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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 H 2 O 2 , 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., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the literature has characterized algorithms that predict pregnancy, ploidy or blastocyst quality, leaving to the side the task of identifying key morphokinetic events. Using a dataset of N = 1909 embryos collected from multiple clinics equipped with EMBRYOSCOPE/EMBRYOSCOPE+ (Vitrolife), GERI (Genea Biomedx) or MIRI (Esco Medical), this study proposes a novel deep-learning architecture to automatically detect 11 kinetic events (from 1-cell to blastocyst). First, a Transformer based video backbone was trained with a custom metric inspired by reverse cross-entropy which enables the model to learn the ordinal structure of the events. Second, embeddings were extracted from the backbone and passed into a Gated Recurrent Unit (GRU) sequence model to account for kinetic dependencies. A weighted average of 66.0%, 67.6% and 66.3% in timing precision, recall and F1-score respectively was reached on a test set of 278 embryos, with a model applicable to multiple TLS., Competing Interests: Declarations. Competing interests: A.B.-C. is a co-owner of, and holds stocks in, ImVitro SAS. A.B.-C. holds a patent for “Devices and processes for machine learning prediction of in vitro fertilization” (EP20305914.2). G.C., A.D., N.D. are or have been employees of ImVitro and have been granted stock options., (© 2024. The Author(s).)

Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer offers the advantage of a shorter culture duration, which reduces exposure to laboratory conditions, potentially enhancing embryonic development within a more conducive uterine environment and improving the likelihood of successful pregnancies. In this paper, we present a novel ResNet-GRU deep-learning model to predict blastocyst formation at 72 HPI. The model considers the time-lapse images from the incubator from Day 0 to Day 3. The model predicts blastocyst formation with a validation accuracy of 93% from the cleavage stage. The sensitivity and specificity are 0.97 and 0.77 respectively. The deep learning model presented in this paper will assist the embryologist in identifying the best embryo to transfer at Day 3, leading to improved patient outcomes and pregnancy rates in ART., Competing Interests: Declarations Conflict of interest The authors declare that they have no conflict of interest., (© 2024. The Author(s).)

For investigations into fate specification and morphogenesis in time-lapse images of preimplantation embryos, automated 3D instance segmentation and tracking of nuclei are invaluable. Low signal-to-noise ratio, high voxel anisotropy, high nuclear density, and variable nuclear shapes can limit the performance of segmentation methods, while tracking is complicated by cell divisions, low frame rates, and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of annotated 3D data. Here, we first report a previously unreported mouse line expressing near-infrared nuclear reporter H2B-miRFP720. We then generate a dataset (termed BlastoSPIM) of 3D images of H2B-miRFP720-expressing embryos with ground truth for nuclear instances. Using BlastoSPIM, we benchmark seven convolutional neural networks and identify Stardist-3D as the most accurate instance segmentation method. With our BlastoSPIM-trained Stardist-3D models, we construct a complete pipeline for nuclear instance segmentation and lineage tracking from the eight-cell stage to the end of preimplantation development (>100 nuclei). Finally, we demonstrate the usefulness of BlastoSPIM as pre-train data for related problems, both for a different imaging modality and for different model systems., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

Microscopy is essential for detecting, identifying, analyzing, and measuring small objects. Access to modern microscopy equipment is crucial for scientific research, especially in the biomedical and analytical sciences. However, the high cost of equipment, limited availability of parts, and challenges associated with transporting equipment often limit the accessibility and operational capabilities of these tools, particularly in field sites and other remote or resource-limited settings. Thus, there is a need for affordable and accessible alternatives to traditional microscopy systems. We address this challenge by investigating the feasibility of using a simple microcontroller board not only as a portable and field-ready digital microscope, but furthermore as a versatile platform which can easily be adapted to a variety of imaging applications. By adding a few external components, we demonstrate that a low-cost ESP32 camera board can be used to build an autonomous in situ platform for digital time-lapse imaging of cells. Our prototype of this approach, which we call ESPressoscope, can be adapted to applications ranging from monitoring incubator cell cultures in the lab to observing ecological phenomena in the sea, and it can be adapted for other techniques such as microfluidics or spectrophotometry. Our prototype of the ESPressoscope concept achieves a low power consumption and small size, which makes it ideal for field research in environments and applications where microscopy was previously infeasible. Its Wi-Fi connectivity enables integration with external image processing and storage systems, including on cloud platforms when internet access is available. Finally, we present several web browser-based tools to help users operate and manage our prototype's software. Our findings demonstrate the potential for low-cost, portable microscopy solutions to enable new and more accessible experiments for biological and analytical applications., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Study Question: Could an artificial intelligence (AI) algorithm predict fetal heartbeat from images of vitrified-warmed embryos?, Summary Answer: Applying AI to vitrified-warmed blastocysts may help predict which ones will result in implantation failure early enough to thaw another., What Is Known Already: The application of AI in the field of embryology has already proven effective in assessing the quality of fresh embryos. Therefore, it could also be useful to predict the outcome of frozen embryo transfers, some of which do not recover their pre-vitrification volume, collapse, or degenerate after warming without prior evidence., Study Design, Size, Duration: This retrospective cohort study included 1109 embryos from 792 patients. Of these, 568 were vitrified blastocysts cultured in time-lapse systems in the period between warming and transfer, from February 2022 to July 2023. The other 541 were fresh-transferred blastocysts serving as controls., Participants/materials, Setting, Methods: Four types of time-lapse images were collected: last frame of development of 541 fresh-transferred blastocysts (FTi), last frame of 467 blastocysts to be vitrified (PVi), first frame post-warming of 568 vitrified embryos (PW1i), and last frame post-warming of 568 vitrified embryos (PW2i). After providing the images to the AI algorithm, the returned scores were compared with the conventional morphology and fetal heartbeat outcomes of the transferred embryos (n = 1098). The contribution of the AI score to fetal heartbeat was analyzed by multivariate logistic regression in different patient populations, and the predictive ability of the models was measured by calculating the area under the receiver-operating characteristic curve (ROC-AUC)., Main Results and the Role of Chance: Fetal heartbeat rate was related to AI score from FTi (P < 0.001), PW1i (P < 0.05), and PW2i (P < 0.001) images. The contribution of AI score to fetal heartbeat was significant in the oocyte donation program for PW2i (odds ratio (OR)=1.13; 95% CI [1.04-1.23]; P < 0.01), and in cycles with autologous oocytes for PW1i (OR = 1.18; 95% CI [1.01-1.38]; P < 0.05) and PW2i (OR = 1.15; 95% CI [1.02-1.30]; P < 0.05), but was not significantly associated with fetal heartbeat in genetically analyzed embryos. AI scores from the four groups of images varied according to morphological category (P < 0.001). The PW2i score differed in collapsed, non-re-expanded, or non-viable embryos compared to normal/viable embryos (P < 0.001). The predictability of the AI score was optimal at a post-warming incubation time of 3.3-4 h (AUC = 0.673)., Limitations, Reasons for Caution: The algorithm was designed to assess fresh embryos prior to vitrification, but not thawed ones, so this study should be considered an external trial., Wider Implications of the Findings: The application of predictive software in the management of frozen embryo transfers may be a useful tool for embryologists, reducing the cancellation rates of cycles in which the blastocyst does not recover from vitrification. Specifically, the algorithm tested in this research could be used to evaluate thawed embryos both in clinics with time-lapse systems and in those with conventional incubators only, as just a single photo is required., Study Funding/competing Interests: This study was supported by the Regional Ministry of Innovation, Universities, Science and Digital Society of the Valencian Community (CIACIF/2021/019) and by Instituto de Salud Carlos III (PI21/00283), and co-funded by European Union (ERDF, 'A way to make Europe'). M.M. received personal fees in the last 5 years as honoraria for lectures from Merck, Vitrolife, MSD, Ferring, AIVF, Theramex, Gedeon Richter, Genea Biomedx, and Life Whisperer. There are no other competing interests., Trial Registration Number: N/A., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Time-lapse imaging is a powerful technique widely used in Cellular and Molecular Biology to capture and analyze the dynamic processes of living cells over specific time periods. Particularly, in the study of cell division, under normal conditions or after drug treatments, this methodology can provide essential data that is impossible to obtain from conventional cell-fixed-based assays. In the context of evaluating cell fate after antimitotic drug treatment, time-lapse imaging provides valuable insights into the mechanisms of action of these compounds. Antimitotic drugs belong to a class of anticancer compounds that interfere with mitosis by targeting microtubules or specific kinases and molecular motors involved in the mitotic apparatus. This chapter aims to highlight the significant advantage of using time-lapse microscopy to assess the effects of antimitotic drugs on cell behavior and fates. We describe the full protocol for time-lapse imaging, using the human lung adenocarcinoma A549 cell line as a model, after exposure to the antimitotic BI2536, a potent inhibitor of polo-like kinase 1 (PLK1). This description includes software for imaging acquisition and data analysis., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Exit from M-phase requires a precise sequence of molecular events for successful completion, with errors in the process resulting in cell death or aneuploidy, a characteristic feature of cancer and the leading cause of pregnancy failure. Exit from the second meiotic division (MII) in oocytes is a unique event triggered by sperm, involving female anaphase II as well as both male and female pronuclear formation. Very little is known about how these events involving two distinct cell types are coordinated. M-phase exit is driven by inactivation of the master cell-cycle regulator, cyclin-dependent kinase 1 (Cdk1), but details of how Cdk1 orchestrates MII exit has remained sketchy due to technical challenges in studying these events. Here we detail a protocol for undertaking in-depth analysis of Cdk1 activity throughout fertilization in live mouse oocytes using a Cdk1 Fluorescence Resonance Energy Transfer (FRET) biosensor. This protocol illustrates the utility of time-lapse imaging and FRET for interrogating experimentally challenging cell-cycle events., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Time-lapse microscopy is a valuable, widely employed technique for investigating cell cycle dynamics. This chapter will guide you in using this tool to determine the duration of anaphase as a measure of mitotic exit kinetics in budding yeast cells. The methodology explained here is based on monitoring the mitotic spindle and provides a comprehensive guide covering all aspects, from sample preparation and microscopy to data analysis., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The role of shear stress in regulating aqueous humor (AH) outflow and intraocular pressure (IOP) in the trabecular meshwork (TM) and Schlemm's canal (SC) of the eye is an emerging field. Shear stress has been shown to activate mechanosensitive ion channels in TM cells and induce nitric oxide production in SC cells, which can affect outflow resistance and lower IOP. Live-cell imaging using fluorescent protein sensors has provided real-time data to investigate the physiological relationship between fluid flow and shear stress in the outflow pathway cells. The successful application of time-lapse live-cell imaging in primary cultured cells has led to the identification of key cellular and molecular mechanisms involved in regulating AH outflow and IOP, including the role of autophagy and primary cilia as mechanosensors. This chapter presents a detailed protocol for conducting time-lapse live-cell imaging under fluid flow conditions in the outflow pathway cells., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using deep learning. Hence, we develop a pipeline of deep learning models that accurately identify dividing cells in time-lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within the Drosophila pupal wing epithelium, both as it undergoes developmental morphogenesis and as it repairs following laser wounding. We show that the division axis is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) burst of cell divisions back from the leading edge., Competing Interests: JT, IC, PM, TL, HW No competing interests declared, (© 2023, Turley et al.)

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&#95;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., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

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., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Time-lapse microscopy offers a powerful approach for analyzing cellular activity. In particular, this technique is valuable for assessing the behavior of bacterial populations, which can exhibit growth and intercellular interactions in a monolayer. Such time-lapse imaging typically generates large quantities of data, limiting the options for manual investigation. Several image-processing software packages have been developed to facilitate analysis. It can thus be a challenge to identify the software package best suited to a particular research goal. Here, we compare four software packages that support the analysis of 2D time-lapse images of cellular populations: CellProfiler, SuperSegger-Omnipose, DeLTA, and FAST. We compare their performance against benchmarked results on time-lapse observations of Escherichia coli populations. Performance varies across the packages, with each of the four outperforming the others in at least one aspect of the analysis. Not surprisingly, the packages that have been in development for longer showed the strongest performance. We found that deep learning-based approaches to object segmentation outperformed traditional approaches, but the opposite was true for frame-to-frame object tracking. We offer these comparisons, together with insight into usability, computational efficiency, and feature availability, as a guide to researchers seeking image-processing solutions., Importance: Time-lapse microscopy provides a detailed window into the world of bacterial behavior. However, the vast amount of data produced by these techniques is difficult to analyze manually. We have analyzed four software tools designed to process such data and compared their performance, using populations of commonly studied bacterial species as our test subjects. Our findings offer a roadmap to scientists, helping them choose the right tool for their research. This comparison bridges a gap between microbiology and computational analysis, streamlining research efforts., Competing Interests: The authors declare no conflict of interest.

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., (© The Author(s) 2024. Published by Oxford University Press.)

Background: For in vitro fertilization (IVF), mitochondrial DNA (mtDNA) levels in the trophectodermal (TE) cells of biopsied blastocysts have been suggested to be associated with the cells' developmental potential. However, scholars have reached differing opinions regarding the use of mtDNA levels as a reliable biomarker for predicting IVF outcomes. Therefore, this study aims to assess the association of mitochondrial copy number measured by mitoscore associated with embryonic developmental characteristics and ploidy., Methods: This retrospective study analyzed the developmental characteristics of embryos and mtDNA levels in biopsied trophectodermal cells. The analysis was carried out using time-lapse monitoring and next-generation sequencing from September 2021 to September 2022. Five hundred and fifteen blastocysts were biopsied from 88 patients undergoing IVF who met the inclusion criteria. Embryonic morphokinetics and morphology were evaluated at 118 h after insemination using all recorded images. Blastocysts with appropriate morphology on day 5 or 6 underwent TE biopsy and preimplantation genetic testing for aneuploidy (PGT-A). Statistical analysis involved generalized estimating equations, Pearson's chi-squared test, Fisher's exact test, and Kruskal-Wallis test, with a significance level set at P < 0.05., Results: To examine differences in embryonic characteristics between blastocysts with low versus high mitoscores, the blastocysts were divided into quartiles based on their mitoscore. Regarding morphokinetic characteristics, no significant differences in most developmental kinetics and observed cleavage dysmorphisms were discovered. However, blastocysts in mitoscore group 1 had a longer time for reaching 3-cell stage after tPNf (t3; median: 14.4 h) than did those in mitoscore group 2 (median: 13.8 h) and a longer second cell cycle (CC2; median: 11.7 h) than did blastocysts in mitoscore groups 2 (median: 11.3 h) and 4 (median: 11.4 h; P < 0.05). Moreover, blastocysts in mitoscore group 4 had a lower euploid rate (22.6%) and a higher aneuploid rate (59.1%) than did those in the other mitoscore groups (39.6-49.3% and 30.3-43.2%; P < 0.05). The rate of whole-chromosomal alterations in mitoscore group 4 (63.4%) was higher than that in mitoscore groups 1 (47.3%) and 2 (40.1%; P < 0.05). A multivariate logistic regression model was used to analyze associations between the mitoscore and euploidy of elective blastocysts. After accounting for factors that could potentially affect the outcome, the mitoscore still exhibited a negative association with the likelihood of euploidy (adjusted OR = 0.581, 95% CI: 0.396-0.854; P = 0.006)., Conclusions: Blastocysts with varying levels of mitochondrial DNA, identified through biopsies, displayed similar characteristics in their early preimplantation development as observed through time-lapse imaging. However, the mitochondrial DNA level determined by the mitoscore can be used as a standalone predictor of euploidy., (© 2024. The Author(s).)

Background: Time-lapse imaging systems for embryo incubation and selection might improve outcomes of in-vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI) treatment due to undisturbed embryo culture conditions, improved embryo selection, or both. However, the benefit remains uncertain. We aimed to evaluate the effectiveness of time-lapse imaging systems providing undisturbed culture and embryo selection, and time-lapse imaging systems providing only undisturbed culture, and compared each with standard care without time-lapse imaging., Methods: We conducted a multicentre, three-parallel-group, double-blind, randomised controlled trial in participants undergoing IVF or ICSI at seven IVF centres in the UK and Hong Kong. Embryologists randomly assigned participants using a web-based system, stratified by clinic in a 1:1:1 ratio to the time-lapse imaging system for undisturbed culture and embryo selection (time-lapse imaging group), time-lapse imaging system for undisturbed culture alone (undisturbed culture group), and standard care without time-lapse imaging (control group). Women were required to be aged 18-42 years and men (ie, their partners) 18 years or older. Couples had to be receiving their first, second, or third IVF or ICSI treatment and could not participate if using donor gametes. Participants and trial staff were masked to group assignment, embryologists were not. The primary outcome was live birth. We performed analyses using the intention-to-treat principle and reported the main analysis in participants with primary outcome data available (full analysis set). The trial is registered on the International Trials Registry (ISRCTN17792989) and is now closed., Findings: 1575 participants were randomly assigned to treatment groups (525 participants per group) between June 21, 2018, and Sept 30, 2022. The live birth rates were 33·7% (175/520) in the time-lapse imaging group, 36·6% (189/516) in the undisturbed culture group, and 33·0% (172/522) in the standard care group. The adjusted odds ratio was 1·04 (97·5% CI 0·73 to 1·47) for time-lapse imaging arm versus control and 1·20 (0·85 to 1·70) for undisturbed culture versus control. The risk reduction for the absolute difference was 0·7 percentage points (97·5% CI -5·85 to 7·25) between the time-lapse imaging and standard care groups and 3·6 percentage points (-3·02 to 10·22) between the undisturbed culture and standard care groups. 79 serious adverse events unrelated to the trial were reported (n=28 in time-lapse imaging, n=27 in undisturbed culture, and n=24 in standard care)., Interpretation: In women undergoing IVF or ICSI treatment, the use of time-lapse imaging systems for embryo culture and selection does not significantly increase the odds of live birth compared with standard care without time-lapse imaging., Funding: Barts Charity, Pharmasure Pharmaceuticals, Hong Kong OG Trust Fund, Hong Kong Health and Medical Research Fund, Hong Kong Matching Fund., Competing Interests: Declaration of interests PB declares support for the current trial and manuscript from Barts Charity (MGU0374) and Pharmasure Pharmaceuticals through Queen Mary University of London. DYLC declares support for the current trial and manuscript from Hong Kong OG trust fund (reference number 6904985), Hong Kong Health and Medical Research Fund (07180566), and Hong Kong Matching Fund (RMG01-8601386) through the Chinese University of Hong Kong. YiC declares grants from the National Institute for Health and Care Research and Medical Research Council through the University of Southampton, honoraria for lectures through Ferring Pharmaceuticals and Merck, and being a minor shareholder for Complete Fertility. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Competing Interests: We declare no competing interests.

Background: The occurrence of blastocyst collapse may become an indicator of preimplantation embryo quality assessment. It has been reported that collapsing blastocysts can lead to higher rates of aneuploidy and poorer clinical outcomes, but more large-scale studies are needed to explore this relationship. This study explored the characteristics of blastocyst collapse identified and quantified by artificial intelligence and explored the associations between blastocyst collapse and embryo ploidy, morphological quality, and clinical outcomes., Methods: This observational study included data from 3288 biopsied blastocysts in 1071 time-lapse preimplantation genetic testing cycles performed between January 2019 and February 2023 at a single academic fertility center. All transferred blastocysts are euploid blastocysts. The artificial intelligence recognized blastocyst collapse in time-lapse microscopy videos and then registered the collapsing times, and the start time, the recovery duration, the shrinkage percentage of each collapse. The effects of blastocyst collapse and embryo ploidy, pregnancy, live birth, miscarriage, and embryo quality were studied using available data from 1196 euploid embryos and 1300 aneuploid embryos., Results: 5.6% of blastocysts collapsed at least once only before the full blastocyst formation (tB), 19.4% collapsed at least once only after tB, and 3.1% collapsed both before and after tB. Multiple collapses of blastocysts after tB (times ≥ 2) are associated with higher aneuploid rates (54.6%, P > 0.05; 70.5%, P < 0.001; 72.5%, P = 0.004; and 71.4%, P = 0.049 in blastocysts collapsed 1, 2, 3 or ≥ 4 times), which remained significant after adjustment for confounders (OR = 2.597, 95% CI 1.464-4.607, P = 0.001). Analysis of the aneuploid embryos showed a higher ratio of collapses and multiple collapses after tB in monosomies and embryos with subchromosomal deletion of segmental nature (P < 0.001). Blastocyst collapse was associated with delayed embryonic development and declined blastocyst quality. There is no significant difference in pregnancy and live birth rates between collapsing and non-collapsing blastocysts., Conclusions: Blastocyst collapse is common during blastocyst development. This study underlined that multiple blastocyst collapses after tB may be an independent risk factor for aneuploidy which should be taken into account by clinicians and embryologists when selecting blastocysts for transfer., (© 2024. The Author(s).)

Multi-Object tracking in real world environments is a tough problem, especially for cell morphogenesis with division. Most cell tracking methods are hard to achieve reliable mitosis detection, efficient inter-frame matching, and accurate state estimation simultaneously within a unified tracking framework. In this paper, we propose a novel unified framework that leverages a spatio-temporal ant colony evolutionary algorithm to track cells amidst mitosis under measurement uncertainty. Each Bernoulli ant colony representing a migrating cell is able to capture the occurrence of mitosis through the proposed Isolation Random Forest (IRF)-assisted temporal mitosis detection algorithm with the assumption that mitotic cells exhibit unique spatio-temporal features different from non-mitotic ones. Guided by prediction of a division event, multiple ant colonies evolve between consecutive frames according to an augmented assignment matrix solved by the extended Hungarian method. To handle dense cell populations, an efficient group partition between cells and measurements is exploited, which enables multiple assignment tasks to be executed in parallel with a reduction in matrix dimension. After inter-frame traversing, the ant colony transitions to a foraging stage in which it begins approximating the Bernoulli parameter to estimate cell state by iteratively updating its pheromone field. Experiments on multi-cell tracking in the presence of cell mitosis and morphological changes are conducted, and the results demonstrate that the proposed method outperforms state-of-the-art approaches, striking a balance between accuracy and computational efficiency.

Background: Enhancing detection of cryptic snakes is critical for the development of conservation and management strategies; yet, finding methods that provide adequate detection remains challenging. Issues with detecting snakes can be particularly problematic for some species, like the invasive Burmese python ( Python bivittatus ) in the Florida Everglades., Methods: Using multiple survey methods, we predicted that our ability to detect pythons, larger snakes and all other snakes would be enhanced with the use of live mammalian lures (domesticated rabbits; Oryctolagus cuniculus ). Specifically, we used visual surveys, python detection dogs, and time-lapse game cameras to determine if domesticated rabbits were an effective lure., Results: Time-lapse game cameras detected almost 40 times more snakes ( n = 375, treatment = 245, control = 130) than visual surveys ( n = 10). We recorded 21 independent detections of pythons at treatment pens (with lures) and one detection at a control pen (without lures). In addition, we found larger snakes, and all other snakes were 165% and 74% more likely to be detected at treatment pens compared to control pens, respectively. Time-lapse cameras detected almost 40 times more snakes than visual surveys; we did not detect any pythons with python detection dogs., Conclusions: Our study presents compelling evidence that the detection of snakes is improved by coupling live mammalian lures with time-lapse game cameras. Although the identification of smaller snake species was limited, this was due to pixel resolution, which could be improved by changing the camera focal length. For larger snakes with individually distinctive patterns, this method could potentially be used to identify unique individuals and thus allow researchers to estimate population dynamics., Competing Interests: McKayla M. Spencer is employed by Florida Fish and Wildlife Conservation Commission, and Kristen Hart is employed by United States Geological Survey.

Time-lapse microscopy for embryos is a non-invasive technology used to characterize early embryo development. This study employs time-lapse microscopy and machine learning to elucidate changes in embryonic growth kinetics with maternal aging. We analyzed morphokinetic parameters of embryos from young and aged C57BL6/NJ mice via continuous imaging. Our findings show that aged embryos accelerated through cleavage stages (from 5-cells) to morula compared to younger counterparts, with no significant differences observed in later stages of blastulation. Unsupervised machine learning identified two distinct clusters comprising of embryos from aged or young donors. Moreover, in supervised learning, the extreme gradient boosting algorithm successfully predicted the age-related phenotype with 0.78 accuracy, 0.81 precision, and 0.83 recall following hyperparameter tuning. These results highlight two main scientific insights: maternal aging affects embryonic development pace, and artificial intelligence can differentiate between embryos from aged and young maternal mice by a non-invasive approach. Thus, machine learning can be used to identify morphokinetics phenotypes for further studies. This study has potential for future applications in selecting human embryos for embryo transfer, without or in complement with preimplantation genetic testing., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for the Study of Reproduction.)

Study Question: Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts?, Summary Answer: Generative AI models exhibit the capability to generate high-fidelity human blastocyst images, thereby providing substantial training datasets crucial for the development of robust AI models., What Is Known Already: The integration of AI into IVF procedures holds the potential to enhance objectivity and automate embryo selection for transfer. However, the effectiveness of AI is limited by data scarcity and ethical concerns related to patient data privacy. Generative adversarial networks (GAN) have emerged as a promising approach to alleviate data limitations by generating synthetic data that closely approximate real images., Study Design, Size, Duration: Blastocyst images were included as training data from a public dataset of time-lapse microscopy (TLM) videos (n = 136). A style-based GAN was fine-tuned as the generative model., Participants/materials, Setting, Methods: We curated a total of 972 blastocyst images as training data, where frames were captured within the time window of 110-120 h post-insemination at 1-h intervals from TLM videos. We configured the style-based GAN model with data augmentation (AUG) and pretrained weights (Pretrained-T: with translation equivariance; Pretrained-R: with translation and rotation equivariance) to compare their optimization on image synthesis. We then applied quantitative metrics including Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) to assess the quality and fidelity of the generated images. Subsequently, we evaluated qualitative performance by measuring the intelligence behavior of the model through the visual Turing test. To this end, 60 individuals with diverse backgrounds and expertise in clinical embryology and IVF evaluated the quality of synthetic embryo images., Main Results and the Role of Chance: During the training process, we observed consistent improvement of image quality that was measured by FID and KID scores. Pretrained and AUG + Pretrained initiated with remarkably lower FID and KID values compared to both Baseline and AUG + Baseline models. Following 5000 training iterations, the AUG + Pretrained-R model showed the highest performance of the evaluated five configurations with FID and KID scores of 15.2 and 0.004, respectively. Subsequently, we carried out the visual Turing test, such that IVF embryologists, IVF laboratory technicians, and non-experts evaluated the synthetic blastocyst-stage embryo images and obtained similar performance in specificity with marginal differences in accuracy and sensitivity., Limitations, Reasons for Caution: In this study, we primarily focused the training data on blastocyst images as IVF embryos are primarily assessed in blastocyst stage. However, generation of an array of images in different preimplantation stages offers further insights into the development of preimplantation embryos and IVF success. In addition, we resized training images to a resolution of 256 × 256 pixels to moderate the computational costs of training the style-based GAN models. Further research is needed to involve a more extensive and diverse dataset from the formation of the zygote to the blastocyst stage, e.g. video generation, and the use of improved image resolution to facilitate the development of comprehensive AI algorithms and to produce higher-quality images., Wider Implications of the Findings: Generative AI models hold promising potential in generating high-fidelity human blastocyst images, which allows the development of robust AI models as it can provide sufficient training datasets while safeguarding patient data privacy. Additionally, this may help to produce sufficient embryo imaging training data with different (rare) abnormal features, such as embryonic arrest, tripolar cell division to avoid class imbalances and reach to even datasets. Thus, generative models may offer a compelling opportunity to transform embryo selection procedures and substantially enhance IVF outcomes., Study Funding/competing Interest(s): This study was supported by a Horizon 2020 innovation grant (ERIN, grant no. EU952516) and a Horizon Europe grant (NESTOR, grant no. 101120075) of the European Commission to A.S. and M.Z.E., the Estonian Research Council (grant no. PRG1076) to A.S., and the EVA (Erfelijkheid Voortplanting & Aanleg) specialty program (grant no. KP111513) of Maastricht University Medical Centre (MUMC+) to M.Z.E., Trial Registration Number: Not applicable., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology.)

Arabidopsis root is a classic model system in plant cell and molecular biology. The sensitivity of plant roots to local environmental perturbation challenges data reproducibility and incentivizes further optimization of imaging and phenotyping tools. Here we present RoPod, an easy-to-use toolkit for low-stress live time-lapse imaging of Arabidopsis roots. RoPod comprises a dedicated protocol for plant cultivation and a customizable 3D-printed vessel with integrated microscopy-grade glass that serves simultaneously as a growth and imaging chamber. RoPod reduces impact of sample handling, preserves live samples for prolonged imaging sessions, and facilitates application of treatments during image acquisition. We describe a protocol for RoPods fabrication and provide illustrative application pipelines for monitoring root hair growth and autophagic activity. Furthermore, we showcase how the use of RoPods advanced our understanding of plant autophagy, a major catabolic pathway and a key player in plant fitness. Specifically, we obtained fine time resolution for autophagy response to commonly used chemical modulators of the pathway and revealed previously overlooked cell type-specific changes in the autophagy response. These results will aid a deeper understanding of the physiological role of autophagy and provide valuable guidelines for choosing sampling time during end-point assays currently employed in plant autophagy research., (© 2024. The Author(s).)

The aim was to explore whether the time-lapse imaging system can help day-3 single cleavage embryo transfer to obtain comparative clinical outcomes to day-4 or 5. The data of 1237 patients who underwent single embryo transfer from January 1, 2018, to September 30, 2020, in our reproductive medicine centre were retrospectively analysed. They were divided into the day-3 single cleavage-stage embryo transfer (SCT) group ( n = 357), day-4 single morula transfer (SMT) group ( n = 129) and day-5 single blastocyst transfer (SBT) group ( n = 751) according to the different embryo transfer stage. The clinical and perinatal outcomes of the three groups were analysed and compared. The clinical pregnancy rates of the patients in the day-3 SCT group, day-4 SMT group and day-5 SBT group were 68.07, 70.54 and 72.04%, respectively. The live birth rates were 56.86, 61.24 and 60.99%, respectively. The monozygotic twin (MZT) rate in the day-3 SCT group was significantly lower than that in the day-5 SBT group ( P = 0.049). Regarding perinatal outcomes, only the secondary sex ratio had a significant difference ( P < 0.05). After age stratification, no improvement was found in the pregnancy outcomes of patients >35 years of age receiving blastocyst transfer. Our findings suggest that for patients with multiple high-quality embryos on day-3, prolonging the culture time can improve the pregnancy outcome to some extent, but it will bring risks. For centres that have established morphodynamic models, day-3 SCT can also achieve an ideal pregnancy outcome and reduce the rate of monozygotic twins and sex ratio.

Differences in gene-expression profiles between individual cells can give rise to distinct cell fate decisions. Yet how localisation on a micropattern impacts initial changes in mRNA, protein, and phosphoprotein abundance remains unclear. To identify the effect of cellular position on gene expression, we developed a scalable antibody and mRNA targeting sequential fluorescence in situ hybridisation (ARTseq-FISH) method capable of simultaneously profiling mRNAs, proteins, and phosphoproteins in single cells. We studied 67 (phospho-)protein and mRNA targets in individual mouse embryonic stem cells (mESCs) cultured on circular micropatterns. ARTseq-FISH reveals relative changes in both abundance and localisation of mRNAs and (phospho-)proteins during the first 48 hours of exit from pluripotency. We confirm these changes by conventional immunofluorescence and time-lapse microscopy. Chemical labelling, immunofluorescence, and single-cell time-lapse microscopy further show that cells closer to the edge of the micropattern exhibit increased proliferation compared to cells at the centre. Together these data suggest that while gene expression is still highly heterogeneous position-dependent differences in mRNA and protein levels emerge as early as 12 hours after LIF withdrawal., (© 2024. The Author(s).)

In this review, we take a fresh look at embryo assessment and selection methods from the perspective of diagnosis and prognosis. On the basis of a systematic search in the literature, we examined the evidence on the prognostic value of different embryo assessment methods, including morphological assessment, blastocyst culture, time-lapse imaging, artificial intelligence, and preimplantation genetic testing for aneuploidy., Competing Interests: Declaration of interests H.Z. has nothing to disclose. R.W. has reported funding from the National Health and Medical Research Council’s (NHMRC) Emerging Leadership Investigator Grant paid to the institution. D.E.M. has nothing to disclose., (Copyright © 2024 American Society for Reproductive Medicine. All rights reserved.)

Purpose: To study the effectiveness of whole-scenario embryo identification using a self-supervised learning encoder (WISE) in in vitro fertilization (IVF) on time-lapse, cross-device, and cryo-thawed scenarios., Methods: WISE was based on the vision transformer (ViT) architecture and masked autoencoders (MAE), a self-supervised learning (SSL) method. To train WISE, we prepared three datasets including the SSL pre-training dataset, the time-lapse identification dataset, and the cross-device identification dataset. To identify whether pairs of images were from the same embryos in different scenarios in the downstream identification tasks, embryo images including time-lapse and microscope images were first pre-processed through object detection, cropping, padding, and resizing, and then fed into WISE to get predictions., Results: WISE could accurately identify embryos in the three scenarios. The accuracy was 99.89% on the time-lapse identification dataset, and 83.55% on the cross-device identification dataset. Besides, we subdivided a cryo-thawed evaluation set from the cross-device test set to have a better estimation of how WISE performs in the real-world, and it reached an accuracy of 82.22%. There were approximately 10% improvements in cross-device and cryo-thawed identification tasks after the SSL method was applied. Besides, WISE demonstrated improvements in the accuracy of 9.5%, 12%, and 18% over embryologists in the three scenarios., Conclusion: SSL methods can improve embryo identification accuracy even when dealing with cross-device and cryo-thawed paired images. The study is the first to apply SSL in embryo identification, and the results show the promise of WISE for future application in embryo witnessing., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5., Methods: We retrospectively analysed the morphokinetics of 575 embryos obtained from 80 women who underwent IVF at our Unit. Embryo morphokinetics was registered using the Geri plus® time-lapse system. Overall, 30 clinical, morphological and morphokinetic variables related to women and embryos were recorded and combined. Some embryos reached the expanded blastocyst stage on day 5 (BL Group, n = 210), some others did not (nBL Group, n = 365)., Results: The novel EmbryoMLSelection framework was developed following four-steps: Feature Selection, Rules Extraction, Rules Selection and Rules Evaluation. Six rules composed by a combination of 8 variables were finally selected, and provided a predictive power described by an AUC of 0.84 and an accuracy of 81%., Conclusions: We provided herein a new feature-signature able to identify with an high performance embryos with the best developmental competence to reach the expanded blastocyst stage on day 5. Clear and clinically relevant cut-offs were identified for each considered variable, providing an objective tool for early embryo developmental assessment., (© 2024. The Author(s).)

During the development of the cerebral cortex, neurons and glial cells originate in the ventricular zone lining the ventricle and migrate toward the brain surface. This process is crucial for proper brain function, and its dysregulation can result in neurodevelopmental and psychiatric disorders after birth. In fact, many genes responsible for these diseases have been found to be involved in this process, and therefore, revealing how these mutations affect cellular dynamics is important for understanding the pathogenesis of these diseases. This protocol introduces a technique for time-lapse imaging of migrating neurons and glial progenitors in brain slices obtained from mouse embryos. Cells are labeled with fluorescent proteins using in utero electroporation, which visualizes individual cells migrating from the ventricular zone with a high signal-to-noise ratio. Moreover, this in vivo gene transfer system enables us to easily perform gain-of-function or loss-of-function experiments on the given genes by co-electroporation of their expression or knockdown/knockout vectors. Using this protocol, the migratory behavior and migration speed of individual cells, information that is never obtained from fixed brains, can be analyzed.

With the rising demand for in vitro fertilization (IVF) cycles, there is a growing need for innovative techniques to optimize procedure outcomes. One such technique is time-lapse system (TLS) for embryo incubation, which minimizes environmental changes in the embryo culture process. TLS also significantly advances predicting embryo quality, a crucial determinant of IVF cycle success. However, the current subjective nature of embryo assessments is due to inter- and intra-observer subjectivity, resulting in highly variable results. To address this challenge, reproductive medicine has gradually turned to artificial intelligence (AI) to establish a standardized and objective approach, aiming to achieve higher success rates. Extensive research is underway investigating the utilization of AI in TLS to predict multiple outcomes. These studies explore the application of popular AI algorithms, their specific implementations, and the achieved advancements in TLS. This review aims to provide an overview of the advances in AI algorithms and their particular applications within the context of TLS and the potential challenges and opportunities for further advancements in reproductive medicine., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: To evaluate the degree of agreement of embryo ranking between embryologists and eight artificial intelligence (AI) algorithms., Design: Retrospective study., Patient(s): A total of 100 cycles with at least eight embryos were selected from the Weill Cornell Medicine database. For each embryo, the full-length time-lapse (TL) videos, as well as a single embryo image at 120 hours, were given to five embryologists and eight AI algorithms for ranking., Intervention(s): None., Main Outcome Measure(s): Kendall rank correlation coefficient (Kendall's τ)., Result(s): Embryologists had a high degree of agreement in the overall ranking of 100 cycles with an average Kendall's tau (K-τ) of 0.70, slightly lower than the interembryologist agreement when using a single image or video (average K-τ = 0.78). Overall agreement between embryologists and the AI algorithms was significantly lower (average K-τ = 0.53) and similar to the observed low inter-AI algorithm agreement (average K-τ = 0.47). Notably, two of the eight algorithms had a very low agreement with other ranking methodologies (average K-τ = 0.05) and between each other (K-τ = 0.01). The average agreement in selecting the best-quality embryo (1/8 in 100 cycles with an expected agreement by random chance of 12.5%; confidence interval [CI]95: 6%-19%) was 59.5% among embryologists and 40.3% for six AI algorithms. The incidence of the agreement for the two algorithms with the low overall agreement was 11.7%. Agreement on selecting the same top two embryos/cycle (expected agreement by random chance corresponds to 25.0%; CI95: 17%-32%) was 73.5% among embryologists and 56.0% among AI methods excluding two discordant algorithms, which had an average agreement of 24.4%, the expected range of agreement by random chance. Intraembryologist ranking agreement (single image vs. video) was 71.7% and 77.8% for single and top two embryos, respectively. Analysis of average raw scores indicated that cycles with low diversity of embryo quality generally resulted in a lower overall agreement between the methods (embryologists and AI models)., Conclusion(s): To our knowledge, this is the first study that evaluates the level of agreement in ranking embryo quality between different AI algorithms and embryologists. The different concordance methods were consistent and indicated that the highest agreement was intraembryologist agreement, followed by interembryologist agreement. In contrast, the agreement between some of the AI algorithms and embryologists was similar to the inter-AI algorithm agreement, which also showed a wide range of pairwise concordance. Specifically, two AI models showed intra- and interagreement at the level expected from random selection., (Copyright © 2023 American Society for Reproductive Medicine. All rights reserved.)

Time-lapse fluorescence microscopy is key to unraveling biological development and function; however, living systems, by their nature, permit only limited interrogation and contain untapped information that can only be captured by more invasive methods. Deep-tissue live imaging presents a particular challenge owing to the spectral range of live-cell imaging probes/fluorescent proteins, which offer only modest optical penetration into scattering tissues. Herein, we employ convolutional neural networks to augment live-imaging data with deep-tissue images taken on fixed samples. We demonstrate that convolutional neural networks may be used to restore deep-tissue contrast in GFP-based time-lapse imaging using paired final-state datasets acquired using near-infrared dyes, an approach termed InfraRed-mediated Image Restoration (IR 2 ). Notably, the networks are remarkably robust over a wide range of developmental times. We employ IR 2 to enhance the information content of green fluorescent protein time-lapse images of zebrafish and Drosophila embryo/larval development and demonstrate its quantitative potential in increasing the fidelity of cell tracking/lineaging in developing pescoids. Thus, IR 2 is poised to extend live imaging to depths otherwise inaccessible., (© 2024. The Author(s).)

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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)., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Quantification of Mycobacterium tuberculosis (Mtb) growth dynamics in cell-based in vitro infection models is traditionally carried out by measurement of colony forming units (CFU). However, Mtb being an extremely slow growing organism (16-24 h doubling time), this approach requires at least 3 weeks of incubation to obtain measurable readouts. In this chapter, we describe an alternative approach based on time-lapse microscopy and quantitative image analysis that allows faster quantification of Mtb growth dynamics in host cells. In addition, this approach provides the capability to capture other readouts from the same experimental setup, such as host cell viability, bacterial localization as well as the dynamics of propagation of infection between the host cells., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Optogenetic tools provide a means for controlling cellular processes that is rapid, noninvasive, and spatially and temporally precise. With the increase in available optogenetic systems, quantitative comparisons of their performances become important to guide experiments. In this chapter, we first discuss how photoreceptors can be repurposed for light-mediated control of transcription. Then, we provide a detailed protocol for characterizing light-regulated transcriptional systems in budding yeast using fluorescence time-lapse microscopy and mathematical modeling, expanding on our recent publication (Gligorovski et al., Nat Commun 14:3810, 2023)., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, cells in living organisms often exhibit extensive and complex movements caused by organ deformation and whole-body motion. These movements pose a challenge in obtaining high-quality time-lapse cell images and tracking the intricate cell movements in the captured images. Recent advances in deep learning techniques provide powerful tools for detecting cells in low-quality images with densely packed cell populations, as well as estimating cell positions for cells undergoing large nonrigid movements. This chapter introduces the challenges of cell tracking in deforming organs and moving animals, outlines the solutions to these challenges, and presents a detailed protocol for data preparation, as well as for performing cell segmentation and tracking using the latest version of 3DeeCellTracker. This protocol is expected to enable researchers to gain deeper insights into organ dynamics and biological processes., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The Association for the Study of Reproductive Biology (ASEBIR) Interest Group in Embryology (in Spanish 'Grupo de Interés de Embriología') reviewed key morphokinetic parameters to assess the contribution of time-lapse technology (TLT) to the ASEBIR grading system. Embryo grading based on morphological characteristics is the most widely used method in human assisted reproduction laboratories. The introduction and implementation of TLT has provided a large amount of information that can be used as a complementary tool for morphological embryo evaluation and selection. As part of IVF treatments, embryologists grade embryos to decide which embryos to transfer or freeze. At the present, the embryo grading system developed by ASEBIR does not consider dynamic events observed through TLT. Laboratories that are using TLT consider those parameters as complementary data for embryo selection. The aim of this review was to evaluate review time-specific morphological changes during embryo development that are not included in the ASEBIR scoring system, and to consider them as candidates to add to the scoring system., (Copyright © 2023 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.)

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., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)