Diagnostic or prognostic? Decoding the role of embryo selection on in vitro fertilization treatment outcomes

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.

WISE: whole-scenario embryo identification using self-supervised learning encoder in IVF

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.

A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development

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.

Time-Lapse Imaging of Migrating Neurons and Glial Progenitors in Embryonic Mouse Brain Slices

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.

Embryo ranking agreement between embryologists and artificial intelligence algorithms

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.

Image restoration of degraded time-lapse microscopy data mediated by near-infrared imaging

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 (IR2). Notably, the networks are remarkably robust over a wide range of developmental times. We employ IR2 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, IR2 is poised to extend live imaging to depths otherwise inaccessible.

Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine

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.

Considerations for future modification of The Association for the Study of Reproductive Biology embryo grading system incorporating time-lapse observations

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.

Atypical initial cleavage patterns minimally impact rhesus macaque in vitro embryo morphokinetics and embryo outgrowth development†

Embryo morphokinetic analysis through time-lapse embryo imaging is envisioned as a method to improve selection of developmentally competent embryos. Morphokinetic analysis could be utilized to evaluate the effects of experimental manipulation on pre-implantation embryo development. The objectives of this study were to establish a normative morphokinetic database for in vitro fertilized rhesus macaque embryos and to assess the impact of atypical initial cleavage patterns on subsequent embryo development and formation of embryo outgrowths. The cleavage pattern and the timing of embryo developmental events were annotated retrospectively for unmanipulated in vitro fertilized rhesus macaque blastocysts produced over four breeding seasons. Approximately 50% of the blastocysts analyzed had an abnormal early cleavage event. The time to the initiation of embryo compaction and the time to completion of hatching was significantly delayed in blastocysts with an abnormal early cleavage event compared to blastocysts that had cleaved normally. Embryo hatching, attachment to an extracellular matrix, and growth during the implantation stage in vitro was not impacted by the initial cleavage pattern. These data establish normative morphokinetic parameters for in vitro fertilized rhesus macaque embryos and suggest that cleavage anomalies may not impact embryo implantation rates following embryo transfer.

The effect of embryo selection using time-lapse monitoring on IVF/ICSI outcomes: A systematic review and meta-analysis

AIM

To explore the effect of embryo selection using the time-lapse monitoring (TLM) system compared with conventional morphological selection (CMS) on in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) outcomes.

METHODS

We searched PubMed, Ovid-Embase, and The Cochrane Library for the following studies: At Comparison 1, embryo selection using TLM images in a TLM incubator based on morphology versus embryo selection using CMS in a conventional incubator based on morphology; at Comparison 2, embryo selection using TLM based on morphokinetics versus embryo selection using CMS based on morphology. The primary outcomes were the live birth rate (LBR), ongoing pregnancy rate (OPR), clinical pregnancy rate (CPR), and implantation rate (IR), and the secondary outcome was the miscarriage rate (MR).

RESULTS

A total of 14 randomized control trials (RCTs) were included. Both based on morphology, TLM incubators increased the IR (risk ratio [RR]: 1.10; 95% confidence interval [CI]: 1.01, 1.18; I2  = 0%, moderate-quality evidence) compared to conventional incubators. Low- to moderate-quality evidence suggests that TLM incubators did not improve LBR, OPR, CPR, and MR compared to conventional incubators. In addition, low- to moderate-quality evidence indicates that embryo selection using TLM based on morphokinetics did not improve LBR, OPR, CPR, IR, or MR compared to CMS based on morphology.

CONCLUSIONS

Low- to moderate-quality evidence suggests that neither TLM incubators nor embryo selection using TLM based on morphokinetics improved clinical outcomes (LBR, OPR, CPR, and MR) compared with CMS based on morphology. TLM is still an investigational procedure for IVF/ICSI practice.

Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy

OBJECTIVE

This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring.

DATA SOURCES

A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms.

STUDY ELIGIBILITY CRITERIA

Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916).

METHODS

Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist.

RESULTS

Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias.

CONCLUSION

The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.

ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells

Detecting and tracking multiple moving objects in a video is a challenging task. For living cells, the task becomes even more arduous as cells change their morphology over time, can partially overlap, and mitosis leads to new cells. Differently from fluorescence microscopy, label-free techniques can be easily applied to almost all cell lines, reducing sample preparation complexity and phototoxicity. In this study, we present ALFI, a dataset of images and annotations for label-free microscopy, made publicly available to the scientific community, that notably extends the current panorama of expertly labeled data for detection and tracking of cultured living nontransformed and cancer human cells. It consists of 29 time-lapse image sequences from HeLa, U2OS, and hTERT RPE-1 cells under different experimental conditions, acquired by differential interference contrast microscopy, for a total of 237.9 hours. It contains various annotations (pixel-wise segmentation masks, object-wise bounding boxes, tracking information). The dataset is useful for testing and comparing methods for identifying interphase and mitotic events and reconstructing their lineage, and for discriminating different cellular phenotypes.

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

MOTIVATION

Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner.

RESULTS

Our trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining.

AVAILABILITY AND IMPLEMENTATION

Open-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.

Evaluation of an automated dish preparation system for IVF and embryo culture using a mouse mode

Manual dish preparation for IVF in human fertility clinics or animal laboratories heavily relies on embryologists' experience, which can lead to occupational illness due to long-term and monotonous operation. Therefore, introducing an automated technique to replace traditional methods is crucial for improving working efficiency and reducing work burden for embryologists. In the current study in the mouse, both manual and automated methods were used to prepare IVF or embryo culture dishes. A one-way analysis of variance was conducted to compare several factors, including preparation time, qualified rates, media osmolality of dishes, fertilization rates, and embryonic development to assess the efficiency and potential of automated preparation. The results showed that automation system significantly reduced the required time and increased the efficiencies and qualified rates of dish preparation, especially for embryo culture dishes, without significantly altering medium osmolalities. There were no significant differences between two preparations in fertilization rates and embryo development in mice. Thus, automated dish preparation can improve working efficiency and qualified rates while maintaining fertilization rates and subsequent embryonic development without compromising osmolality stability of medium. It presents a superior alternative to manual preparation, reducing the workload of embryologists and facilitating the standardization of operational procedures.

DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy

Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell level with high temporal resolution. Successful application of single-cell time-lapse microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. However, segmentation and tracking of single cells remain challenging for the analysis of time-lapse microscopy images, in particular for widely available and non-toxic imaging modalities such as phase-contrast imaging. This work presents a versatile and trainable deep-learning model, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images with higher precision than existing models. We showcase the application of DeepSea by analyzing cell size regulation in embryonic stem cells.

Cost-efficient boundary-free surface patterning achieves high effective-throughput of time-lapse microscopy experiments

Time-lapse microscopy plays critical roles in the studies of cellular dynamics. However, setting up a time-lapse movie experiments is not only laborious but also with low output, mainly due to the cell-losing problem (i.e., cells moving out of limited field of view), especially in a long-time recording. To overcome this issue, we have designed a cost-efficient way that enables cell patterning on the imaging surfaces without any physical boundaries. Using mouse embryonic stem cells as an example system, we have demonstrated that our boundary-free patterned surface solves the cell-losing problem without disturbing their cellular phenotype. Statistically, the presented system increases the effective-throughput of time-lapse microscopy experiments by an order of magnitude.

A Flexible Chamber for Time-Lapse Live-Cell Imaging with Stimulated Raman Scattering Microscopy

Stimulated Raman scattering (SRS) microscopy is a label-free chemical imaging technology. Live-cell imaging with SRS has been demonstrated for many biological and biomedical applications. However, long-term time-lapse SRS imaging of live cells has not been widely adopted. SRS microscopy often uses a high numerical aperture (NA) water-immersion objective and a high NA oil-immersion condenser to achieve high-resolution imaging. In this case, the gap between the objective and the condenser is only a few millimeters. Therefore, most commercial stage-top environmental chambers cannot be used for SRS imaging because of their large thickness with a rigid glass cover. This paper describes the design and fabrication of a flexible chamber that can be used for time-lapse live-cell imaging with transmitted SRS signal detection on an upright microscope frame. The flexibility of the chamber is achieved by using a soft material - a thin natural rubber film. The new enclosure and chamber design can be easily added to an existing SRS imaging setup. The testing and preliminary results demonstrate that the flexible chamber system enables stable, long-term, time-lapse SRS imaging of live cells, which can be used for various bioimaging applications in the future.

Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques

BACKGROUND

Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditional method of manual embryo assessment is time-consuming and highly susceptible to inter- and intra-observer variability. Automation of this process results in more objective and accurate predictions.

METHOD

In this paper, we propose a novel methodology based on deep learning to automatically evaluate the morphological appearance of human embryos from time-lapse imaging. A supervised contrastive learning framework is implemented to predict embryo viability at day 4 and day 5, and an inductive transfer approach is applied to classify embryo quality at both times.

RESULTS

Results showed that both methods outperformed conventional approaches and improved state-of-the-art embryology results for an independent test set. The viability result achieved an accuracy of 0.8103 and 0.9330 and the quality results reached values of 0.7500 and 0.8001 for day 4 and day 5, respectively. Furthermore, qualitative results kept consistency with the clinical interpretation.

CONCLUSIONS

The proposed methods are up to date with the artificial intelligence literature and have been proven to be promising. Furthermore, our findings represent a breakthrough in the field of embryology in that they study the possibilities of embryo selection at day 4. Moreover, the grad-CAMs findings are directly in line with embryologists' decisions. Finally, our results demonstrated excellent potential for the inclusion of the models in clinical practice.

Time-lapse imaging using dual-color coded quantitative differential phase contrast microscopy

SIGNIFICANCE

Quantitative differential phase contrast (qDPC) microscopy enhances phase contrast by asymmetric illumination using partially coherent light and multiple intensity measurements. However, for live cell imaging, motion artifacts and image acquisition time are important issues. For live cell imaging, a large number of intensity measurements can limit the imaging quality and speed. The minimum number of intensity measurements in qDPC can greatly enhance performance for live imaging.

AIM

To obtain high-contrast, isotropic qDPC images with two intensity measurements and perform time-lapse imaging of biological samples.

APPROACH

Based on the color-coded design, a dual-color linear-gradient pupil is proposed to achieve isotropic phase contrast response with two intensity measurements. In our method, the purpose of designing a dual-color coded pupil is twofold: first, to obtain a linear amplitude gradient for asymmetric illumination, which is required to get a circular symmetry of transfer function, and second, to reduce the required number of frames for phase retrieval.

RESULTS

To demonstrate the imaging performance of our system, standard microlens arrays were used as samples. We performed time-lapse quantitative phase imaging of rat astrocytes under a low-oxygen environment. Detailed morphology and dynamic changes such as the apoptosis process and migration of cells were observed.

CONCLUSIONS

It is shown that dual-color linear-gradient pupils in qDPC can outperform half-circle and vortex pupils, and isotropic phase transfer function can be achieved with only two-axis measurements. The reduced number of frames helps in achieving faster imaging speed as compared to the typical qDPC system. The imaging performance of our system is evaluated by time-lapse imaging of rat astrocytes. Different morphological changes in cells during their life cycle were observed in terms of quantitative phase change values.

Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences

Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60–0.75 for KID embryos. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation.

Deep Reinforcement Learning-Based Progressive Sequence Saliency Discovery Network for Mitosis Detection In Time-Lapse Phase-Contrast Microscopy Images

Mitosis detection plays an important role in the analysis of cell status and behavior and is therefore widely utilized in many biological research and medical applications. In this article, we propose a deep reinforcement learning-based progressive sequence saliency discovery network (PSSD)for mitosis detection in time-lapse phase contrast microscopy images. By discovering the salient frames when cell state changes in the sequence, PSSD can more effectively model the mitosis process for mitosis detection. We formulate the discovery of salient frames as a Markov Decision Process (MDP)that progressively adjusts the selection positions of salient frames in the sequence, and further leverage deep reinforcement learning to learn the policy in the salient frame discovery process. The proposed method consists of two parts: 1)the saliency discovery module that selects the salient frames from the input cell image sequence by progressively adjusting the selection positions of salient frames; 2)the mitosis identification module that takes a sequence of salient frames and performs temporal information fusion for mitotic sequence classification. Since the policy network of the saliency discovery module is trained under the guidance of the mitosis identification module, PSSD can comprehensively explore the salient frames that are beneficial for mitosis detection. To our knowledge, this is the first work to implement deep reinforcement learning to the mitosis detection problem. In the experiment, we evaluate the proposed method on the largest mitosis detection dataset, C2C12-16. Experiment results show that compared with the state-of-the-arts, the proposed method can achieve significant improvement for both mitosis identification and temporal localization on C2C12-16.

Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos

With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility treatment, in which only the embryos that were transferred to the female uterus may have labels of pregnancy. In this paper, we apply a self-supervised video alignment method known as temporal cycle-consistency (TCC) on 38176 time-lapse videos of developing embryos, of which 14550 were labeled. We show how TCC can be used to extract temporal similarities between embryo videos and use these for predicting pregnancy likelihood. Our temporal similarity method outperforms the time alignment measurement (TAM) with an area under the receiver operating characteristic (AUC) of 0.64 vs. 0.56. Compared to existing embryo evaluation models, it places in between a pure temporal and a spatio-temporal model that both require manual annotations. Furthermore, we use TCC for transfer learning in a semi-supervised fashion and show significant performance improvements compared to standard supervised learning, when only a small subset of the dataset is labeled. Specifically, two variants of transfer learning both achieve an AUC of 0.66 compared to 0.63 for supervised learning when 16% of the dataset is labeled.

Intracellular Trafficking of Cationic Carbon Dots in Cancer Cell Lines MCF-7 and HeLa—Time Lapse Microscopy, Concentration-Dependent Uptake, Viability, DNA Damage, and Cell Cycle Profile

Fluorescent carbon dots (CDs) are potential tools for the labeling of cells with many advantages such as photostability, multicolor emission, small size, rapid uptake, biocompatibility, and easy preparation. Affinity towards organelles can be influenced by the surface properties of CDs which affect the interaction with the cell and cytoplasmic distribution. Organelle targeting by carbon dots is promising for anticancer treatment; thus, intracellular trafficking and cytotoxicity of cationic CDs was investigated. Based on our previous study, we used quaternized carbon dots (QCDs) for treatment and monitoring the behavior of two human cancer cell MCF-7 and HeLa lines. We found similarities between human cancer cells and mouse fibroblasts in the case of QCDs uptake. Time lapse microscopy of QCDs-labeled MCF-7 cells showed that cells are dying during the first two hours, faster at lower doses than at higher ones. QCDs at a concentration of 100 µg/mL entered into the nucleus before cellular death; however, at a dose of 200 µg/mL, blebbing of the cellular membrane occurred, with a subsequent penetration of QCDs into the nuclear area. In the case of HeLa cells, the dose-depended effect did not happen; however, the labeled cells were also dying in mitosis and genotoxicity occurred nearly at all doses. Moreover, contrasted intracellular compartments, probably mitochondria, were obvious after 24 h incubation with 100 µg/mL of QCDs. The levels of reactive oxygen species (ROS) slightly increased after 24 h, depending on the concentration, thus the genotoxicity was likely evoked by the nanomaterial. A decrease in viability did not reach IC 50 as the DNA damage was probably partly repaired in the prolonged G0/G1 phase of the cell cycle. Thus, the defects in the G2/M phase may have allowed a damaged cell to enter mitosis and undergo apoptosis. The anticancer effect in both cell lines was manifested mainly through genotoxicity.

Blood stem cell PU.1 upregulation is a consequence of differentiation without fast autoregulation

Transcription factors (TFs) regulate cell fates, and their expression must be tightly regulated. Autoregulation is assumed to regulate many TFs' own expression to control cell fates. Here, we manipulate and quantify the (auto)regulation of PU.1, a TF controlling hematopoietic stem and progenitor cells (HSPCs), and correlate it to their future fates. We generate transgenic mice allowing both inducible activation of PU.1 and noninvasive quantification of endogenous PU.1 protein expression. The quantified HSPC PU.1 dynamics show that PU.1 up-regulation occurs as a consequence of hematopoietic differentiation independently of direct fast autoregulation. In contrast, inflammatory signaling induces fast PU.1 up-regulation, which does not require PU.1 expression or its binding to its own autoregulatory enhancer. However, the increased PU.1 levels induced by inflammatory signaling cannot be sustained via autoregulation after removal of the signaling stimulus. We conclude that PU.1 overexpression induces HSC differentiation before PU.1 up-regulation, only later generating cell types with intrinsically higher PU.1.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

Visualizing and Quantifying Data from Time-Lapse Imaging Experiments

One obvious feature of life is that it is highly dynamic. The dynamics can be captured by movies that are made by acquiring images at regular time intervals, a method that is also known as time-lapse imaging. Looking at movies is a great way to learn more about the dynamics in cells, tissue, and organisms. However, science is different from Netflix, in that it aims for a quantitative understanding of the dynamics. The quantification is important for the comparison of dynamics and to study effects of perturbations. Here, we provide detailed processing and analysis methods that we commonly use to analyze and visualize our time-lapse imaging data. All methods use freely available open-source software and use example data that is available from an online data repository. The step-by-step guides together with example data allow for fully reproducible workflows that can be modified and adjusted to visualize and quantify other data from time-lapse imaging experiments.

Analysis and quantification of female and male contributions to the first stages of embryonic kinetics: study from a time-lapse system

PURPOSE

The few studies that examined the effect of male and/or female features on early embryo development, notably using the time-lapse system (TL), reported conflicting results. This can be explained by the small number of studies using an adapted model.

METHODS

We used two original designs to study the female and male effects on embryo development: (1) based on embryos from donor oocytes (TL-DO), and (2) from donor sperm (TL-DS). Firstly, we analyzed the female and male similarities using an ad hoc intraclass correlation coefficient (ICC), then we completed the analysis with a multivariable model to assess the association between both male and female factors, and early embryo kinetics. A total of 572 mature oocytes (TL-DO: 293; TL-DS: 279), fertilized by intracytoplasmic sperm injection (ICSI) and incubated in a TL (Embryoscope®) were included from March 2013 to April 2019; 429 fertilized oocytes (TL-DO: 212; TL-DS: 217) were assessed. The timings of the first 48 h have been analyzed.

RESULTS

The similarities in the timings thought to be related to the female component were significant: (ICC in both DO-DS designs respectively: tPB2: 9-18%; tPNa: 16-21%; tPNf: 40-26%; t2: 38-24%; t3: 15-20%; t4: 21-32%). Comparatively, those related to male were lower. Surprisingly after multivariable analyses, no intrinsic female factors were clearly identified. However, in TL-DO design, oligozoospermia was associated with a tendency to longer timings, notably for tPB2 (p = 0.026).

CONCLUSION

This study quantifies the role of the oocyte in the first embryo cleavages, but without identified specific female factors. However, it also highlights that sperm may have an early embryonic effect.

Spatiotemporal distribution of PKCα, Cdc42, and Rac1 before directed cell migration

Cdc42 is a key factor in directed cell migration and accumulates at the leading edge of migrating cells. However, what kind of proteins control Cdc42 and when is unclear. After mechanical wounding, protein kinase C α (PKCα), a conventional PKC isozyme, begins to accumulate at the edges of cells adjacent to the wounded cells (WCs). In this study, we hypothesized that PKCα may be implicated in directed cell migration at an early stage before Cdc42 controls the migration. We focused on the spatiotemporal distribution of PKCα, Cdc42, and Rac1 before cell migration. After wounding, at the edges of cells adjacent to the WCs, PKCα accumulation, Cdc42 accumulation, Rac1 accumulation, and filopodia formation occurred in that order. The PKCα inhibitor suppressed Cdc42 accumulation at the cell edges. These results suggest that inhibition of PKCα activity inhibits cell migration. In addition, it is not Cdc42 but PKCα that may decide the direction of cell migration.

Optogenetic axon guidance in embryonic zebrafish

Axons form the long-range connections of biological neuronal networks, which are built through the developmental process of axon guidance. Here, we describe a protocol to precisely and non-invasively control axonal growth trajectories in live zebrafish embryos using focal light activation of a photoactivatable Rac1. We outline techniques for photostimulation, time-lapse imaging, and immunohistochemistry. These approaches enable engineering of long-range axonal circuitry or repair of defective circuits in living zebrafish, despite a milieu of competing endogenous signals and repulsive barriers. For complete details on the use and execution of this protocol, please refer to Harris et al. (2020).

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

Body mass index, not race, may be associated with an alteration in early embryo morphokinetics during in vitro fertilization

OBJECTIVE

To assess the relationship between maternal body mass index (BMI) and embryo morphokinetics on time-lapse microscopy (TLM).

DESIGN

Retrospective cohort study.

METHODS

All IVF cycles between June 2015 and April 2017 were reviewed. Female BMI prior to egg retrieval was collected through chart review. BMI (kg/m2) classification included underweight (< 18.5), normal weight (18.5-25), overweight (25-30), and obese (≥ 30). Embryos' morphokinetic parameters were assessed with TLM and included time to syngamy, 2-cell, 3-cell, 4-cell, and 8-cell. A generalized linear mixed model was used to control for potential confounders and multiple embryos resulting from a single IVF cycle.

RESULTS

A total of 2150 embryos from 589 IVF cycles were reviewed and included in the analysis. Classification based on BMI was as follows: underweight (N = 56), normal weight (N = 1252), overweight (N = 502), and obese (N = 340). After adjusting for race and use of intracytoplasmic sperm injection, the mean time to the 8-cell stage in the underweight group was 4.3 (95% CI: - 8.31, - 0.21) h less than in the normal weight group (P = 0.025) and 4.6 (95% CI: - 8.8, - 0.21) h less than in the obese group (p = 0.022). No significant difference was noted between race and TLM after controlling for possible confounders.

CONCLUSIONS

Embryos from underweight women were demonstrated to have a faster time to the 8-cell stage than normal weight or obese women. No significant difference was noted for race. This study demonstrates that weight can be a factor contributing to embryo development as observed with TLM.

Rational design of pyrrole derivatives with aggregation-induced phosphorescence characteristics for time-resolved and two-photon luminescence imaging

Pure organic room-temperature phosphorescent (RTP) materials have been suggested to be promising bioimaging materials due to their good biocompatibility and long emission lifetime. Herein, we report a class of RTP materials. These materials are developed through the simple introduction of an aromatic carbonyl to a tetraphenylpyrrole molecule and also exhibit aggregation-induced emission (AIE) properties. These molecules show non-emission in solution and purely phosphorescent emission in the aggregated state, which are desirable properties for biological imaging. Highly crystalline nanoparticles can be easily fabricated with a long emission lifetime (20 μs), which eliminate background fluorescence interference from cells and tissues. The prepared nanoparticles demonstrate two-photon absorption characteristics and can be excited by near infrared (NIR) light, making them promising materials for deep-tissue optical imaging. This integrated aggregation-induced phosphorescence (AIP) strategy diversifies the existing pool of bioimaging agents to inspire the development of bioprobes in the future.

A 3-Dimensional Coculture Model to Visualize and Monitor Interaction Between Pancreatic Cancer and Islet β Cells

OBJECTIVES

To facilitate exploring a link between pancreatic ductal adenocarcinoma (PDAC) and diabetes mellitus, we constructed a novel 3-dimensional (3D) in vitro coculturing system for studying interactions between PDAC and islet cells.

METHODS

Adopting a 3D rotary cell culture system, we have cocultured several PDAC cell lines and MIN6 islet β cells. The cellular morphology and viability of both cell types were investigated by time-lapse imaging, confocal and scanning electron microscopy, and immunohistochemistry.

RESULTS

The developed coculture method enabled the formation of 3D PDAC and β-cell spheroids (pseudo islets). We showed that surface morphology and growth of cultured cells mimicked their in vivo appearance. In addition, the coculture demonstrated the affinity of the PDAC cells to grow around and invade the pseudo islets.

CONCLUSIONS

Using rotary cell culture system, we have established a simple in vitro 3D pancreatic model. It is a flexible culture system that can easily be expanded with the addition of various stromal/neural components to further mimic in vivo conditions, thus enabling holistic investigation of the endocrine and exocrine pancreas.

Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motifs on promoter state dynamics

Genes are expressed in stochastic transcriptional bursts linked to alternating active and inactive promoter states. A major challenge in transcription is understanding how promoter composition dictates bursting, particularly in multicellular organisms. We investigate two key Drosophila developmental promoter motifs, the TATA box (TATA) and the Initiator (INR). Using live imaging in Drosophila embryos and new computational methods, we demonstrate that bursting occurs on multiple timescales ranging from seconds to minutes. TATA-containing promoters and INR-containing promoters exhibit distinct dynamics, with one or two separate rate-limiting steps respectively. A TATA box is associated with long active states, high rates of polymerase initiation, and short-lived, infrequent inactive states. In contrast, the INR motif leads to two inactive states, one of which relates to promoter-proximal polymerase pausing. Surprisingly, the model suggests pausing is not obligatory, but occurs stochastically for a subset of polymerases. Overall, our results provide a rationale for promoter switching during zygotic genome activation.

End-to-end deep learning for recognition of ploidy status using time-lapse videos

PURPOSE

Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video.

METHODS

By randomly dividing the dataset of time-lapse videos with known outcome of preimplantation genetic testing for aneuploidy (PGT-A), a deep learning model on raw videos was trained by the 80% dataset, and used to test the remaining 20%, by feeding time-lapse videos as input and the PGT-A prediction as output. The performance was measured by an average area under the curve (AUC) of the receiver operating characteristic curve.

RESULT(S)

With 690 sets of time-lapse video image, combined with PGT-A results, our deep learning model has achieved an AUC of 0.74 from the test dataset (138 videos), in discriminating between aneuploid embryos (group 1) and others (group 2, including euploid and mosaic embryos).

CONCLUSION

Our model demonstrated a proof of concept and potential in recognizing the ploidy status of tested embryos. A larger scale and further optimization on the exclusion criteria would be included in our future investigation, as well as prospective approach.

Hindbrain neuropore tissue geometry determines asymmetric cell-mediated closure dynamics in mouse embryos

Gap closure is a common morphogenetic process. In mammals, failure to close the embryonic hindbrain neuropore (HNP) gap causes fatal anencephaly. We observed that surface ectoderm cells surrounding the mouse HNP assemble high-tension actomyosin purse strings at their leading edge and establish the initial contacts across the embryonic midline. Fibronectin and laminin are present, and tensin 1 accumulates in focal adhesion-like puncta at this leading edge. The HNP gap closes asymmetrically, faster from its rostral than caudal end, while maintaining an elongated aspect ratio. Cell-based physical modeling identifies two closure mechanisms sufficient to account for tissue-level HNP closure dynamics: purse-string contraction and directional cell motion implemented through active crawling. Combining both closure mechanisms hastens gap closure and produces a constant rate of gap shortening. Purse-string contraction reduces, whereas crawling increases gap aspect ratio, and their combination maintains it. Closure rate asymmetry can be explained by asymmetric embryo tissue geometry, namely a narrower rostral gap apex, whereas biomechanical tension inferred from laser ablation is equivalent at the gaps' rostral and caudal closure points. At the cellular level, the physical model predicts rearrangements of cells at the HNP rostral and caudal extremes as the gap shortens. These behaviors are reproducibly live imaged in mouse embryos. Thus, mammalian embryos coordinate cellular- and tissue-level mechanics to achieve this critical gap closure event.

Statistics of pathogenic bacteria in the search of host cells

A crucial phase in the infection process, which remains poorly understood, is the localization of suitable host cells by bacteria. It is often assumed that chemotaxis plays a key role during this phase. Here, we report a quantitative study on how Salmonella Typhimurium search for T84 human colonic epithelial cells. Combining time-lapse microscopy and mathematical modeling, we show that bacteria can be described as chiral active particles with strong active speed fluctuations, which are of biological, as opposed to thermal, origin. We observe that there exists a giant range of inter-individual variability of the bacterial exploring capacity. Furthermore, we find Salmonella Typhimurium does not exhibit biased motion towards the cells and show that the search time statistics is consistent with a random search strategy. Our results indicate that in vitro localization of host cells, and also cell infection, are random processes, not involving chemotaxis, that strongly depend on bacterial motility parameters.

3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images

Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and 'straightened' freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90-100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.

Bioluminescence Measurement of Time-Dependent Dynamic Changes of CYP-Mediated Cytotoxicity in CYP-Expressing Luminescent HepG2 Cells

We sought to develop a cell-based cytotoxicity assay using human hepatocytes, which reflect the effects of drug-metabolizing enzymes on cytotoxicity. In this study, we generated luminescent human hepatoblastoma HepG2 cells using the mouse artificial chromosome vector, in which click beetle luciferase alone or luciferase and major drug-metabolizing enzymes (CYP2C9, CYP2C19, CYP2D6, and CYP3A4) are expressed, and monitored the time-dependent changes of CYP-mediated cytotoxicity expression by bioluminescence measurement. Real-time bioluminescence measurement revealed that compared with CYP-non-expressing cells, the luminescence intensity of CYP-expressing cells rapidly decreased when the cells were treated with low concentrations of aflatoxin B1 or primaquine, which exhibits cytotoxicity in the presence of CYP3A4 or CYP2D6, respectively. Using kinetics data obtained by the real-time bioluminescence measurement, we estimated the time-dependent changes of 50% inhibitory concentration (IC₅₀) values in the aflatoxin B1- and primaquine-treated cell lines. The first IC₅₀ value was detected much earlier and at a lower concentration in primaquine-treated CYP-expressing HepG2 cells than in primaquine-treated CYP-non-expressing cells, and the decrease of IC₅₀ values was much faster in the former than the latter. Thus, we successfully monitored time- and concentration-dependent dynamic changes of CYP-mediated cytotoxicity expression in CYP-expressing luminescent HepG2 cells by means of real-time bioluminescence measurement.

Biofilm Spreading by the Adhesin-Dependent Gliding Motility of Flavobacterium johnsoniae. 1. Internal Structure of the Biofilm

The Gram-negative bacterium Flavobacterium johnsoniae employs gliding motility to move rapidly over solid surfaces. Gliding involves the movement of the adhesin SprB along the cell surface. F. johnsoniae spreads on nutrient-poor 1% agar-PY2, forming a thin film-like colony. We used electron microscopy and time-lapse fluorescence microscopy to investigate the structure of colonies formed by wild-type (WT) F. johnsoniae and by the sprB mutant (ΔsprB). In both cases, the bacteria were buried in the extracellular polymeric matrix (EPM) covering the top of the colony. In the spreading WT colonies, the EPM included a thick fiber framework and vesicles, revealing the formation of a biofilm, which is probably required for the spreading movement. Specific paths that were followed by bacterial clusters were observed at the leading edge of colonies, and abundant vesicle secretion and subsequent matrix formation were suggested. EPM-free channels were formed in upward biofilm protrusions, probably for cell migration. In the nonspreading ΔsprB colonies, cells were tightly packed in layers and the intercellular space was occupied by less matrix, indicating immature biofilm. This result suggests that SprB is not necessary for biofilm formation. We conclude that F. johnsoniae cells use gliding motility to spread and maturate biofilms.

Computational 4D-OCM for label-free imaging of collective cell invasion and force-mediated deformations in collagen

Traction force microscopy (TFM) is an important family of techniques used to measure and study the role of cellular traction forces (CTFs) associated with many biological processes. However, current standard TFM methods rely on imaging techniques that do not provide the experimental capabilities necessary to study CTFs within 3D collective and dynamic systems embedded within optically scattering media. Traction force optical coherence microscopy (TF-OCM) was developed to address these needs, but has only been demonstrated for the study of isolated cells embedded within optically clear media. Here, we present computational 4D-OCM methods that enable the study of dynamic invasion behavior of large tumor spheroids embedded in collagen. Our multi-day, time-lapse imaging data provided detailed visualizations of evolving spheroid morphology, collagen degradation, and collagen deformation, all using label-free scattering contrast. These capabilities, which provided insights into how stromal cells affect cancer progression, significantly expand access to critical data about biophysical interactions of cells with their environment, and lay the foundation for future efforts toward volumetric, time-lapse reconstructions of collective CTFs with TF-OCM.

The Inclusion Principles of Human Embryos in the WOW-Based Time-Lapse System: A Retrospective Cohort Study

A time-lapse system (TLS) with a well-of-the-well (WOW) dish, which allows individual identification and the possibility of autocrine and paracrine signaling between group-cultured embryos, has been widely used in clinic. However, there is a need to re-think the inclusion principles of human embryos in WOW-based TLS, especially for grade IV (G4) embryos, which are considered to potentially have detrimental effects on surrounding embryos. Here, we carried out a single-center, large-cohort, retrospective study, comprising 303 patients undergoing IVF (148 cases) and ICSI (155 cases), with a total of 3282 embryos, to compare embryonic development until the blastocyst stage in the group culture system with or without G4 embryos. Further, LC-MS/MS was used to analyze the G1-G4 embryo secretome to understand the influence of G4 embryos on the group culture microenvironment. We proved that polypronuclear (PPN) embryos positively contribute to the development of the neighboring embryos through secretion of ILIAP, ITI-H4, and keratin. Existence of more than one G4 embryo had a negative effect on the other embryos (p < 0.05). Moreover, G4 embryos were found to secrete KLKB1 and VTDB, which might harm the neighboring embryos. Thus, our study clarified that when embryos are subjected to group culture in WOW-based TLS, the PPN-derived embryos need not be removed, and it is important to ensure that no more than one G4 embryo is present to avoid negative effects on the neighboring embryos.

In Vivo Analysis of the Mesenchymal-to-Epithelial Transition During Chick Secondary Neurulation

The neural tube in amniotic embryos forms as a result of two consecutive events along the anteroposterior axis, referred to as primary and secondary neurulation (PN and SN). While PN involves the invagination of a sheet of epithelial cells, SN shapes the caudal neural tube through the mesenchymal-to-epithelial transition (MET) of neuromesodermal progenitors, followed by cavitation of the medullary cord. The technical difficulties in studying SN mainly involve the challenge of labeling and manipulating SN cells in vivo. Here we describe a new method to follow MET during SN in the chick embryo, combining early in ovo chick electroporation with in vivo time-lapse imaging. This procedure allows the cells undergoing SN to be manipulated in order to investigate the MET process, permitting their cell dynamics to be followed in vivo.

Microfluidics for single-cell lineage tracking over time to characterize transmission of phenotypes in Saccharomyces cerevisiae

The budding yeast Saccharomyces cerevisiae is an excellent model organism to dissect the maintenance and inheritance of phenotypes due to its asymmetric division. This requires following individual cells over time as they go through divisions to define pedigrees. Here, we provide a detailed protocol for collecting and analyzing time-lapse imaging data of yeast cells. The microfluidics protocol can achieve improved time resolution for single-cell tracking to enable characterization of maintenance and inheritance of phenotypes. For complete details on the use and execution of this protocol, please refer to Bheda et al. (2020a).

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

In vivo biomolecular imaging of zebrafish embryos using confocal Raman spectroscopy

Zebrafish embryos provide a unique opportunity to visualize complex biological processes, yet conventional imaging modalities are unable to access intricate biomolecular information without compromising the integrity of the embryos. Here, we report the use of confocal Raman spectroscopic imaging for the visualization and multivariate analysis of biomolecular information extracted from unlabeled zebrafish embryos. We outline broad applications of this method in: (i) visualizing the biomolecular distribution of whole embryos in three dimensions, (ii) resolving anatomical features at subcellular spatial resolution, (iii) biomolecular profiling and discrimination of wild type and ΔRD1 mutant Mycobacterium marinum strains in a zebrafish embryo model of tuberculosis and (iv) in vivo temporal monitoring of the wound response in living zebrafish embryos. Overall, this study demonstrates the application of confocal Raman spectroscopic imaging for the comparative bimolecular analysis of fully intact and living zebrafish embryos.

Bacterial Vivisection: How Fluorescence-Based Imaging Techniques Shed a Light on the Inner Workings of Bacteria

The rise in fluorescence-based imaging techniques over the past 3 decades has improved the ability of researchers to scrutinize live cell biology at increased spatial and temporal resolution. In microbiology, these real-time vivisections structurally changed the view on the bacterial cell away from the "watery bag of enzymes" paradigm toward the perspective that these organisms are as complex as their eukaryotic counterparts. Capitalizing on the enormous potential of (time-lapse) fluorescence microscopy and the ever-extending pallet of corresponding probes, initial breakthroughs were made in unraveling the localization of proteins and monitoring real-time gene expression. However, later it became clear that the potential of this technique extends much further, paving the way for a focus-shift from observing single events within bacterial cells or populations to obtaining a more global picture at the intra- and intercellular level. In this review, we outline the current state of the art in fluorescence-based vivisection of bacteria and provide an overview of important case studies to exemplify how to use or combine different strategies to gain detailed information on the cell's physiology. The manuscript therefore consists of two separate (but interconnected) parts that can be read and consulted individually. The first part focuses on the fluorescent probe pallet and provides a perspective on modern methodologies for microscopy using these tools. The second section of the review takes the reader on a tour through the bacterial cell from cytoplasm to outer shell, describing strategies and methods to highlight architectural features and overall dynamics within cells.

The dynamics of plant nutation

In this article we advance a cutting-edge methodology for the study of the dynamics of plant movements of nutation. Our approach, unlike customary kinematic analyses of shape, period, or amplitude, is based on three typical signatures of adaptively controlled processes and motions, as reported in the biological and behavioral dynamics literature: harmonicity, predictability, and complexity. We illustrate the application of a dynamical methodology to the bending movements of shoots of common beans (Phaseolus vulgaris L.) in two conditions: with and without a support to climb onto. The results herewith reported support the hypothesis that patterns of nutation are influenced by the presence of a support to climb in their vicinity. The methodology is in principle applicable to a whole range of plant movements.

Motion magnification analysis of microscopy videos of biological cells

It is well recognized that isolated cardiac muscle cells beat in a periodic manner. Recently, evidence indicates that other, non-muscle cells, also perform periodic motions that are either imperceptible under conventional lab microscope lens or practically not easily amenable for analysis of oscillation amplitude, frequency, phase of movement and its direction. Here, we create a real-time video analysis tool to visually magnify and explore sub-micron rhythmic movements performed by biological cells and the induced movements in their surroundings. Using this tool, we suggest that fibroblast cells perform small fluctuating movements with a dominant frequency that is dependent on their surrounding substrate and its stiffness.

The inclusion of blastomeres into the inner cell mass in early-stage human embryos depends on the sequence of cell cleavages during the fourth division

The fate of the ICM in humans is still unknown, due to the ethical difficulties surrounding experimentation in this field. In this study we have explored the existing time-lapse recording data of embryos in the early stages of development, taking advantage of the large refractile bodies (RBs) within blastomeres as cellular markers. Our study found that the cellular composition of the ICM in humans is largely determined at the time of the fourth division and blastomeres which cleave first to fourth, during the fourth division from 8 cells to 16 cells, have the potential to be incorporated in the ICM.