Democratising deep learning for microscopy with ZeroCostDL4Mic

Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects

Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial susceptibility testing (AST) by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions. In particular, AI-based approaches have demonstrated significant potential in resource-limited settings by enabling cost-effective and portable diagnostic solutions, reducing dependency on specialized infrastructure, and facilitating remote bacterial detection through smartphone-integrated platforms and telemedicine applications. This review highlights AI's transformative role in automating data analysis, minimizing human error, and delivering real-time diagnostic results, ultimately improving patient outcomes and optimizing healthcare efficiency. In addition, we not only examine the current advances in machine learning and deep learning but also review their applications in plate counting, mass spectrometry, morphology-based and motion-based microscopic detection, holographic microscopy, colorimetric and fluorescence detection, electrochemical sensors, Raman and Surface-Enhanced Raman Spectroscopy (SERS), and Atomic Force Microscopy (AFM) for bacterial diagnostics and AST. Finally, we discuss the future directions and potential advancements in AI-driven bacterial diagnostics.

Artificial intelligence-enabled lipid droplets quantification: Comparative analysis of NIS-elements Segment.ai and ZeroCostDL4Mic StarDist networks

Lipid droplets (LDs) are dynamic organelles that are present in almost all cell types, with a particularly high prevalence in adipocytes. The phenotype of LDs in these cells reflects their maturity, metabolic activity and function. Although LDs quantification in adipocytes is significant for understanding the origins of obesity and associated complications, it remains challenging and requires the implementation of computer science innovations. This article outlines a practical workflow for application of Segment.ai neural network from the commercial software NIS-Elements and the open-source StarDist Jupyter notebook from the ZeroCostDL4Mic platform for the analysis of LDs number and morphology. To generate a training dataset, 3T3-L1 cells were differentiated into adipocytes and stained with lipophilic dye BODIPY493/503. Subsequently, confocal live cell images were acquired, annotated and used for training. As an example task, deep learning models were tested on their ability to detect LDs enlargement on images of adipocytes with inhibited lipolysis. We demonstrated that both Segment.ai and StarDist models are capable of accurately recognising LDs on microphotographs, thereby significantly accelerating the processing of imaging data. The advantage of the Segment.ai model is its integration into NIS-Elements General Analysis 3, which performs quantitative and statistical data interpretation. Alternatively, StarDist is a more accessible and transparent tool, enabling precise model evaluation. In conclusion, both created approaches have the potential to accelerate the exploration of LDs dynamics, thus paving the way for further insights into how these organelles regulate energy homeostasis and contribute to the development of metabolic abnormalities.

The nucleoid of rapidly growing Escherichia coli localizes close to the inner membrane and is organized by transcription, translation, and cell geometry

Bacterial chromosomes are spatiotemporally organized and sensitive to environmental changes. However, the mechanisms underlying chromosome configuration and reorganization are not fully understood. Here, we use single-molecule localization microscopy and live-cell imaging to show that the Escherichia coli nucleoid adopts a condensed, membrane-proximal configuration during rapid growth. Drug treatment induces a rapid collapse of the nucleoid from an apparently membrane-bound state within 10 min of halting transcription and translation. This hints toward an active role of transertion (coupled transcription, translation, and membrane insertion) in nucleoid organization, while cell wall synthesis inhibitors only affect nucleoid organization during morphological changes. Further, we provide evidence that the nucleoid spatially correlates with elongasomes in unperturbed cells, suggesting that large membrane-bound complexes might be hotspots for transertion. The observed correlation diminishes in cells with changed cell geometry or upon inhibition of protein biosynthesis. Replication inhibition experiments, as well as multi-drug treatments highlight the role of entropic effects and transcription in nucleoid condensation and positioning. Thus, our results indicate that transcription and translation, possibly in the context of transertion, act as a principal organizer of the bacterial nucleoid, and show that an altered metabolic state and antibiotic treatment lead to major changes in the spatial organization of the nucleoid.

Anchor point based image registration for absolute scale topographic structure detection in microscopy

Microscopy images obtained through remote sensing often suffer from misalignment and deformation, complicating accurate data analysis. As experimental instruments improve and scientific discoveries deepen, the volume of data requiring processing continues to grow. Image registration can contribute to microscopy automation, which enables more efficient data analysis and experimental workflows. For this implementation, image processing techniques that can handle both image registration and localized object analysis are required. This research introduces a computer interface designed to calibrate and analyze specific structures with prior knowledge of the observed target. Our method achieves image registration by aligning anchor points, which correspond to the coordinates of a structural model within the image. It employs homography transform to correct images, restoring them to their original, undistorted form, thus enabling consistent quantitative comparisons across different images on an absolute scale. Additionally, the method provides valuable information from the registered anchor points, allowing for the precise localization of local objects in the structure. We demonstrate this technique across various microscopy scenarios at different scales and evaluate its precision against a keypoint detection AI approach from our previous research, which promises its enhancement in microscopy data analysis and automation.

Physiology and Robustness of Yeasts Exposed to Dynamic pH and Glucose Environments

Gradients negatively affect performance in large-scale bioreactors; however, they are difficult to predict at laboratory scale. Dynamic microfluidics single-cell cultivation (dMSCC) has emerged as an important tool for investigating cell behavior in rapidly changing environments. In the present study, dMSCC, biosensors of intracellular parameters, and robustness quantification were employed to investigate the physiological response of three Saccharomyces cerevisiae strains to substrate and pH changes every 0.75-48 min. All strains showed higher sensitivity to substrate than pH oscillations. Strain-specific intracellular responses included higher relative glycolytic flux and oxidative stress response for strains PE2 and CEN.PK113-7D, respectively. Instead, the Ethanol Red strain displayed the least heterogeneous populations and the highest robustness for multiple functions when exposed to substrate oscillations. This result could arise from a positive trade-off between ATP levels and ATP stability over time. The present study demonstrates the importance of coupling physiological responses to dynamic environments with simultaneous characterization of strains, conditions, individual regimes, and robustness analysis. All these tools are a suitable add-on to traditional evaluation and screening workflows at both laboratory and industrial scale, and can help bridge the gap between these two.

Formulation and Characterization of Novel Ionizable and Cationic Lipid Nanoparticles for the Delivery of Splice-Switching Oligonucleotides

Despite increasing knowledge about the mechanistic aspects of lipid nanoparticles (LNPs) as oligonucleotide carriers, the structure-function relationship in LNPs has been generally overlooked. Understanding this correlation is critical in the rational design of LNPs. Here, a materials characterization approach is utilized, applying structural information from small-angle X-ray scattering experiments to design novel LNPs focusing on distinct lipid organizations with a minimal compositional variation. The lipid phase structures are characterized in these LNPs and their corresponding bulk lipid mixtures with small-angle scattering techniques, and the LNP-cell interactions in vitro with respect to cytotoxicity, hemolysis, cargo delivery, cell uptake, and lysosomal swelling. An LNP is identified that outperforms Onpattro lipid composition using lipid components and molar ratios which differ from the gold standard clinical LNPs. The base structure of these LNPs has an inverse micellar phase organization, whereas the LNPs with inverted hexagonal phases are not functional, suggesting that this phase formation may not be needed for LNP-mediated oligonucleotide delivery. The importance of stabilizer choice for the LNP function is demonstrated and super-resolution microscopy highlights the complexity of the delivery mechanisms, where lysosomal swelling for the majority of LNPs is observed. This study highlights the importance of advanced characterization for the rational design of LNPs to enable the study of structure-function relationships.

Practical considerations for data exploration in quantitative cell biology

Data exploration is an essential step in quantitative cell biology, bridging raw data and scientific insights. Unlike polished, published figures, effective data exploration requires a flexible, hands-on approach that reveals trends, identifies outliers and refines hypotheses. This Opinion offers simple, practical advice for building a structured data exploration workflow, drawing on the authors' personal experience in analyzing bioimage datasets. In addition, the increasing availability of generative artificial intelligence and large language models makes coding and improving data workflows easier than ever before. By embracing these practices, researchers can streamline their workflows, produce more reliable conclusions and foster a collaborative, transparent approach to data analysis in cell biology.

A new era in nephrology: the role of super-resolution microscopy in research, medical diagnostic, and drug discovery

For decades, electron microscopy has been the primary method to visualize ultrastructural details of the kidney, including podocyte foot processes and the slit diaphragm. Despite its status as the gold standard, electron microscopy has significant limitations: it requires laborious sample preparation, works only with very small samples, is mainly qualitative, and is prone to misinterpretation because of section angle bias. In addition, combining imaging and protein staining with antibodies poses a challenge, limiting electron microscopy's integration into routine research and diagnostic workflows. As imaging technology advances, super-resolution microscopy, with an optical resolution below 100 nm, presents a promising alternative for detailed insights into glomerular ultrastructure. This review explores various super-resolution techniques, particularly 3-dimensional structured illumination microscopy, and demonstrates how they can be applied to standard histological sections. The 3-dimensional structured illumination microscopy-based measurement procedure-podocyte exact morphology measurement procedure-offers a novel approach to quantifying podocyte foot process morphology and can detect podocyte foot process changes even before proteinuria is present. Furthermore, the podocyte exact morphology measurement procedure can be combined with mRNA detection, multiplex staining, and deep learning algorithms, making it a powerful tool for kidney research, preclinical studies, and personalized diagnostics. This advancement has the potential to accelerate drug development and enhance therapeutic precision, heralding a new era of high-precision nephrology.

Translation dysregulation in cancer as a source for targetable antigens

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy.

Multi-SpinX: An advanced framework for automated tracking of mitotic spindles and kinetochores in multicellular environments

SpinX, an AI-guided spindle tracking software, allows the 3-dimensional (3D) tracking of metaphase spindle movements in mammalian cells. Using over 900 images of dividing cells, we create the Multi-SpinX framework to significantly expand SpinX's applications: a) to track spindles and cell cortex in multicellular environments, b) to combine two object tracking (spindle with kinetochores marked by centromeric probes) and c) to extend spindle tracking beyond metaphase to prometaphase and anaphase stages where spindle morphology is different. We have used a human-in-the-loop approach to assess our optimisation steps, to manually identify challenges and to build a robust computational pipeline for segmenting kinetochore pairs and spindles. Spindles of both H1299 and RPE1 cells have been assessed and validated for use through Multi-SpinX, and we expect the tool to be versatile in enabling quantitative studies of mitotic subcellular dynamics.

This Microtubule Does Not Exist: Super-Resolution Microscopy Image Generation by a Diffusion Model

Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high-quality realistic data across various domains. Here, the adaptation and training of a diffusion model on super-resolution microscopy images are explored. It is shown that the generated images resemble experimental images, and that the generation process does not exhibit a large degree of memorization from existing images in the training set. To demonstrate the usefulness of the generative model for data augmentation, the performance of a deep learning-based single-image super-resolution (SISR) method trained using generated high-resolution data is compared against training using experimental images alone, or images generated by mathematical modeling. Using a few experimental images, the reconstruction quality and the spatial resolution of the reconstructed images are improved, showcasing the potential of diffusion model image generation for overcoming the limitations accompanying the collection and annotation of microscopy images. Finally, the pipeline is made publicly available, runnable online, and user-friendly to enable researchers to generate their own synthetic microscopy data. This work demonstrates the potential contribution of generative diffusion models for microscopy tasks and paves the way for their future application in this field.

Comparison of Urinary Red Blood Cell Distribution (URD) and Dysmorphic Red Blood Cells for Detecting Glomerular Hematuria: A Multicenter Study

BACKGROUND

The clinical utility of urinary red blood cell (RBC) distribution (URD) remains limited. This study aimed to compare the diagnostic performance of URD and dysmorphic RBC (dRBC) in a multicenter study.

METHODS

This study enrolled 703 patients who visited four tertiary medical centers in Korea. Patients were classified into glomerular diseases with biopsy (N = 169), renal diseases including chronic kidney disease (N = 194), nephrotic syndrome (NS; N = 88), tubulointerstitial diseases (N = 36), acute kidney injury (N = 32), others (N = 10), and extrarenal diseases (N = 174). Renal parameters, urine microscopic examination, urinalysis, and URD assessments were conducted. The diagnostic performances of dRBC and URD were evaluated.

RESULTS

Median values of both dRBC and URD were significantly elevated in patients with glomerular diseases. URD exhibited a significant correlation with dRBC (r = 0.536) and albumin creatinine ratio (r = 0.186), while no significant correlation was observed with specific gravity (r = -0.03). Among renal diseases, dRBC and URD values were notably higher in patients with NS. The agreement rate between dRBC and URD results was 78.3% (112/143), with 31 instances showing discrepancies. ROC curve analysis comparing glomerular and extrarenal diseases yielded cutoff values of 18% for dRBC and 31.9% for URD, resulting in corresponding areas under the curve (AUC) of 0.79 and 0.83, respectively.

CONCLUSIONS

URD exhibited a comparable diagnostic performance, as indicated by a similar AUC value to that of dRBC, while offering the added advantage of providing objective and standardizable results. This attribute enhances its utility as a parameter for distinguishing between patients with glomerular hematuria (GH) and those with non-GH.

High throughput analysis of rare nanoparticles with deep-enhanced sensitivity via unsupervised denoising

The large-scale multiparametric analysis of individual nanoparticles is increasingly vital in the diverse fields of biology, medicine, and materials science. However, the current methods struggle with the tradeoff between measurement scalability and sensitivity, especially when identifying rare nanoparticles in heterogeneous mixtures. By developing and combining an unsupervised deep learning-based denoising method and an optofluidic device tuned for nanoparticle detection, we realize a nanoparticle analyzer that simultaneously achieves high scalability, throughput, and sensitivity levels; we name this approach "Deep Nanometry" (DNM). DNM detects polystyrene beads with a detection of limit of 30 nm at a throughput of over 100,000 events/second. The sensitive and scalable DNM directly detects rare target extracellular vesicles (EVs) in non-purified serum, making up as little as 0.002% of the 1,214,392 total particles. Moreover, DNM accurately and sufficiently counts diagnostic marker EVs present in only 0.93% and 0.17% of particle detections in sera of colorectal cancer patients and healthy controls, demonstrating its potential application to the early detection of colorectal cancer.

EASI-ORC: A pipeline for the efficient analysis and segmentation of smFISH images for organelle-RNA colocalization measurements in yeast

Analysis of single-molecule fluorescent in situ hybridization (smFISH) images is important to translate cellular image data into a quantifiable format. Although smFISH is the gold standard for RNA localization measurements, there are no freely available, user-friendly applications for assaying messenger RNA (mRNA) localization to organelles. EASI-ORC (Efficient Analysis and Segmentation of smFISH Images for Organelle-RNA Colocalization) is a novel pipeline for the automated analysis of multiple smFISH images of yeast cells. EASI-ORC automates the segmentation of cells and organelles, identifies bona fide smFISH signals, and measures mRNA-organelle colocalization. EASI-ORC is efficient, unbiased, and plots the colocalization data and statistical analyses. EASI-ORC utilizes existing ImageJ plugins and original scripts, thus allowing for free access and ease-of-use. To circumvent technical literacy issues, a step-by-step user guide is provided. EASI-ORC offers a robust solution to smFISH image analysis - one that saves time, effort and provides consistent measurements of mRNA-organelle colocalization in yeast.

Machine learning approaches for image classification in developmental biology and clinical embryology

The rapid increase in the amount of available biological data together with increasing computational power and innovative new machine learning algorithms has resulted in great potential for machine learning approaches to revolutionise image analysis in developmental biology and clinical embryology. In this Spotlight, we provide an introduction to machine learning for developmental biologists interested in incorporating machine learning techniques into their research. We give an overview of essential machine learning concepts and models and describe a few recent examples of how these techniques can be used in developmental biology. We also briefly discuss latest advancements in the field and how it might develop in the future.

Quantifying multilabeled brain cells in the whole prefrontal cortex reveals reduced inhibitory and a subtype of excitatory neuronal marker expression in serotonin transporter knockout rats

The prefrontal cortex regulates emotions and is influenced by serotonin. Rodents lacking the serotonin transporter (5-HTT) show increased anxiety and changes in excitatory and inhibitory cell markers in the prefrontal cortex. However, these observations are constrained by limitations in brain representation and cell segmentation, as standard immunohistochemistry is inadequate to consider volume variations in regions of interest. We utilized the deep learning network of the StarDist method in combination with novel open-source methods for automated cell counts in a wide range of prefrontal cortex subregions. We found that 5-HTT knockout rats displayed increased anxiety and diminished relative numbers of subclass excitatory VGluT2+ and activated ΔFosB+ cells in the infralimbic and prelimbic cortices and of inhibitory GAD67+ cells in the prelimbic cortex. Anxiety levels and ΔFosB cell counts were positively correlated in wild-type, but not in knockout, rats. In conclusion, we present a novel method to quantify whole brain subregions of multilabeled cells in animal models and demonstrate reduced excitatory and inhibitory neuronal marker expression in prefrontal cortex subregions of 5-HTT knockout rats.

From text to insight: large language models for chemical data extraction

The vast majority of chemical knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on manual curation and partial automation for data extraction for specific use cases. The advent of large language models (LLMs) represents a significant shift, potentially enabling non-experts to extract structured, actionable data from unstructured text efficiently. While applying LLMs to chemical and materials science data extraction presents unique challenges, domain knowledge offers opportunities to guide and validate LLM outputs. This tutorial review provides a comprehensive overview of LLM-based structured data extraction in chemistry, synthesizing current knowledge and outlining future directions. We address the lack of standardized guidelines and present frameworks for leveraging the synergy between LLMs and chemical expertise. This work serves as a foundational resource for researchers aiming to harness LLMs for data-driven chemical research. The insights presented here could significantly enhance how researchers across chemical disciplines access and utilize scientific information, potentially accelerating the development of novel compounds and materials for critical societal needs.

Microscopy methods for the in vivo study of nanoscale nuclear organization

Eukaryotic genomes are highly compacted within the nucleus and organized into complex 3D structures across various genomic and physical scales. Organization within the nucleus plays a key role in gene regulation, both facilitating regulatory interactions to promote transcription while also enabling the silencing of other genes. Despite the functional importance of genome organization in determining cell identity and function, investigating nuclear organization across this wide range of physical scales has been challenging. Microscopy provides the opportunity for direct visualization of nuclear structures and has pioneered key discoveries in this field. Nonetheless, visualization of nanoscale structures within the nucleus, such as nucleosomes and chromatin loops, requires super-resolution imaging to go beyond the ~220 nm diffraction limit. Here, we review recent advances in imaging technology and their promise to uncover new insights into the organization of the nucleus at the nanoscale. We discuss different imaging modalities and how they have been applied to the nucleus, with a focus on super-resolution light microscopy and its application to in vivo systems. Finally, we conclude with our perspective on how continued technical innovations in super-resolution imaging in the nucleus will advance our understanding of genome structure and function.

Neural network-assisted localization of clustered point spread functions in single-molecule localization microscopy

Single-molecule localization microscopy (SMLM), which has revolutionized nanoscale imaging, faces challenges in densely labelled samples due to fluorophore clustering, leading to compromised localization accuracy. In this paper, we propose a novel convolutional neural network (CNN)-assisted approach to address the issue of locating the clustered fluorophores. Our CNN is trained on a diverse data set of simulated SMLM images where it learns to predict point spread function (PSF) locations by generating Gaussian blobs as output. Through rigorous evaluation, we demonstrate significant improvements in PSF localization accuracy, especially in densely labelled samples where traditional methods struggle. In addition, we employ blob detection as a post-processing technique to refine the predicted PSF locations and enhance localization precision. Our study underscores the efficacy of CNN in addressing clustering challenges in SMLM, thereby advancing spatial resolution and enabling deeper insights into complex biological structures.

Self-Driving Microscopes: AI Meets Super-Resolution Microscopy

The integration of Machine Learning (ML) with super-resolution microscopy represents a transformative advancement in biomedical research. Recent advances in ML, particularly deep learning (DL), have significantly enhanced image processing tasks, such as denoising and reconstruction. This review explores the growing potential of automation in super-resolution microscopy, focusing on how DL can enable autonomous imaging tasks. Overcoming the challenges of automation, particularly in adapting to dynamic biological processes and minimizing manual intervention, is crucial for the future of microscopy. Whilst still in its infancy, automation in super-resolution can revolutionize drug discovery and disease phenotyping leading to similar breakthroughs as have been recognized in this year's Nobel Prizes for Physics and Chemistry.

Phenotypic analysis of complex bioengineered 3D models

With advances in underlying technologies such as complex multicellular systems, synthetic materials, and bioengineering techniques, we can now generate in vitro miniaturized human tissues that recapitulate the organotypic features of normal or diseased tissues. Importantly, these 3D culture models have increasingly provided experimental access to diverse and complex tissues architectures and their morphogenic assembly in vitro. This review presents an analytical toolbox for biological researchers using 3D modeling technologies through which they can find a collation of currently available methods to phenotypically assess their 3D models in their normal state as well as their response to therapeutic or pathological agents.

Artificial Intelligence Methods in Infection Biology Research

Despite unprecedented achievements, the domain-specific application of artificial intelligence (AI) in the realm of infection biology was still in its infancy just a couple of years ago. This is largely attributable to the proneness of the infection biology community to shirk quantitative techniques. The so-called "sorting machine" paradigm was prevailing at that time, meaning that AI applications were primarily confined to the automation of tedious laboratory tasks. However, fueled by the severe acute respiratory syndrome coronavirus 2 pandemic, AI-driven applications in infection biology made giant leaps beyond mere automation. Instead, increasingly sophisticated tasks were successfully tackled, thereby ushering in the transition to the "Swiss army knife" paradigm. Incentivized by the urgent need to subdue a raging pandemic, AI achieved maturity in infection biology and became a versatile tool. In this chapter, the maturation of AI in the field of infection biology from the "sorting machine" paradigm to the "Swiss army knife" paradigm is outlined. Successful applications are illustrated for the three data modalities in the domain, that is, images, molecular data, and language data, with a particular emphasis on disentangling host-pathogen interactions. Along the way, fundamental terminology mentioned in the same breath as AI is elaborated on, and relationships between the subfields these terms represent are established. Notably, in order to dispel the fears of infection biologists toward quantitative methodologies and lower the initial hurdle, this chapter features a hands-on guide on software installation, virtual environment setup, data preparation, and utilization of pretrained models at its very end.

Berendzen K, Wanke F, Gabernet G, Harter K, Nahnsen S. Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue

Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images. This also includes apoplastic pH measurements, which is useful for modeling hormone signaling and cell physiological responses. We show that this nf-core standard-based pipeline successfully automates tissue zone segmentation and is both high-throughput and highly reproducible. In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. We observed a high statistical similarity between the manually generated results and the output of the nf-root.

A spheroid whole mount drug testing pipeline with machine-learning based image analysis identifies cell-type specific differences in drug efficacy on a single-cell level

BACKGROUND

The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of single-cell procedures with sufficient detail has hampered the automated observation of cell-type-specific effects in three-dimensional stroma-tumor cell co-cultures.

METHODS

Here, we developed a high-content pipeline ranging from the setup of novel tumor-fibroblast spheroid co-cultures over optical tissue clearing, whole mount staining, and 3D confocal microscopy to optimized 3D-image segmentation and a 3D-deep-learning model to automate the analysis of a range of cell-type-specific processes, such as cell proliferation, apoptosis, necrosis, drug susceptibility, nuclear morphology, and cell density.

RESULTS

This demonstrated that co-cultures of KP-4 tumor cells with CCD-1137Sk fibroblasts exhibited a growth advantage compared to tumor cell mono-cultures, resulting in higher cell counts following cytostatic treatments with paclitaxel and doxorubicin. However, cell-type-specific single-cell analysis revealed that this apparent benefit of co-cultures was due to a higher resilience of fibroblasts against the drugs and did not indicate a higher drug resistance of the KP-4 cancer cells during co-culture. Conversely, cancer cells were partially even more susceptible in the presence of fibroblasts than in mono-cultures.

CONCLUSION

In summary, this underlines that a novel cell-type-specific single-cell analysis method can reveal critical insights regarding the mechanism of action of drug substances in three-dimensional cell culture models.

Bridging imaging users to imaging analysis - A community survey

The 'Bridging Imaging Users to Imaging Analysis' survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), BioImaging North America (BINA) and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multichoice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarise themselves with the fundamentals of image analysis, provide constant feedback and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for 'written tutorials' to acquire knowledge on image analysis. We also observed that the interest in having 'office hours' to get an expert opinion on their image analysis methods has increased over the years. The results also showed less-than-expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly.

Neural space-time model for dynamic multi-shot imaging

Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.

Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ

This manuscript showcases the latest advancements in deepImageJ, a pivotal Fiji/ImageJ plugin for bioimage analysis in life sciences. The plugin, known for its user-friendly interface, facilitates the application of diverse pre-trained convolutional neural networks to custom data. The manuscript demonstrates several deepImageJ capabilities, particularly in deploying complex pipelines, three-dimensional (3D) image analysis, and processing large images. A key development is the integration of the Java Deep Learning Library, expanding deepImageJ's compatibility with various deep learning (DL) frameworks, including TensorFlow, PyTorch, and ONNX. This allows for running multiple engines within a single Fiji/ImageJ instance, streamlining complex bioimage analysis workflows. The manuscript details three case studies to demonstrate these capabilities. The first case study explores integrated image-to-image translation followed by nuclei segmentation. The second case study focuses on 3D nuclei segmentation. The third case study showcases large image volume segmentation and compatibility with the BioImage Model Zoo. These use cases underscore deepImageJ's versatility and power to make advanced DLmore accessible and efficient for bioimage analysis. The new developments within deepImageJ seek to provide a more flexible and enriched user-friendly framework to enable next-generation image processing in life science.

Roadmap to Success: How Oomycete Plant Pathogens Invade Tissues and Deliver Effectors

Filamentous plant pathogens threaten global food security and ecosystem resilience. In recent decades, significant strides have been made in deciphering the molecular basis of plant-pathogen interactions, especially the interplay between pathogens' molecular weaponry and hosts' defense machinery. Stemming from interdisciplinary investigations into the infection cell biology of filamentous plant pathogens, recent breakthrough discoveries have provided a new impetus to the field. These advances include the biophysical characterization of a novel invasion mechanism (i.e., naifu invasion) and the unraveling of novel effector secretion routes. On the plant side, progress includes the identification of components of cellular networks involved in the uptake of intracellular effectors. This exciting body of research underscores the pivotal role of logistics management by the pathogen throughout the infection cycle, encompassing the precolonization stages up to tissue invasion. More insight into these logistics opens new avenues for developing environmentally friendly crop protection strategies in an era marked by an imperative to reduce the use of agrochemicals.

Opportunities and challenges for deep learning in cell dynamics research

The growth of artificial intelligence (AI) has led to an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes but has also started to support advances in drug development, precision medicine, and genome-phenome mapping. We survey existing AI-based techniques and tools, as well as open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from a computational perspective and review emerging research frontiers and innovative applications for DL-guided automation in cell dynamics research.

Stepwise virus assembly in the cell nucleus revealed by spatiotemporal click chemistry of DNA replication

Biomolecular assemblies are fundamental to life and viral disease. The spatiotemporal coordination of viral replication and assembly is largely unknown. Here, we developed a dual-color click chemistry procedure for imaging adenovirus DNA (vDNA) replication in the cell nucleus. Late- but not early-replicated vDNA was packaged into virions. Early-replicated vDNA segregated from the viral replication compartment (VRC). Single object tracking, superresolution microscopy, fluorescence recovery after photobleaching, and correlative light-electron microscopy revealed a stepwise assembly program involving vDNA and capsid intermediates. Depending on replication and the scaffolding protein 52K, late-replicated vDNA with rapidly exchanging green fluorescent protein-tagged capsid linchpin protein V and incomplete virions emerged from the VRC periphery. These nanogel-like puncta exhibited restricted movements and were located with the capsid proteins hexon, VI, and virions in the nuclear periphery, suggestive of sites for virion formation. Our findings identify VRC dynamics and assembly intermediates, essential for stepwise productive adenovirus morphogenesis.

Machine learning in microscopy - insights, opportunities and challenges

Machine learning (ML) is transforming the field of image processing and analysis, from automation of laborious tasks to open-ended exploration of visual patterns. This has striking implications for image-driven life science research, particularly microscopy. In this Review, we focus on the opportunities and challenges associated with applying ML-based pipelines for microscopy datasets from a user point of view. We investigate the significance of different data characteristics - quantity, transferability and content - and how this determines which ML model(s) to use, as well as their output(s). Within the context of cell biological questions and applications, we further discuss ML utility range, namely data curation, exploration, prediction and explanation, and what they entail and translate to in the context of microscopy. Finally, we explore the challenges, common artefacts and risks associated with ML in microscopy. Building on insights from other fields, we propose how these pitfalls might be mitigated for in microscopy.

Gut Analysis Toolbox - automating quantitative analysis of enteric neurons

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples.

Deep learning permits imaging of multiple structures with the same fluorophores

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

Enhancing quantitative imaging to study DNA damage response: A guide to automated liquid handling and imaging

Laboratory automation and quantitative high-content imaging are pivotal in advancing diverse scientific fields. These innovative techniques alleviate the burden of manual labour, facilitating large-scale experiments characterized by exceptional reproducibility. Nonetheless, the seamless integration of such systems continues to pose a constant challenge in many laboratories. Here, we present a meticulously designed workflow that automates the immunofluorescence staining process, coupled with quantitative high-content imaging to study DNA damage signalling as an example. This is achieved by using an automatic liquid handling system for sample preparation. Additionally, we also offer practical recommendations aimed at ensuring the reproducibility and scalability of experimental outcomes. We illustrate the high level of efficiency and reproducibility achieved through the implementation of the liquid handling system but also addresses the associated challenges. Furthermore, we extend the discussion into critical aspects such as microscope selection, optimal objective choices, and considerations for high-content image acquisition. Our study streamlines the image analysis process, offering valuable recommendations for efficient computing resources and the integration of cutting-edge deep learning techniques. Emphasizing the paramount importance of robust data management systems aligned with the FAIR data principles, we provide practical insights into suitable storage options and effective data visualization techniques. Together, our work serves as a comprehensive guide for life science laboratories seeking to elevate their high-content quantitative imaging capabilities through the seamless integration of advanced laboratory automation.

Fluorescent Reporters, Imaging, and Artificial Intelligence Toolkits to Monitor and Quantify Autophagy, Heterophagy, and Lysosomal Trafficking Fluxes

Lysosomal compartments control the clearance of cell-own material (autophagy) or of material that cells endocytose from the external environment (heterophagy) to warrant supply of nutrients, to eliminate macromolecules or parts of organelles present in excess, aged, or containing toxic material. Inherited or sporadic mutations in lysosomal proteins and enzymes may hamper their folding in the endoplasmic reticulum (ER) and their lysosomal transport via the Golgi compartment, resulting in lysosomal dysfunction and storage disorders. Defective cargo delivery to lysosomal compartments is harmful to cells and organs since it causes accumulation of toxic compounds and defective organellar homeostasis. Assessment of resident proteins and cargo fluxes to the lysosomal compartments is crucial for the mechanistic dissection of intracellular transport and catabolic events. It might be combined with high-throughput screenings to identify cellular, chemical, or pharmacological modulators of these events that may find therapeutic use for autophagy-related and lysosomal storage disorders. Here, discuss qualitative, quantitative and chronologic monitoring of autophagic, heterophagic and lysosomal protein trafficking in fixed and live cells, which relies on fluorescent single and tandem reporters used in combination with biochemical, flow cytometry, light and electron microscopy approaches implemented by artificial intelligence-based technology.

Arkitekt: streaming analysis and real-time workflows for microscopy

Quantitative microscopy workflows have evolved dramatically over the past years, progressively becoming more complex with the emergence of deep learning. Long-standing challenges such as three-dimensional segmentation of complex microscopy data can finally be addressed, and new imaging modalities are breaking records in both resolution and acquisition speed, generating gigabytes if not terabytes of data per day. With this shift in bioimage workflows comes an increasing need for efficient orchestration and data management, necessitating multitool interoperability and the ability to span dedicated computing resources. However, existing solutions are still limited in their flexibility and scalability and are usually restricted to offline analysis. Here we introduce Arkitekt, an open-source middleman between users and bioimage apps that enables complex quantitative microscopy workflows in real time. It allows the orchestration of popular bioimage software locally or remotely in a reliable and efficient manner. It includes visualization and analysis modules, but also mechanisms to execute source code and pilot acquisition software, making 'smart microscopy' a reality.

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.

Similarity Metrics for Subcellular Analysis of FRET Microscopy Videos

Understanding the heterogeneity of molecular environments within cells is an outstanding challenge of great fundamental and technological interest. Cells are organized into specialized compartments, each with distinct functions. These compartments exhibit dynamic heterogeneity under high-resolution microscopy, which reflects fluctuations in molecular populations, concentrations, and spatial distributions. To enhance our comprehension of the spatial relationships among molecules within cells, it is crucial to analyze images of high-resolution microscopy by clustering individual pixels according to their visible spatial properties and their temporal evolution. Here, we evaluate the effectiveness of similarity metrics based on their ability to facilitate fast and accurate data analysis in time and space. We discuss the capability of these metrics to differentiate subcellular localization, kinetics, and structures of protein-RNA interactions in Forster resonance energy transfer (FRET) microscopy videos, illustrated by a practical example from recent literature. Our results suggest that using the correlation similarity metric to cluster pixels of high-resolution microscopy data should improve the analysis of high-dimensional microscopy data in a wide range of applications.

Ultrack: pushing the limits of cell tracking across biological scales

Tracking live cells across 2D, 3D, and multi-channel time-lapse recordings is crucial for understanding tissue-scale biological processes. Despite advancements in imaging technology, achieving accurate cell tracking remains challenging, particularly in complex and crowded tissues where cell segmentation is often ambiguous. We present Ultrack, a versatile and scalable cell-tracking method that tackles this challenge by considering candidate segmentations derived from multiple algorithms and parameter sets. Ultrack employs temporal consistency to select optimal segments, ensuring robust performance even under segmentation uncertainty. We validate our method on diverse datasets, including terabyte-scale developmental time-lapses of zebrafish, fruit fly, and nematode embryos, as well as multi-color and label-free cellular imaging. We show that Ultrack achieves state-of-the-art performance on the Cell Tracking Challenge and demonstrates superior accuracy in tracking densely packed embryonic cells over extended periods. Moreover, we propose an approach to tracking validation via dual-channel sparse labeling that enables high-fidelity ground truth generation, pushing the boundaries of long-term cell tracking assessment. Our method is freely available as a Python package with Fiji and napari plugins and can be deployed in a high-performance computing environment, facilitating widespread adoption by the research community.

TLNRD1 is a CCM complex component and regulates endothelial barrier integrity

We previously identified talin rod domain-containing protein 1 (TLNRD1) as a potent actin-bundling protein in vitro. Here, we report that TLNRD1 is expressed in the vasculature in vivo. Its depletion leads to vascular abnormalities in vivo and modulation of endothelial cell monolayer integrity in vitro. We demonstrate that TLNRD1 is a component of the cerebral cavernous malformations (CCM) complex through its direct interaction with CCM2, which is mediated by a hydrophobic C-terminal helix in CCM2 that attaches to a hydrophobic groove on the four-helix domain of TLNRD1. Disruption of this binding interface leads to CCM2 and TLNRD1 accumulation in the nucleus and actin fibers. Our findings indicate that CCM2 controls TLNRD1 localization to the cytoplasm and inhibits its actin-bundling activity and that the CCM2-TLNRD1 interaction impacts endothelial actin stress fiber and focal adhesion formation. Based on these results, we propose a new pathway by which the CCM complex modulates the actin cytoskeleton and vascular integrity.

Quantitative microbiology with widefield microscopy: navigating optical artefacts for accurate interpretations

Time-resolved live-cell imaging using widefield microscopy is instrumental in quantitative microbiology research. It allows researchers to track and measure the size, shape, and content of individual microbial cells over time. However, the small size of microbial cells poses a significant challenge in interpreting image data, as their dimensions approache that of the microscope's depth of field, and they begin to experience significant diffraction effects. As a result, 2D widefield images of microbial cells contain projected 3D information, blurred by the 3D point spread function. In this study, we employed simulations and targeted experiments to investigate the impact of diffraction and projection on our ability to quantify the size and content of microbial cells from 2D microscopic images. This study points to some new and often unconsidered artefacts resulting from the interplay of projection and diffraction effects, within the context of quantitative microbiology. These artefacts introduce substantial errors and biases in size, fluorescence quantification, and even single-molecule counting, making the elimination of these errors a complex task. Awareness of these artefacts is crucial for designing strategies to accurately interpret micrographs of microbes. To address this, we present new experimental designs and machine learning-based analysis methods that account for these effects, resulting in accurate quantification of microbiological processes.

STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level and whole-slide scale deconvolution, convolution, and clustering for both low- and high-resolution spots. STIE characterizes cell-type-specific gene expression and demonstrates outperforming concordance with true cell-type-specific transcriptomic signatures than the other spot- and subspot-level methods. Furthermore, STIE reveals the single-cell level insights, for instance, lower actual spot resolution than its reported spot size, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatial cell-cell interactions at the single-cell level other than spot level.

Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images

Although quantitative analysis of biological images demands precise extraction of specific organelles or cells, it remains challenging in broad-field grayscale images, where traditional thresholding methods have been hampered due to complex image features. Nevertheless, rapidly growing artificial intelligence technology is overcoming obstacles. We previously reported the fine-tuned apodized phase-contrast microscopy system to capture high-resolution, label-free images of organelle dynamics in unstained living cells (Shimasaki, K. et al. (2024). Cell Struct. Funct., 49: 21-29). We here showed machine learning-based segmentation models for subcellular targeted objects in phase-contrast images using fluorescent markers as origins of ground truth masks. This method enables accurate segmentation of organelles in high-resolution phase-contrast images, providing a practical framework for studying cellular dynamics in unstained living cells.Key words: label-free imaging, organelle dynamics, apodized phase contrast, deep learning-based segmentation.

Anisotropic DBSCAN for 3D SMLM Data Clustering

Single-molecule localization microscopy (SMLM) advanced biological discoveries beyond the diffraction limit. Various implementations enable 3D SMLM to reconstruct volumetric cell images. Yet, the inherent anisotropic point spread function of optical microscopes often limits the localization precision in the axial direction compared to the lateral precision. Such localization anisotropy could also expand spherical cellular structures to ellipsoidal cellular structures. Structure identification, however, is often performed using DBSCAN cluster algorithms, considering an isotropic search volume. Here, we show that an anisotropic DBSCAN search volume identifies anisotropic clusters more reliably using simulated ground truth data sets. Given experimental localization precisions, we suggest optimized search parameters based on an expanded computational grid search and show an enhanced performance of anisotropic DBSCAN amidst variations in localization precision. We demonstrate the capability of anisotropic DBSCAN on experimental data and anticipate that the algorithm allows for a more rigorous identification of clusters in cells, considering the anisotropic localization precisions of astigmatism-based 3D SMLM.

A multiparametric analysis including single-cell and subcellular feature assessment reveals differential behavior of spheroid cultures on distinct ultra-low attachment plate types

Spheroids have become principal three-dimensional models to study cancer, developmental processes, and drug efficacy. Single-cell analysis techniques have emerged as ideal tools to gauge the complexity of cellular responses in these models. However, the single-cell quantitative assessment based on 3D-microscopic data of the subcellular distribution of fluorescence markers, such as the nuclear/cytoplasm ratio of transcription factors, has largely remained elusive. For spheroid generation, ultra-low attachment plates are noteworthy due to their simplicity, compatibility with automation, and experimental and commercial accessibility. However, it is unknown whether and to what degree the plate type impacts spheroid formation and biology. This study developed a novel AI-based pipeline for the analysis of 3D-confocal data of optically cleared large spheroids at the wholemount, single-cell, and sub-cellular levels. To identify relevant samples for the pipeline, automated brightfield microscopy was employed to systematically compare the size and eccentricity of spheroids formed in six different plate types using four distinct human cell lines. This showed that all plate types exhibited similar spheroid-forming capabilities and the gross patterns of growth or shrinkage during 4 days after seeding were comparable. Yet, size and eccentricity varied systematically among specific cell lines and plate types. Based on this prescreen, spheroids of HaCaT keratinocytes and HT-29 cancer cells were further assessed. In HaCaT spheroids, the in-depth analysis revealed a correlation between spheroid size, cell proliferation, and the nuclear/cytoplasm ratio of the transcriptional coactivator, YAP1, as well as an inverse correlation with respect to cell differentiation. These findings, yielded with a spheroid model and at a single-cell level, corroborate earlier concepts of the role of YAP1 in cell proliferation and differentiation of keratinocytes in human skin. Further, the results show that the plate type may influence the outcome of experimental campaigns and that it is advisable to scan different plate types for the optimal configuration during a specific investigation.

In silico labeling in cell biology: Potential and limitations

In silico labeling is the computational cross-modality image translation where the output modality is a subcellular marker that is not specifically encoded in the input image, for example, in silico localization of organelles from transmitted light images. In principle, in silico labeling has the potential to facilitate rapid live imaging of multiple organelles with reduced photobleaching and phototoxicity, a technology enabling a major leap toward understanding the cell as an integrated complex system. However, five years have passed since feasibility was attained, without any demonstration of using in silico labeling to uncover new biological insight. In here, we discuss the current state of in silico labeling, the limitations preventing it from becoming a practical tool, and how we can overcome these limitations to reach its full potential.

CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data

In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.

Automatic classification of normal and abnormal cell division using deep learning

In recent years, there has been a surge in the development of methods for cell segmentation and tracking, with initiatives like the Cell Tracking Challenge driving progress in the field. Most studies focus on regular cell population videos in which cells are segmented and followed, and parental relationships annotated. However, DNA damage induced by genotoxic drugs or ionizing radiation produces additional abnormal events since it leads to behaviors like abnormal cell divisions (resulting in a number of daughters different from two) and cell death. With this in mind, we developed an automatic mitosis classifier to categorize small mitosis image sequences centered around one cell as "Normal" or "Abnormal." These mitosis sequences were extracted from videos of cell populations exposed to varying levels of radiation that affect the cell cycle's development. We explored several deep-learning architectures and found that a network with a ResNet50 backbone and including a Long Short-Term Memory (LSTM) layer produced the best results (mean F1-score: 0.93 ± 0.06). In the future, we plan to integrate this classifier with cell segmentation and tracking to build phylogenetic trees of the population after genomic stress.

Tissue clearing and imaging approaches for in toto analysis of the reproductive system†

New microscopy techniques in combination with tissue clearing protocols and emerging analytical approaches have presented researchers with the tools to understand dynamic biological processes in a three-dimensional context. This paves the road for the exploration of new research questions in reproductive biology, for which previous techniques have provided only approximate resolution. These new methodologies now allow for contextualized analysis of far-larger volumes than was previously possible. Tissue optical clearing and three-dimensional imaging techniques posit the bridging of molecular mechanisms, macroscopic morphogenic development, and maintenance of reproductive function into one cohesive and comprehensive understanding of the biology of the reproductive system. In this review, we present a survey of the various tissue clearing techniques and imaging systems, as they have been applied to the developing and adult reproductive system. We provide an overview of tools available for analysis of experimental data, giving particular attention to the emergence of artificial intelligence-assisted methods and their applicability to image analysis. We conclude with an evaluation of how novel image analysis approaches that have been applied to other organ systems could be incorporated into future experimental evaluation of reproductive biology.