Tumours often display invasive behaviours that induce fingering, branching and fragmentation processes. The phenomenon, known as diffusional instability, is driven by differential cell proliferation, migration, and death due to the presence of metabolite and catabolite concentration gradients. An understanding of the intricate dynamics of this spatially heterogeneous process plays a key role in the investigation of tumour growth and invasion. In this study, we developed an in vitro tumour invasion assay to investigate cell invasiveness in tumour spheroids under a chemotactic stimulus. Our method, employing tumour spheroids seeded in a 3D collagen gel within a microfluidic chemotaxis chamber, focuses on the role of diffusive gradients. Using Time-Lapse Microscopy, the dynamic evolution of tumour spheroids was monitored in real-time, providing a comprehensive view of the morphological changes and cell migration patterns under different chemotactic conditions. Specifically, we explored the impact of fetal bovine serum (FBS) gradients on the behaviour of CT26 mouse colon carcinoma cells and compared the effects of varying FBS concentrations to two isotropic control conditions. Furthermore, a finite element in silico model was developed to quantify the diffusive flow of nutrients in the chemotaxis chamber and obtain a detailed understanding of tumour dynamics. Our findings reveal that the presence of a chemotactic gradient significantly influences tumour invasiveness, with higher concentrations of nutrients associated with increased cancer growth and cell migration.

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the \"healthy\" state of the cell and the \"dynamic stabilization\" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.

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.

Live-cell imaging of fluorescent biosensors has demonstrated that space-time correlations in signalling of cell collectives play an important organisational role in morphogenesis, wound healing, regeneration, and maintaining epithelial homeostasis. Here, we demonstrate how to quantify one such phenomenon, namely apoptosis-induced ERK activity waves in the MCF10A epithelium. We present a protocol that starts from raw time-lapse fluorescence microscopy images and, through a sequence of image manipulations, ends with ARCOS, our computational method to detect and quantify collective signalling. We also describe the same workflow in the interactive napari image viewer to quantify collective phenomena for users without prior programming experience. Our approach can be applied to space-time correlations in cells, cell collectives, or communities of multicellular organisms, in 2D and 3D geometries.

Studies of classical microbiology rely on the average behaviour of large cell populations without considering that clonal bacterial populations may bifurcate into phenotypic distinct sub-populations by random switching mechanisms.Listeria monocytogenes exposure to sublethal stresses may induce different physiological states that co-exist (i.e., sublethal injury or dormancy) and present variable resuscitation capacity. Exposures to peracetic acid (PAA; 10-30 ppm; for 3 h), acetic acid and hydrochloric acid (AA and HCl; pH 3.0-2.5; for 5 h) at 20 °C were used to induce different physiological states in L. monocytogenes, Scott A strain. After stress exposure, colony growth of single cells was monitored, on Tryptic Soy Agar supplemented with 0.6 % Yeast Extract, using time-lapse microscopy, at 37 °C. Images were acquired every 5 min and were analyzed using BaSCA framework. Most of the obtained growth curves of the colonies were fitted to the model of Baranyi and Roberts for the estimation of lag time (λ) and maximum specific growth rate (μmax), except the ones obtained after exposure to AA pH 2.7 and 2.5 that were fitted to the Trilinear model. The data of λ and μmax that followed a multivariate normal distribution were used to predict growth variability using Monte Carlo simulations. Outgrowth kinetics after treatment with AA (pH 2.7 and 2.5; for 5 h at 20 °C), PAA (30 ppm; for 3 h at 20 °C) revealed that these stress conditions increase the skewness of the variability distributions to the right, meaning that the variability in lag times increases in favour of longer outgrowth. Exposures to AA pH 2.5 and 30 ppm PAA resulted in two distinct subpopulations per generation with different growth dynamics. This switching mechanism may have evolved as a survival strategy for L. monocytogenes cells, maximizing the chances of survival. Simulation of microbial growth showed that heterogeneity in growth dynamics is increased when cells are recovering from exposure to sublethal stresses (i.e. PAA and acidic conditions) that may induce injury or dormancy.

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.

OBJECTIVE: Is there an association between morphokinetic variables of meiotic maturation and the severity of aneuploidy following in vitro maturation (IVM) in the mouse?
CONCLUSIONS: The severity of meiotic aneuploidy correlates with an extended time to first polar body extrusion (tPB1) and duration of meiosis I (dMI).
BACKGROUND: Morphokinetic variables measured using time-lapse technology allow for the non-invasive evaluation of preimplantation embryo development within clinical assisted reproductive technology (ART). We recently applied this technology to monitor meiotic progression during IVM of mouse gametes. Whether there is a relationship between morphokinetic variables of meiotic progression and aneuploidy in the resulting egg has not been systematically examined at the resolution of specific chromosomes. Next-generation sequencing (NGS) is a robust clinical tool for determining aneuploidy status and has been reverse-translated in mouse blastocysts and oocytes. Therefore, we harnessed the technologies of time-lapse imaging and NGS to determine the relationship between the morphokinetics of meiotic progression and egg aneuploidy.
UNASSIGNED: Cumulus-oocyte complexes were collected from large antral follicles from hyperstimulated CD-1 mice. Cumulus cells were removed, and spontaneous IVM was performed in the absence or presence of two doses of Nocodazole (25 or 50 nM) to induce a spectrum of spindle abnormalities and chromosome segregation errors during oocyte meiosis. Comprehensive chromosome screening was then performed in the resulting eggs, and morphokinetic variables and ploidy status were compared across experimental groups (control, n = 11; 25 nM Nocodazole, n = 13; 50 nM Nocodazole, n = 23).
METHODS: We monitored IVM in mouse oocytes using time-lapse microscopy for 16 h, and time to germinal vesicle breakdown (tGVBD), tPB1, and dMI were analyzed. Following IVM, comprehensive chromosome screening was performed on the eggs and their matched first polar bodies via adaptation of an NGS-based preimplantation genetic testing for aneuploidy (PGT-A) assay. Bioinformatics analysis was performed to align reads to the mouse genome and determine copy number-based predictions of aneuploidy. The concordance of each polar body-egg pair (reciprocal errors) was used to validate the results. Ploidy status was categorized as euploid, 1-3 chromosomal segregation errors, or ≥4 chromosomal segregation errors. Additionally, aneuploidy due to premature separation of sister chromatids (PSSC) versus non-disjunction (NDJ) was distinguished.
RESULTS: We applied and validated state-of-the-art NGS technology to screen aneuploidy in individual mouse eggs and matched polar bodies at the chromosome-specific level. By performing IVM in the presence of different doses of Nocodazole, we induced a range of aneuploidy. No aneuploidy was observed in the absence of Nocodazole (0/11), whereas IVM in the presence of 25 and 50 nM Nocodazole resulted in an aneuploidy incidence of 7.69% (1/13) and 82.61% (19/23), respectively. Of the aneuploid eggs, 5% (1/20) was due to PSSC, 65% (13/20) to NDJ, and the remainder to a combination of both. There was no relationship between ploidy status and tGVBD, but tPB1 and the dMI were both significantly prolonged in eggs with reciprocal aneuploidy events compared to the euploid eggs, and this scaled with the severity of aneuploidy. Eggs with ≥4 aneuploid chromosomes had the longest tPB1 and dMI (P < 0.0001), whereas eggs with one to three aneuploid chromosomes exhibited intermediate lengths of time (P < 0.0001).
METHODS: N/A.
CONCLUSIONS: We used Nocodazole in this study to disrupt the meiotic spindle and induce aneuploidy in mouse oocytes. Whether the association between morphokinetic variables of meiotic progression and the severity of aneuploidy occurs with other compounds that induce chromosome segregation errors remain to be investigated. In addition, unlike mouse oocytes, human IVM requires the presence of cumulus cells, which precludes visualization of morphokinetic variables of meiotic progression. Thus, our study may have limited direct clinical translatability.
CONCLUSIONS: We validated NGS in mouse eggs to detect aneuploidy at a chromosome-specific resolution which greatly improves the utility of the mouse model. With a tractable and validated model system for characterizing meiotic aneuploidy, investigations into the molecular mechanisms and factors which may influence aneuploidy can be further elaborated. Time-lapse analyses of morphokinetic variables of meiotic progression may be a useful non-invasive predictor of aneuploidy severity.
BACKGROUND: This work was supported by the Bill & Melinda Gates Foundation (INV-003385). Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. The authors have no conflict of interest to disclose.

The postnuclear entry steps of HIV-1 involve reverse transcription, uncoating, and integration into the host genome. The differential regulation of these steps has a significant impact on HIV overall replication, including integration site selection and viral gene expression. Recently, another important phenomenon has been uncovered as part of HIV interplay with the nuclear environment, specifically involving the cleavage and polyadenylation specific factor 6 (CPSF6) protein. This phenomenon is the formation of nuclear HIV-induced membraneless organelles (HIV-1 MLOs). In this article, we will describe the methods used to assess the composition and liquid-liquid phase separation (LLPS) properties of these organelles using fluorescence microscopy. The study of HIV-1 MLOs represents a new frontier that may reveal previously unknown key players in the fate of HIV-infected cells.

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.

OBJECTIVE: Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts?
CONCLUSIONS: 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.
BACKGROUND: 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.
METHODS: 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.
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.
RESULTS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
BACKGROUND: 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.
BACKGROUND: Not applicable.