Biology subjects

Royer, L. A.

Publications and source records attributed to Royer, L. A..

At least 19 recordsLinked to original sources

bioRxiv2026

gene2fish: a multi-gene spatial expression browser for in situ hybridization data across zebrafish development

Single-cell RNA-sequencing (scRNA-seq) atlases of zebrafish development have catalogued marker genes for hundreds of cell types, but transcriptomic data alone cannot reveal their expression patterns in the embryo. The systematically annotated Thisse high-throughput whole-mount in situ hybridization (ISH) collection at ZFIN provides spatial expression data across twelve developmental stages, yet the ZFIN web interface is organized around individual genes, making side-by-side cross-gene comparison laborious. We present gene2fish, a web application that displays ISH images from the Thisse dataset, linked directly to their ZFIN records, in a gene-by-developmental-stage grid. With gene2fish, researchers can quickly explore and validate the spatial expression of marker gene lists, and find the genes annotated to specific anatomical structures across developmental time. The application is part of Zebrahub and is freely available at https://zebrahub.sf.czbiohub.org/gene2fish.

developmental biology↗

bioRxiv2026

Luxar: Gaussian splatting for microscopy and scalable interactive web visualisation of multidimensional scientific data

Volumetric microscopy produces datasets too large to share as files and too rich to convey as still images, yet exploring them interactively means installing desktop software or building web infrastructure. Here we present Luxar, an open-source Python framework that compiles multidimensional data into compact archives that any modern browser renders from static file hosting. Fluorescence volumes are represented as mixtures of anisotropic Gaussians (Gaussian splats): the image becomes geometry that the GPU draws directly, with no voxel grid to rebuild. Splats share one scene with points, trajectories and surfaces. Blind-spot cross-validation sets the number of Gaussians without noise-free references. Across 17 volumes from 12 datasets and four modalities, this yields a median 99-fold (6- to 340-fold) size reduction at 26 to 67 dB peak signal-to-noise ratio (PSNR), in minutes per volume on one GPU. We demonstrate Luxar on a 500-timepoint embryogenesis recording, super-resolution localisations, single-cell atlases and lineage tracking, each explorable through a shareable link.

bioinformatics↗

bioRxiv2026

Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales

Predicting how cells respond to genetic and chemical perturbations is a central challenge in drug discovery and functional genomics. A growing ecosystem of specialized single-cell foundation models has been developed to address this problem, yet their practical advantage over domain-agnostic approaches remains unclear. Here we evaluate the power of Tabular Foundation Models such as TabICL and TabPFN, general-purpose pre-trained regression models, against domain-specific architectures including PRESAGE, scGPT, scLAMBDA, STACK and Prophet across four complementary evaluation settings: cell-level in-context cross-cell-type prediction, pseudobulk perturbation prediction on five Perturb-seq datasets of cell-lines, a genome-wide CRISPR screen in primary human CD4+ T cells, and embryo-level cell-type composition prediction in a zebrafish developmental perturbation atlas. In the cell-level cross-cell type perturbation prediction, Tabular Foundation Models perform on par or better than specialized models. On pseudobulk perturbation prediction, Tabular Foundation Models consistently out-perform specialized baselines across multiple evaluation metrics and datasets. On whole-embryo cell-type composition prediction, Tabular Foundation Models are competitive with specialized baselines. These results demonstrate that general-purpose tabular in-context learning provides a strong and scalable alternative to bespoke biological architectures for perturbation response modeling across cell systems and scales.

bioinformatics↗

bioRxiv2026

A multimodal perturbation atlas defines the phenotypic resolution of cellular morphology.

Modeling cellular behavior requires measurements that capture how cells evolve across time, environments, and interventions. Microscopy is uniquely suited to this goal: it is non-destructive and can be applied to living cells in their native context. Yet its phenotypic resolving power remains incompletely characterized relative to molecular assays. Here, we present a multimodal perturbation atlas of 1,000 pooled CRISPR knockouts in A549 cells, profiled by fluorescence microscopy (42 live, 13 fixed markers), label-free quantitative phase imaging of the same live cells (at single timepoints), and single-cell RNA sequencing (scRNA-seq). We develop deep learning frameworks to interpret the rich cell-biological signatures in these ~65M single-cell profiles. At matched reagent cost, phase imaging exceeds the phenotypic resolution of both fluorescence imaging and scRNA-seq, and more reliably recovers higher-order pathway organization. These results establish intrinsic morphology as a high-precision readout of cellular state, and lay a foundation for live-cell profiling of phenotypic trajectories.

systems biology↗

bioRxiv2026

DanioDecima: A DNA sequence-to-function model of zebrafish embryogenesis

Deep learning DNA sequence-to-function models offer the promise of gaining mechanistic insights into genome regulation, however their performance is often limited by data scarcity in the species of interest. We present DanioDecima, a zebrafish-specific model leveraging transfer learning from human and mouse-trained models to predict tissue- and cell-type-specific gene expression during zebrafish embryogenesis. Initializing DanioDecima with pretrained human and mouse Borzoi and Decima weights raises the median pseudobulk Pearson r sub-stantially across cell-types and improves gene-level correlations of test set genes. An in silico directed-evolution loop guided by DanioDecima scoring generated synthetic promoters whose motif architectures cluster by the expected target lineage. These findings exemplify a cross-species transfer learning methodology for sequence-to-function models, and position DanioDecima as a practical resource for zebrafish regulatory engineering.

genomics↗

bioRxiv2024

inTRACKtive - A Web-Based Tool for Interactive Cell Tracking Visualization

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. inTRACKtives 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.

developmental biology↗

bioRxiv2024

Zebrahub-Multiome: Uncovering Gene Regulatory Network Dynamics During Zebrafish Embryogenesis

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 single-cell multiomic atlas that captures chromatin accessibility and gene expression from 94,562 cells across six stages of zebrafish embryogenesis (10-24 hours post-fertilization), capturing key developmental stages from the end of gastrulation to the onset of organogenesis. By measuring regulatory element activity alongside transcriptional output from the same cells, we identify 640,000 cis-regulatory elements organized into 402 hierarchically structured modules corresponding to specific developmental pathways. Early embryonic stages employ broadly shared regulatory programs that progressively fragment into lineage-specific modules. Timeresolved gene regulatory network inference reveals that transcription factors undergo functional transitions - from multilineage regulators to specialized, lineage-committed factors. These quantitative measurements reveal the regulatory network rewiring that drives cell fate specification. Our interactive web portal (zebrahub.org/epigenomics) enables exploration of gene dynamics, regulatory networks, and perturbation predictions, providing a quantitative framework for understanding vertebrate developmental regulation.

developmental biology↗

bioRxiv2024

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.

developmental biology↗

bioRxiv2024

Repeated extrinsic and anisotropic mechanical inputs promote polarized adherens junction elongation

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, FRAP (Fluorescence Recovery After Photobleaching) 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 modulates 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, drives their polarized extension.

developmental biology↗

bioRxiv2024

Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts

Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to observe the progression of individual cells transcriptomic profiles over time. Among the computational techniques used to predict future cellular states, 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.

bioinformatics↗

bioRxiv2024

PCM1 conveys centrosome asymmetry to polarized endosome dynamics in regulating daughter cell fate

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.

cell biology↗

bioRxiv2024

Single-cell transcriptional dynamics in a living vertebrate

The ability to quantify transcriptional dynamics in individual cells via live imaging has revolutionized our understanding 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 transcriptional dynamics of the segmentation clock. Our measurements challenge the traditional view of smooth clock oscillations and instead suggest a model of 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.

developmental biology↗

bioRxiv2023

Zebrahub - Multimodal Zebrafish Developmental Atlas Reveals the State Transition Dynamics of Late Vertebrate Pluripotent Axial Progenitors

Elucidating the developmental processes of organisms 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 trajectory reconstructions. 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 multi-modal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of Neuro-Mesodermal Progenitors (NMPs). Our publicly accessible web-based platform, Zebrahub, is a foundational resource for studying developmental processes at both transcriptional and spatiotemporal levels, providing researchers with an integrated approach to exploring and analyzing the complexities of cellular lineages during zebrafish embryogenesis.

developmental biology↗

bioRxiv2021

ZAF -- An Open Source Fully Automated Feeder for Aquatic Facilities

In the past few decades, aquatic animals have become popular model organisms in biology, spurring a growing need for establishing aquatic facilities. Zebrafish are widely studied and relatively easy to culture using commercial systems. However, a challenging aspect of maintaining aquatic facilities is animal feeding, which is both time- and resource-consuming. We have developed an open-source fully automatic daily feeding system, Zebrafish Automatic Feeder (ZAF). ZAF is reliable, provides a standardized amount of food to every tank, is cost-efficient and easy to build. The advanced version, ZAF+, allows for the precise control of food distribution as a function of fish density per tank, and has a user-friendly interface. Both ZAF and ZAF+ are adaptable to any laboratory environment and facilitate the implementation of aquatic colonies. Here we provide all blueprints and instructions for building the mechanics, electronics, fluidics, as well as to setup the control software and its user-friendly graphical interface. Importantly, the design is modular and can be scaled to meet different user needs. Furthermore, our results show that ZAF and ZAF+ do not adversely affect zebrafish culture, enabling fully automatic feeding for any aquatic facility.

developmental biology↗

bioRxiv2021

OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization

Elucidating the wiring diagram of the human cell is a central goal of the post-genomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery, and uncovers that RNA-binding proteins form a specific sub-group defined by unique interaction and localization properties. Furthermore, we discover that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions. Paired with a fully interactive website opencell.czbiohub.org, we provide a resource for the quantitative cartography of human cellular organization.

cell biology↗

bioRxiv2021

Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization

Elucidating the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here, we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. cytoself leverages a self-supervised training scheme that does not require pre-existing knowledge, categories, or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes such as nuclear, cytoplasmic and vesicular, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoselfs ability to cluster proteins into organelles and protein complex clusters using a clustering score, and show that cytoself attains higher scores than previous unsupervised or self-supervised approaches. Finally, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret these features in the context of the fluorescence images, and analyze the performance contributions of the different components of our approach.

cell biology↗

bioRxiv2020

High-Resolution, Large Field-of-View, and Multi-View Single Objective Light-Sheet Microscopy

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 m axially and a field of view of 3000 m x 800 m x 300 m. 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.

developmental biology↗

bioRxiv2020

Optical Aberration Correction via Phase Diversity and Deep Learning

In modern microscopy imaging systems, optical components are carefully designed to obtain diffraction-limited resolution. However, live imaging of large biological samples rarely attains this limit because of sample induced refractive index inhomogeneities that create unknown temporally variant optical aberrations. Importantly, these aberrations are also spatially variant, thus making it challenging to correct over wide fields of view. Here, we present a framework for deep-learning based wide-field optical aberration sensing and correction. Our model consists of two modules which take in a set of three phase-diverse images and (i) estimate the wavefront phase in terms of its constituent Zernike polynomial coefficients and (ii) perform blind-deconvolution to yield an aberration-free image. First, we demonstrate our framework on simulations that incorporate optical aberrations, spatial variance, and realistic modelling of sensor noise. We find that our blind deconvolution achieves a 2-fold improvement in frequency support compared to input images, and our phase-estimation achieves a coefficient of determination (r2) of at least 80% when estimating astigmatism, spherical aberration and coma. Second, we show that our results mostly hold for strongly varying spatially-variant aberrations with a 30% resolution improvement. Third, we demonstrate practical usability for light-sheet microscopy: we show a 46% increase in frequency support even in imaging regions affected by detection and illumination scattering.

developmental biology↗