to map the ultrastructural feature of autophagosomal structures, and time-lapse imaging to monitor the temporal recruitment of autophagy machinery during autophagosome formation.

Visualization and Quantification of Rapid Chromosome Movements at Early Stages of Mouse Meiosis.

Telomere-led rapid chromosome movements (RPMs) are a conserved characteristic of chromosome dynamics in meiosis. RPMs have been suggested to influence critical meiotic functions such as DNA repair and the association of the homologous chromosomes. Here, we describe a method using 3D time-lapse fluorescence imaging to monitor RPMs in Hoechst-stained mouse seminiferous tubules explants. We supplement visualization with customized quantitative motion analysis and in silico simulation. The ability to carry out live imaging, combined with quantitative image analysis, offers a sensitive tool to investigate the regulation of RPMs, chromosome reorganizations that precede dynamic mid-prophase events, and their contribution to faithful transmission of genetic information.

Exploring the principles of embryonic mammary gland branching morphogenesis.

Branching morphogenesis is a characteristic feature of many essential organs, such as the lung and kidney, and most glands, and is the net result of two tissue behaviors: branch point initiation and elongation. Each branched organ has a distinct architecture customized to its physiological function, but how patterning occurs in these ramified tubular structures is a fundamental problem of development. Here, we use quantitative 3D morphometrics, time-lapse imaging, manipulation of ex vivo cultured mouse embryonic organs and mice deficient in the planar cell polarity component Vangl2 to address this question in the developing mammary gland. Our results show that the embryonic epithelial trees are highly complex in topology owing to the flexible use of two distinct modes of branch point initiation: lateral branching and tip bifurcation. This non-stereotypy was contrasted by the remarkably constant average branch frequency, indicating a ductal growth invariant, yet stochastic, propensity to branch. The probability of branching was malleable and could be tuned by manipulating the Fgf10 and Tgfß1 pathways. Finally, our in vivo data and ex vivo time-lapse imaging suggest the involvement of tissue rearrangements in mammary branch elongation.

Dynamics of Replication-Associated Protein Levels through the Cell Cycle.

The measurement of dynamic changes in protein level and localization throughout the cell cycle is of major relevance to studies of cellular processes tightly coordinated with the cycle, such as replication, transcription, DNA repair, and checkpoint control. Currently available methods include biochemical assays of cells in bulk following synchronization, which determine protein levels with poor temporal and no spatial resolution. Taking advantage of genetic engineering and live-cell microscopy, we performed time-lapse imaging of cells expressing fluorescently tagged proteins under the control of their endogenous regulatory elements in order to follow their levels throughout the cell cycle. We effectively discern between cell cycle phases and S subphases based on fluorescence intensity and distribution of co-expressed proliferating cell nuclear antigen (PCNA)-mCherry. This allowed us to precisely determine and compare the levels and distribution of multiple replication-associated factors, including Rap1-interacting factor 1 (RIF1), minichromosome maintenance complex component 6 (MCM6), origin recognition complex subunit 1 (ORC1, and Claspin, with high spatiotemporal resolution in HeLa Kyoto cells. Combining these data with available mass spectrometry-based measurements of protein concentrations reveals the changes in the concentration of these proteins throughout the cell cycle. Our approach provides a practical basis for a detailed interrogation of protein dynamics in the context of the cell cycle.

da_Tracker: Automated workflow for high throughput single cell and single phagosome tracking in infected cells.

Time-lapse microscopy has emerged as a crucial tool in cell biology, facilitating a deeper understanding of dynamic cellular processes. While existing tracking tools have proven effective in detecting and monitoring objects over time, the quantification of signals within these tracked objects often faces implementation constraints. In the context of infectious diseases, the quantification of signals at localized compartments within the cell and around intracellular pathogens can provide even deeper insight into the interactions between the pathogen and host cell organelles. Existing quantitative analysis at a single-phagosome level remains limited and dependent on manual tracking methods. We developed a near-fully automated workflow that performs with limited bias, high-throughput cell segmentation and quantitative tracking of both single cell and single bacterium/phagosome within multi-channel, z-stack, time-lapse confocal microscopy videos. We took advantage of the PyImageJ library to bring Fiji functionality into a Python environment and combined deep-learning-based segmentation from Cellpose with tracking algorithms from Trackmate. The 'da_tracker' workflow provides a versatile toolkit of functions for measuring relevant signal parameters at the single-cell level (such as velocity or bacterial burden) and at the single-phagosome level (i.e. assessment of phagosome maturation over time). Its capabilities in both single-cell and single-phagosome quantification, its flexibility and open-source nature should assist studies that aim to decipher for example the pathogenicity of bacteria and the mechanism of virulence factors that could pave the way for the development of innovative therapeutic approaches.

PostFocus: automated selective post-acquisition high-throughput focus restoration using diffusion model for label-free time-lapse microscopy.

MOTIVATION: High-throughput time-lapse imaging is a fundamental tool for efficient living cell profiling at single-cell resolution. Label-free phase-contrast video microscopy enables noninvasive, nontoxic, and long-term imaging. The tradeoff between speed and throughput, however, implies that despite the state-of-the-art autofocusing algorithms, out-of-focus cells are unavoidable due to the migratory nature of immune cells (velocities >10 µm/min). Here, we propose PostFocus to (i) identify out-of-focus images within time-lapse sequences with a classifier, and (ii) deploy a de-noising diffusion probabilistic model to yield reliable in-focus images. RESULTS: De-noising diffusion probabilistic model outperformed deep discriminative models with a superior performance on the whole image and around cell boundaries. In addition, PostFocus improves the accuracy of image analysis (cell and contact detection) and the yield of usable videos. AVAILABILITY AND IMPLEMENTATION: Open-source code and sample data are available at: https://github.com/kwu14victor/PostFocus.