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BACKGROUND: A traditional view is that stem cells (SCs) divide slowly. Meanwhile, both embryonic and pluripotent SCs display a shorter cell cycle duration (CCD) in comparison to more committed progenitors (CPs). METHODS: We examined the in vitro proliferation and cycling behavior of somatic adult human cells using live cell imaging of passage zero keratinocytes and single-cell RNA sequencing. RESULTS: We found two populations of keratinocytes: those with short CCD and protracted near exponential growth, and those with long CCD and terminal differentiation. Applying the ergodic principle, the comparative numbers of cycling cells in S phase in an enriched population of SCs confirmed a shorter CCD than CPs. Further, analysis of single-cell RNA sequencing of cycling adult human keratinocyte SCs and CPs indicated a shortening of both G1 and G2M phases in the SC. CONCLUSIONS: Contrary to the pervasive paradigm, SCs progress through cell cycle more quickly than more differentiated dividing CPs. Thus, somatic human adult keratinocyte SCs may divide infrequently, but divide rapidly when they divide. Additionally, it was found that SC-like proliferation persisted in vitro.

Abstract 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

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Is there an association between morphokinetic variables of meiotic maturation and the severity of aneuploidy following in vitro maturation (IVM) in the mouse? SUMMARY ANSWER The severity of meiotic aneuploidy correlates with an extended time to first polar body extrusion (tPB1) and duration of meiosis I (dMI). WHAT IS KNOWN ALREADY 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. STUDY DESIGN, SIZE, DURATION 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). PARTICIPANTS/MATERIALS, SETTING, 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. MAIN RESULTS AND THE ROLE OF CHANCE 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). LARGE SCALE DATA N/A. LIMITATIONS, REASONS FOR CAUTION 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. WIDER IMPLICATIONS OF THE FINDINGS 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. STUDY FUNDING/COMPETING INTERESTS 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. [ABSTRACT FROM AUTHOR]

An alarming rise in antimicrobial resistance worldwide has spurred efforts into the search for alternatives to antibiotic treatments. The use of bacteriophages, bacterial viruses harmless to humans, represents a promising approach with potential to treat bacterial infections (phage therapy). Recent advances in microscopy-based single-cell techniques have allowed researchers to develop new quantitative methodologies for assessing the interactions between bacteria and phages, especially the ability of phages to eradicate bacterial pathogen populations and to modulate growth of both commensal and pathogen populations. Here we combine droplet microfluidics with fluorescence time-lapse microscopy to characterize the growth and lysis dynamics of the bacterium Escherichia coli confined in droplets when challenged with phage. We investigated phages that promote lysis of infected E. coli cells, specifically, a phage species with DNA genome, T7 (Escherichia virus T7) and two phage species with RNA genomes, MS2 (Emesvirus zinderi) and Qβ (Qubevirus durum). Our microfluidic trapping device generated and immobilized picoliter-sized droplets, enabling stable imaging of bacterial growth and lysis in a temperature-controlled setup. Temporal information on bacterial population size was recorded for up to 25  h, allowing us to determine growth rates of bacterial populations and helping us uncover the extent and speed of phage infection. In the long-term, the development of novel microfluidic single-cell and population-level approaches will expedite research towards fundamental understanding of the genetic and molecular basis of rapid phage-induced lysis and eco-evolutionary aspects of bacteria-phage dynamics, and ultimately help identify key factors influencing the success of phage therapy [ABSTRACT FROM AUTHOR]

The embryological stage of the implementation of ART programs can be considered one of the most important, since the assessment of the competencies or "quality" of oocytes, their fertilization and in vitro cultivation to the stage of preimplantation embryos largely determine its success. An efficient method of embryo selection is currently in high demand in this field, since it is a method of selecting embryos that have the highest potential for implantation. Almost all publications on time-lapse microscopy have focused on timing specific embryonic division events and then using this information to create algorithms to help select an embryo for transfer, but there are insufficient data on obstetric and perinatal outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: We used the charged-particle microbeam installed at the AIFIRA facility to perform micro-irradiation experiments and measure the recruitment kinetics of DNA signaling and repair proteins. We developed and validated image acquisition and processing methods to enable a systematic study of the recruitment kinetics of two GFP-tagged proteins (GFP-XRCC1 and GFP-RNF8) after irradiation with protons and α-particles. Online measurement of fluorescence intensity and recruitment time as a function of particle type and number allowed us to characterize the differences in behavior between the two proteins. Time-lapse fluorescence imaging coupled to micro-irradiation devices provides information on the kinetics of DNA repair protein accumulation, from a few seconds to several minutes after irradiation. Charged-particle microbeams are valuable tools for such studies since they provide a way to selectively irradiate micrometric areas within a cell nucleus, control the dose and the micro-dosimetric quantities by means of advanced detection systems and Monte Carlo simulations and monitor the early cell response by means of beamline microscopy. We used the charged-particle microbeam installed at the AIFIRA facility to perform micro-irradiation experiments and measure the recruitment kinetics of two proteins involved in DNA signaling and repair pathways following exposure to protons and α-particles. We developed and validated image acquisition and processing methods to enable a systematic study of the recruitment kinetics of GFP-XRCC1 and GFP-RNF8. We show that XRCC1 is recruited to DNA damage sites a few seconds after irradiation as a function of the total deposited energy and quite independently of the particle LET. RNF8 is recruited to DNA damage sites a few minutes after irradiation and its recruitment kinetics depends on the particle LET. [ABSTRACT FROM AUTHOR]

Cells use molecular circuits to interpret and respond to extracellular cues, such as hormones and cytokines, which are often released in a temporally varying fashion. In this study, we combine microfluidics, time-lapse microscopy, and computational modeling to investigate how the type I interferon (IFN)-responsive regulatory network operates in single human cells to process repetitive IFN stimulation. We found that IFN-α pretreatments lead to opposite effects, priming versus desensitization, depending on input durations. These effects are governed by a regulatory network composed of a fast-acting positive feedback loop and a delayed negative feedback loop, mediated by upregulation of ubiquitin-specific peptidase 18 (USP18). We further revealed that USP18 upregulation can only be initiated at the G1/early S phases of cell cycle upon the treatment onset, resulting in heterogeneous and delayed induction kinetics in single cells. This cell cycle gating provides a temporal compartmentalization of feedback loops, enabling duration-dependent desensitization to repetitive stimulations.

During the last few decades, assisted reproductive technologies (ARTs) have flourished rapidly and accompanied a set of advanced procedures such as intracytoplasmic sperm injection (ICSI), electronic witnessing, digital monitoring through embryoscope time-lapse systems, consistent decision-making algorithms with advanced statistics modes and preimplantation genetic testing (PGT). In usual practice, manual procedures were routinely used in IVF (in vitro fertilization) laboratories worldwide, but automation and artificial intelligence (AI) systems are promising techniques for quality assurance, which reduced the burden on the working staff in the embryology laboratory. In addition, these systems are equipped with powerful mathematical tools that minimize technician variability in the IVF lab and efficiently generate data for impaired gametes and embryos. The principal challenge of single-sperm selection out of 108 gametes can be sorted out by incorporating machine learning algorithms coupled with advanced data processing capabilities. In the same line, the emergence of closed embryo culture systems (CECSs) in human embryology has enabled the accurate morphokinetic evaluation of the more rapid cell division and the identification of normal and abnormal hallmarks of embryo viability. In particular, these CECSs are guided by the latest time-lapse microscopy (TLM) facility to continuously monitor embryo development kinetics without removing them from controlled and stable incubator conditions. In conclusion, AI-driven models can reduce technical variability in sample handling and remove the burden of the most subjective, tedious and/or monotonous aspects of the IVF lab. Furthermore, these systems also highlight environmental stressors that could hamper embryo development competence. In a broader sense, AI-based approaches are more accurate, precise and rapid in predicting embryo quality noninvasively.

ABSTRACT Clostridioides difficile infections begin when its metabolically dormant spores germinate in response to sensing bile acid germinants alongside amino acid and divalent cation co-germinants in the small intestine. While bile acid germinants are essential for C. difficile spore germination, it is currently unclear whether both co-germinant signals are required. One model proposes that divalent cations, particularly Ca2+, are essential for inducing germination, while another proposes that either co-germinant class can induce germination. The former model is based on the finding that spores defective in releasing large stores of internal Ca2+ in the form of calcium dipicolinic acid (CaDPA) cannot germinate when germination is induced with bile acid germinant and amino acid co-germinant alone. However, since the reduced optical density of CaDPA-less spores makes it difficult to accurately measure their germination, we developed a novel automated, time-lapse microscopy-based germination assay to analyze CaDPA mutant germination at the single-spore level. Using this assay, we found that CaDPA mutant spores germinate in the presence of amino acid co-germinant and bile acid germinant. Higher levels of amino acid co-germinants are nevertheless required to induce CaDPA mutant spores to germinate relative to WT spores because CaDPA released by WT spores during germination can function in a feedforward loop to potentiate the germination of other spores within the population. Collectively, these data indicate that Ca2+ is not essential for inducing C. difficile spore germination because amino acid and Ca2+ co-germinant signals are sensed by parallel signaling pathways. IMPORTANCE Clostridioides difficile spore germination is essential for this major nosocomial pathogen to initiate infection. C. difficile spores germinate in response to sensing bile acid germinant signals alongside co-germinant signals. There are two classes of co-germinant signals: Ca2+ and amino acids. Prior work suggested that Ca2+ is essential for C. difficile spore germination based on bulk population analyses of germinating CaDPA mutant spores. Since these assays rely on optical density to measure spore germination and the optical density of CaDPA mutant spores is reduced relative to WT spores, this bulk assay is limited in its capacity to analyze germination. To overcome this limitation, we developed an automated image analysis pipeline to monitor C. difficile spore germination using time-lapse microscopy. With this analysis pipeline, we demonstrate that, although Ca2+ is dispensable for inducing C. difficile spore germination, CaDPA can function in a feedforward loop to potentiate the germination of neighboring spores.

Brain tissue metabolism is distributed across several cell types and subcellular compartments, which activate at different times and with different temporal patterns. The introduction of genetically-encoded fluorescent indicators that are imaged using time-lapse microscopy has opened the possibility of studying brain metabolism at cellular and sub-cellular levels. There are indicators for sugars, monocarboxylates, Krebs cycle intermediates, amino acids, cofactors, and energy nucleotides, which inform about relative levels, concentrations and fluxes. This review offers a brief survey of the metabolic indicators that have been validated in brain cells, with some illustrative examples from the literature. Whereas only a small fraction of the metabolome is currently accessible to fluorescent probes, there are grounds to be optimistic about coming developments and the application of these tools to the study of brain disease.

Much of biochemical regulation ultimately controls growth rate, particularly in microbes. Although time-lapse microscopy visualises cells, determining their growth rates is challenging, particularly for those that divide asymmetrically, like Saccharomyces cerevisiae, because cells often overlap in images. Here, we present the Birth Annotator for Budding Yeast (BABY), an algorithm to determine single-cell growth rates from label-free images. Using a convolutional neural network, BABY resolves overlaps through separating cells by size and assigns buds to mothers by identifying bud necks. BABY uses machine learning to track cells and determine lineages and estimates growth rates as the rates of change of volumes. Using BABY and a microfluidic device, we show that bud growth is likely first sizer- then timer-controlled, that the nuclear concentration of Sfp1, a regulator of ribosome biogenesis, varies before the growth rate does, and that growth rate can be used for real-time control. By estimating single-cell growth rates and so fitness, BABY should generate much biological insight.

Although genetic mutations that alter organisms' average lifespans have been identified in aging research, our understanding of the dynamic changes during aging remains limited. Here, we integrate single-cell imaging, microfluidics, and computational modeling to investigate phenotypic divergence and cellular heterogeneity during replicative aging of single S. cerevisiae cells. Specifically, we find that isogenic cells diverge early in life toward one of two aging paths, which are characterized by distinct age-associated phenotypes. We captured the dynamics of single cells along the paths with a stochastic discrete-state model, which accurately predicts both the measured heterogeneity and the lifespan of cells on each path within a cell population. Our analysis suggests that genetic and environmental factors influence both a cell's choice of paths and the kinetics of paths themselves. Given that these factors are highly conserved throughout eukaryotes, divergent aging might represent a general scheme in cellular aging of other organisms.

Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is ongoing. Although insufficient to replace PGT-A, embryo morphological evaluation is significantly associated with embryonic competence, but scarcely reproducible. Recently, artificial intelligence-powered analyses have been proposed to objectify and automate image evaluations. iDAScore v1.0 is a deep-learning model based on a 3D convolutional neural network trained on time-lapse videos from implanted and non-implanted blastocysts. It is a decision support system for ranking blastocysts without manual input. This retrospective, pre-clinical, external validation included 3604 blastocysts and 808 euploid transfers from 1232 cycles. All blastocysts were retrospectively assessed through the iDAScore v1.0; therefore, it did not influence embryologists' decision-making process. iDAScore v1.0 was significantly associated with embryo morphology and competence, although AUCs for euploidy and live-birth prediction were 0.60 and 0.66, respectively, which is rather comparable to embryologists' performance. Nevertheless, iDAScore v1.0 is objective and reproducible, while embryologists' evaluations are not. In a retrospective simulation, iDAScore v1.0 would have ranked euploid blastocysts as top quality in 63% of cases with one or more euploid and aneuploid blastocysts, and it would have questioned embryologists' ranking in 48% of cases with two or more euploid blastocysts and one or more live birth. Therefore, iDAScore v1.0 may objectify embryologists' evaluations, but randomized controlled trials are required to assess its clinical value. [ABSTRACT FROM AUTHOR]

Abstract While CRISPR‐Cas defence mechanisms have been studied on a population level, their temporal dynamics and variability in individual cells have remained unknown. Using a microfluidic device, time‐lapse microscopy and mathematical modelling, we studied invader clearance in Escherichia coli across multiple generations. We observed that CRISPR interference is fast with a narrow distribution of clearance times. In contrast, for invaders with escaping PAM mutations we found large cell‐to‐cell variability, which originates from primed CRISPR adaptation. Faster growth and cell division and higher levels of Cascade increase the chance of clearance by interference, while slower growth is associated with increased chances of clearance by priming. Our findings suggest that Cascade binding to the mutated invader DNA, rather than spacer integration, is the main source of priming heterogeneity. The highly stochastic nature of primed CRISPR adaptation implies that only subpopulations of bacteria are able to respond quickly to invading threats. We conjecture that CRISPR‐Cas dynamics and heterogeneity at the cellular level are crucial to understanding the strategy of bacteria in their competition with other species and phages.

Abstract Background Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the end point. To the best of our knowledge, there is no predictive model that uses the outcome of live birth as the predictive end point. Can a deep learning model predict the probability of live birth from time-lapse system? Methods This study retrospectively analyzed the time-lapse data and live birth outcomes of embryos samples from January 2018 to November 2019. We used the SGD optimizer with an initial learning rate of 0.025 and cosine learning rate reduction strategy. The network is randomly initialized and trained for 200 epochs from scratch. The model is quantitively evaluated over a hold-out test and a 5-fold cross-validation by the average area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results The deep learning model was able to predict live birth outcomes from time-lapse images with an AUC of 0.968 in 5-fold stratified cross-validation. Conclusions This research reported a deep learning model that predicts the live birth outcome of a single blastocyst transfer. This efficient model for predicting the outcome of live births can automatically analyze the time-lapse images of the patient’s embryos without the need for manual embryo annotation and evaluation, and then give a live birth prediction score for each embryo, and sort the embryos by the predicted value.

Background: Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the end point. To the best of our knowledge, there is no predictive model that uses the outcome of live birth as the predictive end point. Can a deep learning model predict the probability of live birth from time-lapse system?Methods: This study retrospectively analyzed the time-lapse data and live birth outcomes of embryos samples from January 2018 to November 2019. We used the SGD optimizer with an initial learning rate of 0.025 and cosine learning rate reduction strategy. The network is randomly initialized and trained for 200 epochs from scratch. The model is quantitively evaluated over a hold-out test and a 5-fold cross-validation by the average area under the curve (AUC) of the receiver operating characteristic (ROC) curve.Results: The deep learning model was able to predict live birth outcomes from time-lapse images with an AUC of 0.968 in 5-fold stratified cross-validation.Conclusions: This research reported a deep learning model that predicts the live birth outcome of a single blastocyst transfer. This efficient model for predicting the outcome of live births can automatically analyze the time-lapse images of the patient's embryos without the need for manual embryo annotation and evaluation, and then give a live birth prediction score for each embryo, and sort the embryos by the predicted value. [ABSTRACT FROM AUTHOR]

The enhancement of photodynamic therapy (PDT) effectiveness by combining it with other treatment modalities and improved drug delivery has become an interesting field in cancer research. We have prepared and characterized nanoliposomes containing the chemotherapeutic drug irinotecan (CPT11lip), the photodynamic agent protoporphyrin IX (PpIXlip), or their combination (CPT11-PpIXlip). The effects of individual and bimodal (chemo-phototherapeutic) treatments on HeLa cells have been studied by a combination of biological and photophysical studies. Bimodal treatments show synergistic cytotoxic effects on HeLa cells at relatively low doses of PpIX/PDT and CPT11. Mechanistic cell inactivation studies revealed mitotic catastrophe, apoptosis, and senescence contributions. The enhanced anticancer activity is due to a sustained generation of reactive oxygen species, which increases the number of double-strand DNA breaks. Bimodal chemo-phototherapeutic liposomes may have a very promising future in oncological therapy, potentially allowing a reduction in the CPT11 concentration required to achieve a therapeutic effect and overcoming resistance to individual cancer treatments. [ABSTRACT FROM AUTHOR]

Abstract Background Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate. Results We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view. Conclusions ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar .

During renewal of the intestine, cells are continuously generated by proliferation. Proliferation and differentiation must be tightly balanced, as any bias toward proliferation results in uncontrolled exponential growth. Yet, the inherently stochastic nature of cells raises the question how such fluctuations are limited. We used time-lapse microscopy to track all cells in crypts of growing mouse intestinal organoids for multiple generations, allowing full reconstruction of the underlying lineage dynamics in space and time. Proliferative behavior was highly symmetric between sister cells, with both sisters either jointly ceasing or continuing proliferation. Simulations revealed that such symmetric proliferative behavior minimizes cell number fluctuations, explaining our observation that proliferating cell number remained constant even as crypts increased in size considerably. Proliferative symmetry did not reflect positional symmetry but rather lineage control through the mother cell. Our results indicate a concrete mechanism to balance proliferation and differentiation with minimal fluctuations that may be broadly relevant for other tissues.

Cells are inherently dynamic, whether they are responding to environmental conditions or simply at equilibrium, with biomolecules constantly being made and destroyed. Due to their small volumes, the chemical reactions inside cells are stochastic, such that genetically identical cells display heterogeneous behaviors and gene expression profiles. Studying these dynamic processes is challenging, but the development of microfluidic methods enabling the tracking of individual prokaryotic cells with microscopy over long time periods under controlled growth conditions has led to many discoveries. This review focuses on the recent developments of one such microfluidic device nicknamed the mother machine. We overview the original device design, experimental setup, and challenges associated with this platform. We then describe recent methods for analyzing experiments using automated image segmentation and tracking. We further discuss modifications to the experimental setup that allow for time-varying environmental control, replicating batch culture conditions, cell screening based on their dynamic behaviors, and to accommodate a variety of microbial species. Finally, this review highlights the discoveries enabled by this technology in diverse fields, such as cell-size control, genetic mutations, cellular aging, and synthetic biology.

Mitochondria form highly dynamic populations in the cells of plants (and almost all eukaryotes). The characteristics and benefits of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently developed quantitative approach to reveal and analyse the physical and collective 'social' dynamics of mitochondria in an Arabidopsis msh1 mutant where the organelle DNA maintenance machinery is compromised. We use a newly created line combining the msh1 mutant with mitochondrially targeted green fluorescent protein (GFP), and characterize mitochondrial dynamics with a combination of single-cell time-lapse microscopy, computational tracking, and network analysis. The collective physical behaviour of msh1 mitochondria is altered from that of the wild type in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected, with greater potential efficiency for inter-organelle exchange—reflecting a potential compensatory mechanism for the genetic challenge to the mitochondrial DNA population, supporting more inter-organelle exchange. We find that these changes are similar to those observed in friendly , where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges, where physical dynamics of mitochondria may be altered to control the genetic structure of the mtDNA population. [ABSTRACT FROM AUTHOR]

The effects of electric fields (EFs) on various cell types have been thoroughly studied, and exhibit a well-known regulatory effect on cell processes, implicating their usage in several medical applications. While the specific effect exerted on cells is highly parameter-dependent, the majority of past research has focused primarily on low-frequency alternating fields (<1 kHz) and high-frequency fields (in the order of MHz). However, in recent years, low-intensity (1–3 V/cm) alternating EFs with intermediate frequencies (100–500 kHz) have been of topical interest as clinical treatments for cancerous tumours through their disruption of cell division and the mitotic spindle, which can lead to cell death. These aptly named tumour-treating fields (TTFields) have been approved by the FDA as a treatment modality for several cancers, such as malignant pleural mesothelioma and glioblastoma multiforme, demonstrating remarkable efficacy and a high safety profile. In this work, we report the results of in vitro experiments with HeLa and MCF-10A cells exposed to TTFields for 18 h, imaged in real time using live-cell imaging. Both studied cell lines were exposed to 100 kHz TTFields with a 1-1 duty cycle, which resulted in significant mitotic and cytokinetic arrest. In the experiments with HeLa cells, the effects of the TTFields' frequency (100 kHz vs. 200 kHz) and duty cycle (1-1 vs. 1-0) were also investigated. Notably, the anti-mitotic effect was stronger in the HeLa cells treated with 100 kHz TTFields. Additionally, it was found that single and two-directional TTFields (oriented orthogonally) exhibit a similar inhibitory effect on HeLa cell division. These results provide real-time evidence of the profound ability of TTFields to hinder the process of cell division by significantly delaying both the mitosis and cytokinesis phases of the cell cycle. [ABSTRACT FROM AUTHOR]

The phase separation and aggregation of proteins are hallmarks of many neurodegenerative diseases. These processes can be studied in living cells using fluorescent protein constructs and quantitative live-cell imaging techniques, such as fluorescence recovery after photobleaching (FRAP) or the related fluorescence loss in photobleaching (FLIP). While the acquisition of FLIP images is straightforward on most commercial confocal microscope systems, the analysis and computational modeling of such data is challenging. Here, a novel model-free method is presented, which resolves complex spatiotemporal fluorescence-loss kinetics based on dynamic-mode decomposition (DMD) of FLIP live-cell image sequences. It is shown that the DMD of synthetic and experimental FLIP image series (DMD-FLIP) allows for the unequivocal discrimination of subcellular compartments, such as nuclei, cytoplasm, and protein condensates based on their differing transport and therefore fluorescence loss kinetics. By decomposing fluorescence-loss kinetics into distinct dynamic modes, DMD-FLIP will enable researchers to study protein dynamics at each time scale individually. Furthermore, it is shown that DMD-FLIP is very efficient in denoising confocal time series data. Thus, DMD-FLIP is an easy-to-use method for the model-free detection of barriers to protein diffusion, of phase-separated protein assemblies, and of insoluble protein aggregates. It should, therefore, find wide application in the analysis of protein transport and aggregation, in particular in relation to neurodegenerative diseases and the formation of protein condensates in living cells. [ABSTRACT FROM AUTHOR]

Time-lapse fluorescence imaging coupled to micro-irradiation devices provides information on the kinetics of DNA repair protein accumulation, from a few seconds to several minutes after irradiation. Charged-particle microbeams are valuable tools for such studies since they provide a way to selectively irradiate micrometric areas within a cell nucleus, control the dose and the micro-dosimetric quantities by means of advanced detection systems and Monte Carlo simulations and monitor the early cell response by means of beamline microscopy. We used the charged-particle microbeam installed at the AIFIRA facility to perform micro-irradiation experiments and measure the recruitment kinetics of two proteins involved in DNA signaling and repair pathways following exposure to protons and α-particles. We developed and validated image acquisition and processing methods to enable a systematic study of the recruitment kinetics of GFP-XRCC1 and GFP-RNF8. We show that XRCC1 is recruited to DNA damage sites a few seconds after irradiation as a function of the total deposited energy and quite independently of the particle LET. RNF8 is recruited to DNA damage sites a few minutes after irradiation and its recruitment kinetics depends on the particle LET.

While CRISPR‐Cas defence mechanisms have been studied on a population level, their temporal dynamics and variability in individual cells have remained unknown. Using a microfluidic device, time‐lapse microscopy and mathematical modelling, we studied invader clearance in Escherichia coli across multiple generations. We observed that CRISPR interference is fast with a narrow distribution of clearance times. In contrast, for invaders with escaping PAM mutations we found large cell‐to‐cell variability, which originates from primed CRISPR adaptation. Faster growth and cell division and higher levels of Cascade increase the chance of clearance by interference, while slower growth is associated with increased chances of clearance by priming. Our findings suggest that Cascade binding to the mutated invader DNA, rather than spacer integration, is the main source of priming heterogeneity. The highly stochastic nature of primed CRISPR adaptation implies that only subpopulations of bacteria are able to respond quickly to invading threats. We conjecture that CRISPR‐Cas dynamics and heterogeneity at the cellular level are crucial to understanding the strategy of bacteria in their competition with other species and phages. Synopsis: Time‐lapse microscopy combined with computational modeling reveals new insights into the single‐cell biology of CRISPR‐Cas defense during invader DNA clearance in E. coli. CRISPR adaptation and interference over multiple generations can be tracked using time‐lapse microscopy.Direct interference is fast and efficient, while priming shows large variations between cells.Slower cell growth leads to earlier spacer acquisition within a population.Mathematical modeling shows reduced Cascade binding affinity for the target drives variation between cells during priming. [ABSTRACT FROM AUTHOR]

As they proliferate, living cells undergo transitions between specific molecularly and developmentally distinct states. Despite the functional centrality of these transitions in multicellular organisms, it has remained challenging to determine which transitions occur and at what rates without perturbations and cell engineering. Here, we introduce kin correlation analysis (KCA) and show that quantitative cell-state transition dynamics can be inferred, without direct observation, from the clustering of cell states on pedigrees (lineage trees). Combining KCA with pedigrees obtained from time-lapse imaging and endpoint single-molecule RNA-fluorescence in situ hybridization (RNA-FISH) measurements of gene expression, we determined the cell-state transition network of mouse embryonic stem (ES) cells. This analysis revealed that mouse ES cells exhibit stochastic and reversible transitions along a linear chain of states ranging from 2C-like to epiblast-like. Our approach is broadly applicable and may be applied to systems with irreversible transitions and non-stationary dynamics, such as in cancer and development.

Background: Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells ("persisters"), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate. Results: We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view. Conclusions: ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar. [ABSTRACT FROM AUTHOR]

Uropathogenic Escherichia coli (UPEC) proliferate within superficial bladder umbrella cells to form intracellular bacterial communities (IBCs) during early stages of urinary tract infections. However, the dynamic responses of IBCs to host stresses and antibiotic therapy are difficult to assess in situ. We develop a human bladder-chip model wherein umbrella cells and bladder microvascular endothelial cells are co-cultured under flow in urine and nutritive media respectively, and bladder filling and voiding mimicked mechanically by application and release of linear strain. Using time-lapse microscopy, we show that rapid recruitment of neutrophils from the vascular channel to sites of infection leads to swarm and neutrophil extracellular trap formation but does not prevent IBC formation. Subsequently, we tracked bacterial growth dynamics in individual IBCs through two cycles of antibiotic administration interspersed with recovery periods which revealed that the elimination of bacteria within IBCs by the antibiotic was delayed, and in some instances, did not occur at all. During the recovery period, rapid proliferation in a significant fraction of IBCs reseeded new foci of infection through bacterial shedding and host cell exfoliation. These insights reinforce a dynamic role for IBCs as harbors of bacterial persistence, with significant consequences for non-compliance with antibiotic regimens.

Background: Early prediction of time-lapse microscopy experiments enables intelligent data management and decision-making. Aim: Using time-lapse data of HepG2 cells exposed to lipid nanoparticles loaded with mRNA for expression of GFP, the authors hypothesized that it is possible to predict in advance whether a cell will express GFP. Methods: The first modeling approach used a convolutional neural network extracting per-cell features at early time points. These features were then combined and explored using either a long short-term memory network (approach 2) or time series feature extraction and gradient boosting machines (approach 3). Results: Accounting for the temporal dynamics significantly improved performance. Conclusion: The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high-content imaging. [ABSTRACT FROM AUTHOR]

The phase separation and aggregation of proteins are hallmarks of many neurodegenerative diseases. These processes can be studied in living cells using fluorescent protein constructs and quantitative live-cell imaging techniques, such as fluorescence recovery after photobleaching (FRAP) or the related fluorescence loss in photobleaching (FLIP). While the acquisition of FLIP images is straightforward on most commercial confocal microscope systems, the analysis and computational modeling of such data is challenging. Here, a novel model-free method is presented, which resolves complex spatiotemporal fluorescence-loss kinetics based on dynamic-mode decomposition (DMD) of FLIP live-cell image sequences. It is shown that the DMD of synthetic and experimental FLIP image series (DMD-FLIP) allows for the unequivocal discrimination of subcellular compartments, such as nuclei, cytoplasm, and protein condensates based on their differing transport and therefore fluorescence loss kinetics. By decomposing fluorescence-loss kinetics into distinct dynamic modes, DMD-FLIP will enable researchers to study protein dynamics at each time scale individually. Furthermore, it is shown that DMD-FLIP is very efficient in denoising confocal time series data. Thus, DMD-FLIP is an easy-to-use method for the model-free detection of barriers to protein diffusion, of phase-separated protein assemblies, and of insoluble protein aggregates. It should, therefore, find wide application in the analysis of protein transport and aggregation, in particular in relation to neurodegenerative diseases and the formation of protein condensates in living 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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for the Study of Reproduction.)

Particulate matter (PM) is a general atmospheric pollutant released into the air by an anthropogenic and naturally derived mixture of substances. Current studies indicate that fine dust can result in different health defects, including endothelial dysfunction, asthma, lung cancer, cardiovascular diseases, uterine leiomyoma, deterioration in sperm quality, and overall birth impairment. However, the most prominent effects of PM10 (diameter < 10 μM) exposure on the female reproductive system, especially with respect to oocyte maturation, remain unclear. In the present study, maturing mouse oocytes were treated with PM10 and the phenotypes of the resulting toxic effects were investigated. Exposure to PM10 led to impairment of maturation capacity by inducing cell cycle arrest and blocking normal polar body extrusion during in vitro maturation and activation of fertilization of mouse oocytes. Additionally, defects in tubulin formation and DNA alignment were observed in PM10-treated oocytes during metaphase I to anaphase/telophase I transition. Moreover, PM10 induced reactive oxygen species generation, mitochondrial dysfunction, DNA damage, and early apoptosis. Taken together, these results indicate that PM10 exposure leads to a decline in oocyte quality and affects the subsequent embryonic development potential of mammalian oocytes.

We establish a murine lung-on-chip infection model and use time-lapse imaging to reveal the dynamics of host-Mycobacterium tuberculosis interactions at an air-liquid interface with a spatiotemporal resolution unattainable in animal models and to probe the direct role of pulmonary surfactant in early infection. Surfactant deficiency results in rapid and uncontrolled bacterial growth in both macrophages and alveolar epithelial cells. In contrast, under normal surfactant levels, a significant fraction of intracellular bacteria are non-growing. The surfactant-deficient phenotype is rescued by exogenous addition of surfactant replacement formulations, which have no effect on bacterial viability in the absence of host cells. Surfactant partially removes virulence-associated lipids and proteins from the bacterial cell surface. Consistent with this mechanism, the attenuation of bacteria lacking the ESX-1 secretion system is independent of surfactant levels. These findings may partly explain why smokers and elderly persons with compromised surfactant function are at increased risk of developing active tuberculosis.

Cells use molecular circuits to interpret and respond to extracellular cues, such as hormones and cytokines, which are often released in a temporally varying fashion. In this study, we combine microfluidics, time-lapse microscopy, and computational modeling to investigate how the type I interferon (IFN)-responsive regulatory network operates in single human cells to process repetitive IFN stimulation. We found that IFN-α pretreatments lead to opposite effects, priming versus desensitization, depending on input durations. These effects are governed by a regulatory network composed of a fast-acting positive feedback loop and a delayed negative feedback loop, mediated by upregulation of ubiquitin-specific peptidase 18 (USP18). We further revealed that USP18 upregulation can only be initiated at the G1/early S phases of cell cycle upon the treatment onset, resulting in heterogeneous and delayed induction kinetics in single cells. This cell cycle gating provides a temporal compartmentalization of feedback loops, enabling duration-dependent desensitization to repetitive stimulations.

Abstract Methicillin‐resistant Staphylococcus aureus (MRSA) is resistant to most β‐lactams due to the expression of an extra penicillin‐binding protein, PBP2a, with low β‐lactam affinity. It has long been known that heterologous expression of the PBP2a‐encoding mecA gene in methicillin‐sensitive S. aureus (MSSA) provides protection towards β‐lactams, however, some reports suggest that the degree of protection can vary between different β‐lactams. To test this more systematically, we introduced an IPTG‐inducible mecA into the MSSA laboratory strain RN4220. We confirm, by growth assays as well as single‐cell microfluidics time‐lapse microscopy experiments, that PBP2a expression protects against β‐lactams in S. aureus RN4220. By testing a panel of ten different β‐lactams, we conclude that there is also a great variation in the level of protection conferred by PBP2a. Expression of PBP2a resulted in an only fourfold increase in minimum inhibitory concentration (MIC) for imipenem, while a 32‐fold increase in MIC was observed for cefaclor and cephalexin. Interestingly, in our experimental setup, PBP2a confers the highest protection against cefaclor and cephalexin—two β‐lactams that are known to have a high specific affinity toward the transpeptidase PBP3 of S. aureus. Notably, using a single‐cell microfluidics setup we demonstrate a considerable phenotypic variation between cells upon β‐lactam exposure and show that mecA‐expressing S. aureus can survive β‐lactam concentrations much higher than the minimal inhibitory concentrations. We discuss possible explanations and implications of these results including important aspects regarding treatment of infection.

Featured Application: Novel method for Automated Cell Detection and Counting (ACDC) in time-lapse fluorescence microscopy. Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7 × speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96 , calculated against the manual cell counting, on the two tested datasets. [ABSTRACT FROM AUTHOR]

Purpose: To describe an interesting not previously described morphokinetic finding. Methods: Retrospective case report of a couple undergoing controlled ovarian stimulation (COS) followed by in vitro fertilization and blastocyst transfer. Results: We identified a unique finding of blastulation of a fertilized human zygote after conventional in vitro fertilization. The fertilized zygote did not show any clear cytokinesis until approximately 107 h post insemination, when it started dividing into a blastocyst. By 113 h post insemination, inner cell mass and trophectoderm cells could be clearly distinguished and the blastocyst was completely hatched by 136 h post insemination. Conclusion: Time-lapse systems offer more detailed observations of embryonic development. Here, we report an atypical development of an embryo that was not described previously. We hope to become an insightful discussion among peers and incentive the publication of such findings in the future. [ABSTRACT FROM AUTHOR]

Background: Cell invasion through extracellular matrix (ECM) is a critical step in tumor metastasis. To study cell invasion in vitro, the internal microenvironment can be simulated via the application of 3D models. Results: This study presents a method for 3D invasion examination using microcarrier-based spheroids. Cell invasiveness can be evaluated by quantifying cell dispersion in matrices or tracking cell movement through time-lapse imaging. It allows measuring of cell invasion and monitoring of dynamic cell behavior in three dimensions. Here we show different invasive capacities of several cell types using this method. The content and concentration of matrices can influence cell invasion, which should be optimized before large scale experiments. We also introduce further analysis methods of this 3D invasion assay, including manual measurements and homemade semi-automatic quantification. Finally, our results indicate that the position of spheroids in a matrix has a strong impact on cell moving paths, which may be easily overlooked by researchers and may generate false invasion results. Conclusions: In all, the microcarrier-based spheroids 3D model allows exploration of adherent cell invasion in a fast and highly reproducible way, and provides informative results on dynamic cell behavior in vitro. [ABSTRACT FROM AUTHOR]

The increasing interest for microfluidic devices in medicine and biology has opened the way to new time-lapse microscopy era where the amount of images and their acquisition time will become crucial. In this optic, new data analysis algorithms have to be developed in order to extract novel features of cell behavior and cell–cell interactions. In this brief article, we emphasize the potential strength of a new paradigm arising in the integration of microfluidic devices (i.e., organ on chip), time-lapse microscopy analysis, and machine learning approaches. Some snapshots of previous case studies in the context of immunotherapy are included as proof of concepts of the proposed strategies while a visionary description concludes the work foreseeing future research and applicative scenarios.

Embryonic stem (ES) cells are pluripotent cells widely used in cell therapy and tissue engineering. However, the broader clinical applications of ES cells are limited by their genomic instability and karyotypic abnormalities. Thus, understanding the mechanisms underlying ES cell karyotypic abnormalities is critical to optimizing their clinical use. In this study, we focused on proliferating human and mouse ES cells undergoing multipolar divisions. Specifically, we analyzed the frequency and outcomes of such divisions using a combination of time-lapse microscopy and cell tracking. This revealed that cells resulting from multipolar divisions were not only viable, but they also frequently underwent subsequent cell divisions. Our novel data also showed that in human and mouse ES cells, multipolar spindles allowed more robust escape from chromosome segregation control mechanisms than bipolar spindles. Considering the frequency of multipolar divisions in proliferating ES cells, it is conceivable that cell division errors underlie ES cell karyotypic instability. [ABSTRACT FROM AUTHOR]

The increasing interest for microfluidic devices in medicine and biology has opened the way to new time-lapse microscopy era where the amount of images and their acquisition time will become crucial. In this optic, new data analysis algorithms have to be developed in order to extract novel features of cell behavior and cell–cell interactions. In this brief article, we emphasize the potential strength of a new paradigm arising in the integration of microfluidic devices (i.e., organ on chip), time-lapse microscopy analysis, and machine learning approaches. Some snapshots of previous case studies in the context of immunotherapy are included as proof of concepts of the proposed strategies while a visionary description concludes the work foreseeing future research and applicative scenarios. [ABSTRACT FROM AUTHOR]

The derivation of input–output relationships in deep learning architectures is mostly a black-box process, in which uninformative or confounding factors might bias the classification results, with no clue for the user. However, the analysis of living cells requires extracting latent space representations with interpretable meaning, which can be investigated not only for classification but mainly for understanding purposes. Using the properties of variational autoencoders in deriving unsupervised regular space representations, we propose a novel Supervised-Source-Separation Variational Autoencoder (S3-VAE) algorithm capable of guiding the encoding process of images toward a more effective class-separability. The scope has been reached, under the assumption of full-covariance Gaussian posterior, by introducing a term in the cost function that forces class-dependent sources of variations toward a one-hot encoding. The proposed approach has been designed for an automatic platform for single-cell biological investigations based on time-lapse microscopy, in which a dedicated video processing pipeline has been designed, as an additional contribution, to reduce the effects of confounding factors on the S3-VAE representations. The results obtained in a classification scenario with artificially generated phantom cells and with experimental data of human prostate cells, in non-neoplastic, neoplastic and metastatic neoplastic conditions, show that S3-VAE-based latent representations allow for investigation and recognition of the morphological differences among classes. Comparative analysis with other benchmark deep learning architectures demonstrates the effectiveness of the proposed approach and shows that S3-VAE is capable of achieving high performance in discriminating cell lines based on phenotypic variations. [ABSTRACT FROM AUTHOR]

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.