Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to capture and quantify the transcriptomic profile of a single cell at a specific timepoint. Among the computational techniques used to predict cellular trajectories, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.

The ability to follow transcription in individual cells with live imaging has revealed key dynamical mechanisms of gene regulation. However, such measurements are lacking in the context of vertebrate embryos. We addressed this deficit by applying MS2-MCP mRNA labeling to the quantification of transcription in zebrafish, a model vertebrate. We developed a platform of transgenic organisms, light-sheet fluorescence microscopy, and optimized image analysis that enables visualization and quantification of MS2 reporters. With these tools, we obtained single-cell, real-time measurements of the transcriptional dynamics of the segmentation clock. Our measurements reveal that smooth clock protein oscillations arise from discrete transcriptional bursts that are organized in space and time. Together, these results highlight how measuring single-cell transcriptional activity in the context of vertebrate organisms can reveal unexpected features of gene regulation and how this data can fuel the dialogue between theory and experiment.

Tracking live cells across two-dimensional, three-dimensional (3D) and multichannel time-lapse recordings is crucial for understanding tissue-scale biological processes. Despite advancements in imaging technology, accurately tracking cells 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 leverages 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-lapse recordings of zebrafish, fruit fly and nematode embryos, as well as multicolor and label-free cellular imaging. We demonstrate that Ultrack achieves superior or comparable performance in the cell tracking challenge, particularly when tracking densely packed 3D 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.

A key challenge in development is understanding how complex organisms physically coordinate the morphogenesis of multiple tissues. Here, using biophysical approaches, we investigate how muscles under the epidermis specifically stimulate the extension of anterior-posterior (AP)-oriented epidermal adherens junctions during late C. elegans embryonic elongation. First, light-sheet imaging shows that asynchronous patterns of muscle contractions drive embryo rotations. In turn, junctions between the lateral and dorso-ventral epidermis repeatedly oscillate between a folded, hypotensed state and an extended, hypertensed state. Second, fluorescence recovery after photobleaching (FRAP) analysis of an E-cadherin::GFP construct shows that muscle contractions stimulate E-cadherin turnover. Moreover, a mechano-chemical model backed by genetic tests suggests that E-cadherin trafficking controls junction elongation due to lower line tension. Altogether, our results illustrate how muscle contractions fluidize epidermal adherens junctions, which, combined with anisotropic tension in the epidermis, drive their polarized extension.

Biology is at the precipice of a new era where AI accelerates and amplifies the ability to study how cells operate, organize, and work as systems, revealing why disease happens and how to correct it. Organizations globally are prioritizing AI to accelerate basic research, drug discovery, personalized medicine, and synthetic biology. However, despite these opportunities, scientific data have proven a bottleneck, and progress has been slow and fragmented. Unless the scientific community takes a technology-led, community-focused approach to scaling and harnessing data, we will fail to capture this opportunity to drive new insights and biological discovery. The data bottleneck presents a unique paradox. It is increasingly simple to generate huge data volumes, thanks to expanding imaging datasets and plummeting sequencing costs, but scientists lack standards and tooling for large biological datasets, preventing integration into a multimodal foundational dataset that unlocks generalizable models of cellular and tissue function. This contradiction highlights two interrelated problems: abundant data that's difficult to manage, and a lack of data resources with necessary quality and utility to realize AI's potential in biology. Science must forge a collective approach enabling distributed contributions to combine into cohesive, powerful datasets transcending individual purposes. Here, we present a technological and data generation roadmap for scaling scientific impact. We outline AI's opportunity, mechanisms to scale data generation, the need for multi-modal measurements, and means to pool resources, standardize approaches, and collectively build the foundation enabling AI's full potential in biological discovery.

We introduce inTRACKtive, an innovative web-based tool for interactive visualization and sharing of large 3D cell tracking datasets, eliminating the need for software installations or data downloads. Built with modern web technologies, inTRACKtive enables researchers to explore cell-tracking results from terabyte-scale microscopy data, conduct virtual fate-mapping experiments, and share these results via simple hyperlinks. The platform powers the Virtual Embryo Zoo, an online resource showcasing cell tracking datasets from state-of-the-art light-sheet embryonic microscopy of six model organisms. inTRACKtive's open-source code allows users to visualize their own data or host customized viewer instances. By providing easy access to complex tracking datasets, inTRACKtive offers a versatile, interactive, collaborative tool for developmental biology. ### Competing Interest Statement The authors have declared no competing interest.

Elucidating organismal developmental processes requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by reconstructions of cellular trajectories. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate-mapping experiments. To demonstrate the versatility of our multimodal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of neuro-mesodermal progenitors (NMPs) and the origins of a joint kidney-hemangioblast progenitor population.

Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and cell state, enabling investigations across diverse fields such as developmental biology, immunology, and cancer biology. However, obtaining a high-quality single-cell suspension is still challenging, particularly when starting with limited materials like Zebrafish embryos, a powerful animal model for studying developmental processes and human diseases. Here, we present an optimized protocol for isolating single cells from individual zebrafish embryos, offering a valuable resource for researchers interested in working with limited starting material. The protocol facilitates unique investigations utilizing individual embryos, such as inter-individual genetic differences and embryo-specific lineage tracing analysis. Using a refined single-cell isolation protocol alongside zebrafish as a model organism, researchers can access a resource for exploring the emergence of all types and states of cells, advancing our understanding of cellular processes and disease mechanisms.

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.

In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can solve problems containing millions of segmentation instances in terabyte-scale 3D+t datasets; (ii) It achieves competitive results with or without deep learning, bypassing the requirement of 3D annotated data, that is scarce in the fluorescence microscopy field. The proposed method computes cell tracks and segments using a hierarchy of segmentation hypotheses and selects disjoint segments by maximizing the overlap between adjacent frames. We show that this method is the first to achieve state-of-the-art in both nuclei- and membrane-based cell tracking by evaluating it on the 2D epithelial cell benchmark and 3D images from the cell tracking challenge. Furthermore, it has a faster integer linear programming formulation, and the framework is flexible, supporting segmentations from individual off-the-shelf cell segmentation models or their combination as an ensemble. The code is available as supplementary material.

Vertebrate radial glia progenitors (RGPs), the principal neural stem cells, balance self-renewal and differentiation through asymmetric cell division (ACD), during which unequal inheritance of centrosomes is observed. Mechanistically, how centrosome asymmetry leads to distinct daughter cell fate remains largely unknown. Here we find that the centrosome protein Pericentriolar Material 1 (Pcm1), asymmetrically distributed at the centrosomes, regulates polarized endosome dynamics and RGP fate. In vivo time-lapse imaging and nanoscale-resolution expansion microscopy of zebrafish embryonic RGPs detect Pcm1 on Notch ligand-containing endosomes, in a complex with the polarity regulator Par-3 and dynein motor. Loss of pcm1 disrupts endosome dynamics, with clonal analysis uncovering increased neuronal production at the expense of progenitors. Pcm1 facilitates an exchange of Rab5b (early) for Rab11a (recycling) endosome markers and promotes the formation of Par-3 and dynein macromolecular complexes on recycling endosomes. Finally, in human-induced pluripotent stem cell-derived brain organoids, PCM1 shows asymmetry and co-localization with PARD3 and RAB11A in mitotic neural progenitors. Our data reveal a new mechanism by which centrosome asymmetry is conveyed by Pcm1 to polarize endosome dynamics and Notch signaling in regulating ACD and progenitor fate.

The field of developmental biology has declined in prominence in recent decades, with off-shoots from the field becoming more fashionable and highly funded. This has created inequity in discovery and opportunity, partly due to the perception that the field is antiquated or not cutting edge. A 'think tank'of scientists from multiple developmental biology-related disciplines came together to define specific challenges in the field that may have inhibited innovation, and to provide tangible solutions to some of the issues facing developmental biology. The community suggestions include a call to the community to help 'rebrand' the field, alongside proposals for additional funding apparatuses, frameworks for interdisciplinary innovative collaborations, pedagogical access, improved science communication, increased diversity and inclusion, and equity of resources to provide maximal impact to the community.

During embryonic development, gene regulatory networks (GRNs) drive molecular differentiation of cell types. However, the temporal dynamics of these networks remain poorly understood. Here, we present Zebrahub-Multiome, a comprehensive, time-resolved atlas of zebrafish embryogenesis, integrating single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) from over 94,000 cells sampled across six key developmental stages (10 to 24 hours post-fertilization). Our analysis reveals early-stage GRNs shared across multiple lineages, followed by the emergence of lineage-specific regulatory programs during later stages. We also observe a shift in transcription factor (TF) influence from broad, multi-lineage roles in early development to more specialized, cell-type-specific functions as development progresses. Using in silico genetic perturbations, we highlight the dynamic role of TFs in driving cell fate decisions, emphasizing the gradual specialization of regulatory circuits. All data and analyses are made accessible through an interactive web portal, enabling users to explore zebrafish gene regulatory dynamics across time and cell types. This resource provides a foundation for hypothesis generation and deeper insights into vertebrate development. ### Competing Interest Statement The authors have declared no competing interest.

The field of bioimage analysis is poised for a major transformation, owing to advancements in imaging technologies and artificial intelligence. The emergence of multimodal foundation models — which are akin to large language models (such as ChatGPT) but are capable of comprehending and processing biological images — holds great potential for ushering in a revolutionary era in bioimage analysis.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

SARS-CoV-2 infection primarily targets the lung but may also damage other organs, including the brain, heart, kidney, and intestine. Central nervous system (CNS) pathologies include loss of smell and taste, headache, delirium, acute psychosis, seizures, and stroke. Coronavirus disease 2019 (COVID-19) is frequently associated with neurological deficits, but how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces these effects remains unclear. Here, we show that astrocytes are readily infected by SARS-CoV-2, but surprisingly, neuropilin-1, not angiotensin-converting enzyme 2 (ACE2), serves as the principal receptor mediating cell entry. Infection is further positively modulated by the two-pore segment channel 2 (TPC2) protein that regulates membrane trafficking and endocytosis. Astrocyte infection produces a pathological response closely resembling reactive astrogliosis characterized by elevated type I interferon (IFN) production, increased inflammation, and the decreased expression of transporters of water, ions, choline, and neurotransmitters. These combined events initiated within astrocytes produce a hostile microenvironment that promotes the dysfunction and death of uninfected bystander neurons. IMPORTANCE SARS-CoV-2 infection primarily targets the lung but may also damage other organs, including the brain, heart, kidney, and intestine. Central nervous system (CNS) pathologies include loss of smell and taste, headache, delirium, acute psychosis, seizures, and stroke. Pathological loss of gray matter occurs in SARS-CoV-2 infection, but it is unclear whether this is due to direct viral infection, indirect effects associated with systemic inflammation, or both. Here, we used induced pluripotent stem cell (iPSC)-derived brain organoids and primary human astrocytes from the cerebral cortex to study direct SARS-CoV-2 infection. Our findings support a model where SARS-CoV-2 infection of astrocytes produces a panoply of changes in the expression of genes regulating innate immune signaling and inflammatory responses. The deregulation of these genes in astrocytes produces a microenvironment within the CNS that ultimately disrupts normal neuron function, promoting neuronal cell death and CNS deficits.

Asymmetric cell division (ACD) is fundamental for balancing cell proliferation and differentiation in metazoans. During active neurogenesis in the developing zebrafish forebrain, radial glia progenitors (RGPs) mainly undergo ACD to produce one daughter with high activity of Delta/Notch signaling (proliferative cell fate) and another daughter with low Delta/Notch signaling (differentiative cell fate). The cell polarity protein partitioning-defective 3 (Par-3) is critical for regulating this process. To understand how polarized Par-3 on the cell cortex can lead to differential Notch activity in the nuclei of daughter cells, we combined an anti-Delta D (Dld) -atto 647N antibody uptake assay with label retention expansion microscopy (LR-ExM), to obtain high resolution immunofluorescent images of Par-3, dynein light intermediate chain 1 (Dlic1), and Dld endosomes in mitotic RGPs. We then developed a protocol for analyzing the colocalization of Par-3, Dlic1, and endosomal DeltaD, using JACoP (Just Another Co-localization Plugin) in ImageJ software (Bolte and Cordelières, 2006). Through such analyses, we have shown that cytosolic Par-3 is associated with Dlic1 on Dld endosomes. Our work demonstrates a direct involvement of Par-3 in dynein-mediated polarized transport of Notch signaling endosomes. This bio-protocol may be generalizable for analysis of protein co-localization in any cryosectioned and immunostained tissue samples.

Recent developments in Oblique Plane Microscopy (OPM) have shown that it can achieve high spatio-temporal resolution. Here we describe a single objective light-sheet microscope based on oblique plane illumination that achieves: (i) large field of view and high-resolution imaging via a custom remote focusing objective; (ii) fast volumetric imaging by means of light-sheet stabilised stage scanning – a novel scanning modality that extends the imaging volume without compromising imaging speed nor quality; (iii) multi-view imaging by alternating the orientation of light-sheet illumination and detection to improve the image quality on large samples; (iv) simpler design and ergonomics by remote placement of coverslips to allow inverted imaging, enabling imaging across scales in a high-throughput format. Overall, we achieved a resolution of 450 nm laterally and 2 μm axially and a field of view of 3000 μm × 800 μm × 300 μm. We demonstrate the speed, field of view, resolution and versatility of our novel instrument by imaging various systems, including zebrafish whole brain activity, Drosophila egg chamber development, and zebrafish development – up to nine embryos simultaneously.