A segmentation clock patterns cellular differentiation in a bacterial biofilm.

Contrary to multicellular organisms that display segmentation during development, communities of unicellular organisms are believed to be devoid of such sophisticated patterning. Unexpectedly, we find that the gene expression underlying the nitrogen stress response of a developing Bacillus subtilis biofilm becomes organized into a ring-like pattern. Mathematical modeling and genetic probing of the underlying circuit indicate that this patterning is generated by a clock and wavefront mechanism, similar to that driving vertebrate somitogenesis. We experimentally validated this hypothesis by showing that predicted nutrient conditions can even lead to multiple concentric rings, resembling segments. We additionally confirmed that this patterning mechanism is driven by cell-autonomous oscillations. Importantly, we show that the clock and wavefront process also spatially patterns sporulation within the biofilm. Together, these findings reveal a biofilm segmentation clock that organizes cellular differentiation in space and time, thereby challenging the paradigm that such patterning mechanisms are exclusive to plant and animal development.

EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization.

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 µm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis of scRNA-seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

Whole-body integration of gene expression and single-cell morphology.

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets.

Modulation of Phase Shift between Wnt and Notch Signaling Oscillations Controls Mesoderm Segmentation.

How signaling dynamics encode information is a central question in biology. During vertebrate development, dynamic Notch signaling oscillations control segmentation of the presomitic mesoderm (PSM). In mouse embryos, this molecular clock comprises signaling oscillations of several pathways, i.e., Notch, Wnt, and FGF signaling. Here, we directly address the role of the relative timing between Wnt and Notch signaling oscillations during PSM patterning. To this end, we developed a new experimental strategy using microfluidics-based entrainment that enables specific control of the rhythm of segmentation clock oscillations. Using this approach, we find that Wnt and Notch signaling are coupled at the level of their oscillation dynamics. Furthermore, we provide functional evidence that the oscillation phase shift between Wnt and Notch signaling is critical for PSM segmentation. Our work hence reveals that dynamic signaling, i.e., the relative timing between oscillatory signals, encodes essential information during multicellular development.

The Fly Brain Atlas.

The brain's synaptic networks endow an animal with powerfully adaptive biological behavior. Maps of such synaptic circuits densely reconstructed in those model brains that can be examined and manipulated by genetic means offer the best prospect for understanding the underlying biological bases of behavior. That prospect is now technologically feasible and a scientifically enabling possibility in neurobiology, much as genomics has been in molecular biology and genetics. In Drosophila, two major advances are in electron microscopic technology, using focused ion beam-scanning electron microscopy (FIB-SEM) milling to capture and align digital images, and in computer-aided reconstruction of neuron morphologies. The last decade has witnessed enormous progress in detailed knowledge of the actual synaptic circuits formed by real neurons. Advances in various brain regions that heralded identification of the motion-sensing circuits in the optic lobe are now extending to other brain regions, with the prospect of encompassing the fly's entire nervous system, both brain and ventral nerve cord.

Excitable Dynamics and Yap-Dependent Mechanical Cues Drive the Segmentation Clock.

The periodic segmentation of the vertebrate body axis into somites, and later vertebrae, relies on a genetic oscillator (the segmentation clock) driving the rhythmic activity of signaling pathways in the presomitic mesoderm (PSM). To understand whether oscillations are an intrinsic property of individual cells or represent a population-level phenomenon, we established culture conditions for stable oscillations at the cellular level. This system was used to demonstrate that oscillations are a collective property of PSM cells that can be actively triggered in vitro by a dynamical quorum sensing signal involving Yap and Notch signaling. Manipulation of Yap-dependent mechanical cues is sufficient to predictably switch isolated PSM cells from a quiescent to an oscillatory state in vitro, a behavior reminiscent of excitability in other systems. Together, our work argues that the segmentation clock behaves as an excitable system, introducing a broader paradigm to study such dynamics in vertebrate morphogenesis.

Oscillatory control of embryonic development.

Proper embryonic development depends on the timely progression of a genetic program. One of the key mechanisms for achieving precise control of developmental timing is to use gene expression oscillations. In this Review, we examine how gene expression oscillations encode temporal information during vertebrate embryonic development by discussing the gene expression oscillations occurring during somitogenesis, neurogenesis, myogenesis and pancreas development. These oscillations play important but varied physiological functions in different contexts. Oscillations control the period of somite formation during somitogenesis, whereas they regulate the proliferation-to-differentiation switch of stem cells and progenitor cells during neurogenesis, myogenesis and pancreas development. We describe the similarities and differences of the expression pattern in space (i.e. whether oscillations are synchronous or asynchronous across neighboring cells) and in time (i.e. different time scales) of mammalian Hes/zebrafish Her genes and their targets in different tissues. We further summarize experimental evidence for the functional role of their oscillations. Finally, we discuss the outstanding questions for future research.

Nuclear instance segmentation and tracking for preimplantation mouse embryos.

For investigations into fate specification and morphogenesis in time-lapse images of preimplantation embryos, automated 3D instance segmentation and tracking of nuclei are invaluable. Low signal-to-noise ratio, high voxel anisotropy, high nuclear density, and variable nuclear shapes can limit the performance of segmentation methods, while tracking is complicated by cell divisions, low frame rates, and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of annotated 3D data. Here we first report a novel mouse line expressing near-infrared nuclear reporter H2B-miRFP720. We then generate a dataset (termed BlastoSPIM) of 3D images of H2B-miRFP720-expressing embryos with ground truth for nuclear instances. Using BlastoSPIM, we benchmark seven convolutional neural networks and identify Stardist-3D as the most accurate instance segmentation method. With our BlastoSPIM-trained Stardist-3D models, we construct a complete pipeline for nuclear instance segmentation and lineage tracking from the 8-cell stage to the end of preimplantation development (>100 nuclei). Finally, we demonstrate BlastoSPIM's usefulness as pre-train data for related problems, both for a different imaging modality and for different model systems.

A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.

We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading tool that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues.

Temporal control of RNAi reveals both robust and labile feedback loops in the segmentation clock of the red flour beetle.

Animals from all major clades have evolved a segmented trunk, reflected in the human spine or the insect segments. These units emerge during embryogenesis from a posterior segment addition zone (SAZ), where repetitive gene activity is regulated by a mechanism described by the clock and wavefront/speed gradient model. In the red flour beetle Tribolium castaneum, RNA interference (RNAi) has been used to continuously knock down the function of primary pair-rule genes (pPRGs), caudal or Wnt pathway components, which has led to the complete breakdown of segmentation. However, it has remained untested, if this breakdown was reversible by bringing the missing gene function back to the system. To fill this gap, we established a transgenic system in T. castaneum, which allows blocking an ongoing RNAi effect with temporal control by expressing a viral inhibitor of RNAi via heat shock. We show that the T. castaneum segmentation machinery was able to reestablish after RNAi targeting the pPRGs Tc-eve, Tc-odd, and Tc-runt was blocked. However, we observed no rescue after blocking RNAi targeting Wnt pathway components. We conclude that the insect segmentation system contains both robust feedback loops that can reestablish and labile feedback loops that break down irreversibly. This combination may reconcile conflicting needs of the system: Labile systems controlling initiation and maintenance of the SAZ ensure that only one SAZ is formed. Robust feedback loops confer developmental robustness toward external disturbances.

Breaking (musical) boundaries by investigating brain dynamics of event segmentation during real-life music-listening.

The perception of musical phrase boundaries is a critical aspect of human musical experience: It allows us to organize, understand, derive pleasure from, and remember music. Identifying boundaries is a prerequisite for segmenting music into meaningful chunks, facilitating efficient processing and storage while providing an enjoyable, fulfilling listening experience through the anticipation of upcoming musical events. Expanding on Sridharan et al.'s [Neuron 55, 521-532 (2007)] work on coarse musical boundaries between symphonic movements, we examined finer-grained boundaries. We measured the fMRI responses of 18 musicians and 18 nonmusicians during music listening. Using general linear model, independent component analysis, and Granger causality, we observed heightened auditory integration in anticipation to musical boundaries, and an extensive decrease within the fronto-temporal-parietal network during and immediately following boundaries. Notably, responses were modulated by musicianship. Findings uncover the intricate interplay between musical structure, expertise, and cognitive processing, advancing our knowledge of how the brain makes sense of music.

Balancing the length of the distal tip by septins is key for stability and signalling function of primary cilia.

Primary cilia are antenna-like organelles required for signalling transduction. How cilia structure is mechanistically maintained at steady-state to promote signalling is largely unknown. Here, we define that mammalian primary cilia axonemes are formed by proximal segment (PS) and distal segment (DS) delineated by tubulin polyglutamylation-rich and -poor regions, respectively. The analysis of proximal/distal segmentation indicated that perturbations leading to cilia over-elongation influenced PS or DS length with a different impact on cilia behaviour. We identified septins as novel repressors of DS growth. We show that septins control the localisation of MKS3 and CEP290 required for a functional transition zone (TZ), and the cilia tip accumulation of the microtubule-capping kinesin KIF7, a cilia-growth inhibitor. Live-cell imaging and analysis of sonic-hedgehog (SHH) signalling activation established that DS over-extension increased cilia ectocytosis events and decreased SHH activation. Our data underlines the importance of understanding cilia segmentation for length control and cilia-dependent signalling.

An end-to-end pipeline based on open source deep learning tools for reliable analysis of complex 3D images of ovaries.

Computational analysis of bio-images by deep learning (DL) algorithms has made exceptional progress in recent years and has become much more accessible to non-specialists with the development of ready-to-use tools. The study of oogenesis mechanisms and female reproductive success has also recently benefited from the development of efficient protocols for three-dimensional (3D) imaging of ovaries. Such datasets have a great potential for generating new quantitative data but are, however, complex to analyze due to the lack of efficient workflows for 3D image analysis. Here, we have integrated two existing open-source DL tools, Noise2Void and Cellpose, into an analysis pipeline dedicated to 3D follicular content analysis, which is available on Fiji. Our pipeline was developed on larvae and adult medaka ovaries but was also successfully applied to different types of ovaries (trout, zebrafish and mouse). Image enhancement, Cellpose segmentation and post-processing of labels enabled automatic and accurate quantification of these 3D images, which exhibited irregular fluorescent staining, low autofluorescence signal or heterogeneous follicles sizes. In the future, this pipeline will be useful for extensive cellular phenotyping in fish or mammals for developmental or toxicology studies.

Molecular mechanism of synovial joint site specification and induction in developing vertebrate limbs.

The vertebrate appendage comprises three primary segments, the stylopod, zeugopod and autopod, each separated by joints. The molecular mechanisms governing the specification of joint sites, which define segment lengths and thereby limb architecture, remain largely unknown. Existing literature suggests that reciprocal gradients of retinoic acid (RA) and fibroblast growth factor (FGF) signaling define the expression domains of the putative segment markers Meis1, Hoxa11 and Hoxa13. Barx1 is expressed in the presumptive joint sites. Our data demonstrate that RA-FGF signaling gradients define the expression domain of Barx1 in the first presumptive joint site. When misexpressed, Barx1 induces ectopic interzone-like structures, and its loss of function partially blocks interzone development. Simultaneous perturbations of RA-FGF signaling gradients result in predictable shifts of Barx1 expression domains along the proximo-distal axis and, consequently, in the formation of repositioned joints. Our data suggest that during early limb bud development in chick, Meis1 and Hoxa11 expression domains are overlapping, whereas the Barx1 expression domain resides within the Hoxa11 expression domain. However, once the interzone is formed, the expression domains are refined and the Barx1 expression domain becomes congruent with the border of these two putative segment markers.

A systematic evaluation of computational methods for cell segmentation.

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.

MyoV: a deep learning-based tool for the automated quantification of muscle fibers.

Accurate approaches for quantifying muscle fibers are essential in biomedical research and meat production. In this study, we address the limitations of existing approaches for hematoxylin and eosin-stained muscle fibers by manually and semiautomatically labeling over 660 000 muscle fibers to create a large dataset. Subsequently, an automated image segmentation and quantification tool named MyoV is designed using mask regions with convolutional neural networks and a residual network and feature pyramid network as the backbone network. This design enables the tool to allow muscle fiber processing with different sizes and ages. MyoV, which achieves impressive detection rates of 0.93-0.96 and precision levels of 0.91-0.97, exhibits a superior performance in quantification, surpassing both manual methods and commonly employed algorithms and software, particularly for whole slide images (WSIs). Moreover, MyoV is proven as a powerful and suitable tool for various species with different muscle development, including mice, which are a crucial model for muscle disease diagnosis, and agricultural animals, which are a significant meat source for humans. Finally, we integrate this tool into visualization software with functions, such as segmentation, area determination and automatic labeling, allowing seamless processing for over 400 000 muscle fibers within a WSI, eliminating the model adjustment and providing researchers with an easy-to-use visual interface to browse functional options and realize muscle fiber quantification from WSIs.

On the core segmentation algorithms of copy number variation detection tools.

Shotgun sequencing is a high-throughput method used to detect copy number variants (CNVs). Although there are numerous CNV detection tools based on shotgun sequencing, their quality varies significantly, leading to performance discrepancies. Therefore, we conducted a comprehensive analysis of next-generation sequencing-based CNV detection tools over the past decade. Our findings revealed that the majority of mainstream tools employ similar detection rationale: calculates the so-called read depth signal from aligned sequencing reads and then segments the signal by utilizing either circular binary segmentation (CBS) or hidden Markov model (HMM). Hence, we compared the performance of those two core segmentation algorithms in CNV detection, considering varying sequencing depths, segment lengths and complex types of CNVs. To ensure a fair comparison, we designed a parametrical model using mainstream statistical distributions, which allows for pre-excluding bias correction such as guanine-cytosine (GC) content during the preprocessing step. The results indicate the following key points: (1) Under ideal conditions, CBS demonstrates high precision, while HMM exhibits a high recall rate. (2) For practical conditions, HMM is advantageous at lower sequencing depths, while CBS is more competitive in detecting small variant segments compared to HMM. (3) In case involving complex CNVs resembling real sequencing, HMM demonstrates more robustness compared with CBS. (4) When facing large-scale sequencing data, HMM costs less time compared with the CBS, while their memory usage is approximately equal. This can provide an important guidance and reference for researchers to develop new tools for CNV detection.

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net.

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence-based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet.

A Systems-Level Study Reveals Regulators of Membrane-less Organelles in Human Cells.

Membrane-less organelles (MLOs) are liquid-like subcellular compartments that form through phase separation of proteins and RNA. While their biophysical properties are increasingly understood, their regulation and the consequences of perturbed MLO states for cell physiology are less clear. To study the regulatory networks, we targeted 1,354 human genes and screened for morphological changes of nucleoli, Cajal bodies, splicing speckles, PML nuclear bodies (PML-NBs), cytoplasmic processing bodies, and stress granules. By multivariate analysis of MLO features we identified hundreds of genes that control MLO homeostasis. We discovered regulatory crosstalk between MLOs, and mapped hierarchical interactions between aberrant MLO states and cellular properties. We provide evidence that perturbation of pre-mRNA splicing results in stress granule formation and reveal that PML-NB abundance influences DNA replication rates and that PML-NBs are in turn controlled by HIP kinases. Together, our comprehensive dataset is an unprecedented resource for deciphering the regulation and biological functions of MLOs.

Founding the Wnt gene family: How wingless was found to be a positional signal and oncogene homolog.

The Wnt family of developmental regulators were named after the Drosophila segmentation gene wingless and the murine proto-oncogene int-1. Homology between these two genes connected oncogenesis to cell-cell signals in development. I review how wingless was initially characterized, and cloned, as part of the quest to identify developmental cell-to-cell signals, based on predictions of the Positional Information Model, and on the properties of homeotic and segmentation gene mutants. The requirements and cell-nonautonomy of wingless in patterning multiple embryonic and adult structures solidified its status as a candidate signaling molecule. The physical location of wingless mutations and transcription unit defined the gene and its developmental transcription pattern. When the Drosophila homolog of int-1 was then isolated, and predicted to encode a secreted proto-oncogene homolog, it's identity to the wingless gene confirmed that a developmental cell-cell signal had been identified and connected cancer to development.