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PubMed2026

Development of the zebrafish foveal analogue: a quantitative atlas of high-acuity zone growth and retinal regionalisation.

The vertebrate retina contains specialised regions for high-acuity vision, exemplified by the human fovea and its zebrafish analogue, the high-acuity zone (HAZ). Despite the widespread use of zebrafish to model retinal disease, a stage-resolved quantitative reference describing normal eye, photoreceptor layer (PRL) and lens growth has been lacking. Here, we apply contrast-enhanced micro-computed tomography (micro-CT) to construct the first three-dimensional micro-CT normative atlas of wild-type zebrafish eye development across five larval stages [3, 5, 7, 10 and 18 days post-fertilisation (dpf)], mapping circumferential PRL thickness, eye and lens morphology, and compartment growth rates. Regional PRL thickening within the temporo-ventral region of the expected HAZ emerged by 5 dpf and was sustained by a localised redistribution of growth, persisting and extending towards the optic nerve through 18 dpf. The PRL, lens and eye grew through four phases, alternating between disproportionate PRL expansion and coordinated growth, while the eye remodelled from a nasal-dominant to a temporo-ventral-dominant form. This regional specialisation was protracted relative to gross ocular growth and could proceed independently of it, paralleling the extended postnatal maturation of the human fovea. This atlas provides a quantitative baseline for distinguishing disease-induced changes from normal variation, supporting zebrafish models of foveal hypoplasia and related disorders.

Animals↗
PubMed2026

Structure-informed theoretical modeling defines principles governing avidity in bivalent protein interactions.

In signaling cascades, signaling proteins often encode multiple domains or motifs, which presents the possibility for avidity -- where multivalent binding drastically increases interaction strength and duration. However, predicting and validating multivalent interactions that interact with avidity is a challenge. Here, we integrate mechanistic modeling, structure-based analysis, and experimental approaches as a framework for defining the conditions under which avidity plays a role. We explore the tandem SH2 domain family of interactions with bisphosphorylated partners as a multivalent archetype, which encompasses key secondary messengers in tyrosine kinase signaling networks. Theoretical modeling suggests that maximum avidity occurs with closely spaced tyrosine phosphorylation sites combined with moderate monovalent affinities - exactly around the innate range of SH2 domain affinity - or with phosphorylation sites separated by sufficiently flexible linkers. Surprisingly, despite sequence diversity, structure-based analysis showed relatively conserved three-dimensional spacing between SH2 domains across all tandem SH2 families, which we corroborate experimentally, suggesting evolutionary optimization for avidity interactions. The combination of structure-based analysis of domain spacing with available monovalent experimental data appears, along with iterative experimental refinement of biophysical parameters, can identify high affinity interactions of tandem SH2 domain recruitment to the EGFR C-terminal tail. Using these principles, we extended bivalent predictions into the full phosphoproteome space and structural parameterization of other partners of SH2 domain binding, providing resources and methods for more rapid expansion of bivalent analysis. These approaches lay the groundwork for larger utility in multivalent prediction and testing to help better understand protein interactions that drive cell signaling.

BLI↗
PubMed2013

Structural insights into adeno-associated virus serotype 5.

The adeno-associated viruses (AAVs) display differential cell binding, transduction, and antigenic characteristics specified by their capsid viral protein (VP) composition. Toward structure-function annotation, the crystal structure of AAV5, one of the most sequence diverse AAV serotypes, was determined to 3.45-Å resolution. The AAV5 VP and capsid conserve topological features previously described for other AAVs but uniquely differ in the surface-exposed HI loop between βH and βI of the core β-barrel motif and have pronounced conformational differences in two of the AAV surface variable regions (VRs), VR-IV and VR-VII. The HI loop is structurally conserved in other AAVs despite amino acid differences but is smaller in AAV5 due to an amino acid deletion. This HI loop is adjacent to VR-VII, which is largest in AAV5. The VR-IV, which forms the larger outermost finger-like loop contributing to the protrusions surrounding the icosahedral 3-fold axes of the AAVs, is shorter in AAV5, creating a smoother capsid surface topology. The HI loop plays a role in AAV capsid assembly and genome packaging, and VR-IV and VR-VII are associated with transduction and antigenic differences, respectively, between the AAVs. A comparison of interior capsid surface charge and volume of AAV5 to AAV2 and AAV4 showed a higher propensity of acidic residues but similar volumes, consistent with comparable DNA packaging capacities. This structure provided a three-dimensional (3D) template for functional annotation of the AAV5 capsid with respect to regions that confer assembly efficiency, dictate cellular transduction phenotypes, and control antigenicity.

Capsid Proteins↗
PubMed2024

Structural modelling and preventive strategy targeting of WSSV hub proteins to combat viral infection in shrimp Penaeus monodon.

White spot syndrome virus (WSSV) presents a considerable peril to the aquaculture sector, leading to notable financial consequences on a global scale. Previous studies have identified hub proteins, including WSSV051 and WSSV517, as essential binding elements in the protein interaction network of WSSV. This work further investigates the functional structures and potential applications of WSSV hub complexes in managing WSSV infection. Using computational methodologies, we have successfully generated comprehensive three-dimensional (3D) representations of hub proteins along with their three mutual binding counterparts, elucidating crucial interaction locations. The results of our study indicate that the WSSV051 hub protein demonstrates higher binding energy than WSSV517. Moreover, a unique motif, denoted as "S-S-x(5)-S-x(2)-P," was discovered among the binding proteins. This pattern perhaps contributes to the detection of partners by the hub proteins of WSSV. An antiviral strategy targeting WSSV hub proteins was demonstrated through the oral administration of dual hub double-stranded RNAs to the black tiger shrimp, Penaeus monodon, followed by a challenge assay. The findings demonstrate a decrease in shrimp mortality and a cessation of WSSV multiplication. In conclusion, our research unveils the structural features and dynamic interactions of hub complexes, shedding light on their significance in the WSSV protein network. This highlights the potential of hub protein-based interventions to mitigate the impact of WSSV infection in aquaculture.

Animals↗
PubMed2026

Plant cis-regulatory grammar: Decoding the multidimensional code of transcriptional regulation for programmable crop engineering.

Cis-regulatory elements (CREs) orchestrate the spatiotemporal precision of gene expression that underlies plant development, adaptation, and domestication. Decoding the cis-regulatory grammar of plant genomes remains a central challenge in modern biology, with profound implications for programmable crop engineering. Here, recent conceptual and technological advances are synthesized to reshape our understanding of plant CREs. This review first argues that CRE function is not only an intrinsic property of DNA sequence alone but also emerges from a multidimensional context, including chromatin accessibility, histone modifications, three-dimensional genome topology, and cell type-specific regulatory landscapes. Furthermore, the convergence of single-cell epigenomics, high-throughput functional assays, and CRISPR-based dissection has begun to unravel this contextual grammar, revealing the computational principles governing transcriptional regulation. Critically, we propose that artificial intelligence (AI) platforms are catalyzing an ongoing transition from descriptive discovery to predictive engineering, wherein these platforms outperform natural evolution in designing synthetic CREs. Finally, a roadmap is outlined toward a plant regulatory grammar foundation model, which will enable truly predictive engineering of gene expression when fine-tuned for specific tasks. Collectively, the integration of single-cell resolution maps, precise genome editing, AI-driven design, and regulatory-compliant delivery systems promises to transform our ability to reprogram plant gene regulation for next-generation agriculture, bridging the gap between foundational regulatory biology and tangible crop improvement.

artificial intelligence↗
PubMed2026

Chiron3D: an interpretable deep learning framework for understanding the DNA code of chromatin looping.

MOTIVATION: Three-dimensional folding of the genome into structures such as chromatin loops is essential for gene regulation. Current experimental methods for mapping these structures, like Hi-C and HiChIP, are labor-intensive and require repeated assays to test hypothesized mutation effects. This motivates the need for predictive approaches that reveal the sequence determinants of chromatin loops. RESULTS: In this work, we present a novel and interpretable computational pipeline for predicting CTCF-mediated chromatin loops. We propose Chiron3D, a DNA-only model trained in a cell-type specific manner to predict CTCF HiChIP contact maps. By leveraging pre-trained embeddings from a foundation model, our approach is competitive with baselines that take CTCF ChIP-seq as additional input, while enabling nucleotide-level attribution to the input DNA sequence. Using our framework, we provide likely mechanistic insights into the physical control of loop dynamics. Specifically, we find that the strength of the loop extrusion anchorage site is largely governed by the amount and binding affinity of CTCF sites at the boundaries. Furthermore, we reveal that loop stability is regulated by the amount of intra-loop CTCF binding sites, where fewer intra-loop sites are associated with greater loop stability. Using targeted, single-nucleotide edit simulations with Chiron3D, we show that both loop strength and stability can be precisely controlled. Together, these results provide novel mechanistic insights into the physical control of genome organization and highlight the potential of decoding the DNA sequence logic in silico. AVAILABILITY: The Chiron3D pipeline is made available at https://github.com/BoevaLab/Chiron3D.

Chromatin↗
PubMed2023

Mega-Enhancer Bodies Organize Neuronal Long Genes in the Cerebellum.

Dynamic regulation of gene expression plays a key role in establishing the diverse neuronal cell types in the brain. Recent findings in genome biology suggest that three-dimensional (3D) genome organization has important, but mechanistically poorly understood functions in gene transcription. Beyond local genomic interactions between promoters and enhancers, we find that cerebellar granule neurons undergoing differentiation in vivo exhibit striking increases in long-distance genomic interactions between transcriptionally active genomic loci, which are separated by tens of megabases within a chromosome or located on different chromosomes. Among these interactions, we identify a nuclear subcompartment enriched for near-megabase long enhancers and their associated neuronal long genes encoding synaptic or signaling proteins. Neuronal long genes are differentially recruited to this enhancer-dense subcompartment to help shape the transcriptional identities of granule neuron subtypes in the cerebellum. SPRITE analyses of higher-order genomic interactions, together with IGM-based 3D genome modeling and imaging approaches, reveal that the enhancer-dense subcompartment forms prominent nuclear structures, which we term mega-enhancer bodies. These novel nuclear bodies reside in the nuclear periphery, away from other transcriptionally active structures, including nuclear speckles located in the nuclear interior. Together, our findings define additional layers of higher-order 3D genome organization closely linked to neuronal maturation and identity in the brain.

Journal Article↗
PubMed2026

3D chromatin remodeling during domestication defines novel targets for crop improvement.

Three-dimensional (3D) genome folding shapes gene regulation, yet the genetic underpinnings linking 3D genome evolution to phenotypic innovation during domestication remain elusive. Using population-scale Hi-C profiling of 34 semi-wild and 267 cultivated allotetraploid cottons, we generated a pan-3D genome atlas capturing extensive diversity in topologically associating domains (TADs) and chromatin loops. Chromatin interactome-wide association studies identified 105 TAD reconfigurations and 58 loop rewirings that were established as the 3D chromatin basis of fiber quality, boosting heritability estimates for fiber strength by 16% and fiber length by 20%. We reveal that domestication selection within sequence-defined sweeps fixed 57% of 3D conformation signatures, thereby decoupling sequence-level from chromatin-level selection and shifting the subgenome expression balance of 39 homoeologs in cultivated cotton. Sequence-based modeling and mutational analyses identified the C2H2 zinc-finger protein YY1 as a conserved mediator of 3D genome organization. This study provides a resource for redefining precision-breeding paradigms by harnessing cryptic 3D chromatin targets.

3D genome↗
PubMed2026

A comparative evaluation of multiple enlarged perivascular space segmentation tools.

BACKGROUND: Enlarged perivascular spaces (ePVS) are a marker of cerebral small vessel disease, potentially reflecting reduced waste clearance. Because manual quantification is unfeasible in large datasets, we developed and evaluated an automated tool. METHODS: Detection Of Regions of Enlarged perivascular Spaces (DORES), a 3D nnU-Net-based deep learning algorithm was developed for ePVS segmentation using T1-weighted and fluid-attenuated inversion recovery magnetic resonance imaging (MRI). DORES was developed in two stages: an initial model trained on 35 manually segmented scans and a final model on 1460 pseudo-labeled sessions from the Vanderbilt Memory and Aging Project (VMAP). A subset of VMAP participants with 3 T brain MRI underwent whole-brain manual ePVS tracing (n = 35, 73 ± 9 years, 51% male) and visual rating (n = 388, 71 ± 8 years, 54% male) by a neuroradiologist. DORES was evaluated and compared against three other segmentation tools using Dice and F1 scores, absolute volume and element differences, correlation, and agreement. External validation used an Alzheimer's Disease Neuroimaging Initiative 3 subset with manual tracings (ADNI3, n = 18, 73 ± 9 years, 67% female). RESULTS: DORES achieved Dice scores of 0.61 ± 0.16 (white matter) and 0.72 ± 0.08 (basal ganglia) in VMAP, with strong correlations and agreement for ePVS count and volume. Performances modestly declined in ADNI3 across algorithms. Scanner-stratified analyses showed stronger correlations for Philips versus Siemens images in the basal ganglia, indicating scanner-dependent differences in measurement consistency. CONCLUSIONS: DORES provides a multimodal nnU-Net-based pipeline for ePVS segmentation in older adults. The model demonstrates robust within-cohort performance and reasonable external validity, though scanner-related effects limit application across sites.

Humans↗
PubMed2026

Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. METHODS: The EpiSwitch® 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein-protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. RESULTS: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. CONCLUSIONS: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.

Humans↗
PubMed2026

Uchimata: a toolkit for visualization of 3D genome structures on the web and in computational notebooks.

SUMMARY: Uchimata is a toolkit for visualization of 3D structures of genomes. It consists of two packages: a Javascript library facilitating the rendering of 3D models of genomes, and a Python widget for visualization in Jupyter Notebooks. Main features include an expressive way to specify visual encodings, and filtering of 3D genome structures based on genomic semantics and spatial aspects. Uchimata is designed to be highly integratable with biological tooling available in Python. AVAILABILITY AND IMPLEMENTATION: Uchimata is released under the MIT License. The Javascript library is available on NPM, while the widget is available as a Python package hosted on PyPI. The source code for both is available publicly on Github (https://github.com/hms-dbmi/uchimata and https://github.com/hms-dbmi/uchimata-py) and Zenodo (https://doi.org/10.5281/zenodo.17831959 and https://doi.org/10.5281/zenodo.17832045). The documentation with examples is hosted at https://hms-dbmi.github.io/uchimata/.

Software↗
PubMed2026

Three-dimensional porous nano-hydroxyapatite@gelatin composite as efficient adsorbent for uranyl ion removal from low-level radioactive wastewater.

The contamination of water resources by uranyl (UO22+) ions poses significant environmental and health risks, requiring the development of efficient and sustainable remediation strategies. Adsorption-based techniques have emerged as promising approaches in the field of UO22+ removal, but the design of cost-effective, high-capacity, and environmentally friendly adsorbents remains challenging. In this study, a three-dimensional porous nano-hydroxyapatite@gelatin (nHAP@Ge) composite was synthesized through glutaraldehyde cross-linking, combining the structural stability of Ge with the high uranium affinity of nHAP. The optimized nHAP@Ge, with a nHAP:Ge mass ratio of 1:0.5, exhibited exceptional UO22+ removal efficiency (97 %), along with high adsorption capacity (364.03 mg/g). Systematic characterizations using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS) methods revealed that the porous structure and surface functional groups (-OH, Ca2+, and PO43-) of the material synergistically contributed to binding UO22+ species. Furthermore, the incorporation of nHAP into the Ge framework resulted in enhanced thermal stability while significantly improving the UO22+ adsorption performance. This work presents a scalable, eco-friendly, and recyclable strategy for the effective treatment of uranium-contaminated water, with potential applications in nuclear wastewater treatment and environmental remediation.

Adsorption↗
PubMed2025

MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures.

The genome is organized into distinct chromatin compartments with at least two main classes, a transcriptionally active A and an inactive B compartment, broadly corresponding to euchromatin and heterochromatin. Chromatin regions within the same compartment preferentially interact with each other over regions in the opposite compartment. A/B compartments are traditionally identified from ensemble Hi-C contact frequency matrices using principal component analysis of their covariance matrices. However, defining compartments at the single-cell level from sparse single-cell Hi-C data is challenging, especially since homologous copies are often not resolved. To address this, we present MaxComp, an unsupervised method, for inferring single-cell A/B compartments based on 3D geometric considerations in single-cell chromosome structures-derived either from multiplexed FISH-omics imaging or 3D structure models derived from Hi-C data. By representing each 3D chromosome structure as an undirected graph with edge-weights encoding structural information, MaxComp reformulates compartment prediction as a variant of the Max-cut problem, solved using semidefinite graph programming (SPD) to optimally partition the graph into two structural compartments. Our results show that the population average of MaxComp single-cell compartment annotations closely matches those derived from ensemble Hi-C principal component analysis, demonstrating that compartmentalization can be recovered from geometric principles alone, using only the 3D coordinates and nuclear microenvironment of chromatin regions. Our approach reveals widespread cell-to-cell variability in compartment organization, with substantial heterogeneity across genomic loci. When applied to multiplexed FISH imaging data, MaxComp also uncovers relationships between compartment annotations and transcriptional activity at the single-cell level. In summary, MaxComp offers a new framework for understanding chromatin compartmentalization in single cells, connecting 3D genome architecture, and transcriptional activity with the cell-to-cell variations of chromatin compartments.

Chromatin↗
PubMed2023

EMPIAR: the Electron Microscopy Public Image Archive.

Public archiving in structural biology is well established with the Protein Data Bank (PDB; wwPDB.org) catering for atomic models and the Electron Microscopy Data Bank (EMDB; emdb-empiar.org) for 3D reconstructions from cryo-EM experiments. Even before the recent rapid growth in cryo-EM, there was an expressed community need for a public archive of image data from cryo-EM experiments for validation, software development, testing and training. Concomitantly, the proliferation of 3D imaging techniques for cells, tissues and organisms using volume EM (vEM) and X-ray tomography (XT) led to calls from these communities to publicly archive such data as well. EMPIAR (empiar.org) was developed as a public archive for raw cryo-EM image data and for 3D reconstructions from vEM and XT experiments and now comprises over a thousand entries totalling over 2 petabytes of data. EMPIAR resources include a deposition system, entry pages, facilities to search, visualize and download datasets, and a REST API for programmatic access to entry metadata. The success of EMPIAR also poses significant challenges for the future in dealing with the very fast growth in the volume of data and in enhancing its reusability.

Imaging, Three-Dimensional↗
PubMed2026

Fused Deposition Modeling (FDM) of polyether-ether-ketone (PEEK) dental implants: A systematic review of the effect of printing parameters on mechanical behaviour and surface quality.

PURPOSE: This systematic review evaluated how FDM printing parameters influence mechanical behaviour and surface characteristics of 3D-printed PEEK and identified parameter combinations linked to the most favourable mechanical performance and surface quality. MATERIALS AND METHODS: An electronic search was conducted in: MEDLINE (Ovid), PubMed, Embase, Web of Science, Scopus, and Compendex (last update: January 2025). Studies that evaluated the effect of FDM printing parameters on mechanical and surface properties of PEEK were included. Outcomes comprised compressive, tensile, and flexural strengths, elastic modulus, fracture toughness, surface hardness, roughness, and wettability. RESULTS: Of 4005 reports screened, 54 manuscripts were included. 92.6% (n = 50) of articles showed low risk-of-bias, while 7.4% (n = 4) showed medium risk-of-bias. Tensile strength was the most investigated mechanical parameter (78%), followed by elastic modulus (41%), flexural strength (30%), compressive strength (20%), and fracture toughness (6%). Surface roughness was the most evaluated surface property (30%), followed by hardness (17%) and wettability (6%). Across studies, higher printing temperatures, lower printing speed, thinner layer thickness, and maximum infill ratio in a horizontal printing orientation were associated with higher strengths, less warpage, increased accuracy, and improved surface quality. CONCLUSION: Specific combinations of FDM printing parameters can significantly improve the mechanical and surface properties of PEEK. However, it is difficult to meet all the optimal conditions simultaneously. Thus, balancing between different parameters must be considered in practical production.

Benzophenones↗
PubMed2026

Four-dimensional molecular mapping from a spatial snapshot reveals the dynamics of hair follicle organogenesis.

Understanding organ formation requires capturing molecular information simultaneously in three-dimensional (3D) space and across developmental time. To this end, we developed 3D DNase-Enhanced Expression Profiling (3DEEP), a tissue-clearing approach that removes genomic DNA to extend spatial transcriptomic profiling hundreds of microns into intact tissues. We applied 3DEEP to neonatal mouse skin, capturing hundreds of developing hair follicles across their organogenesis trajectory. Ordering follicles by molecularly inferred developmental age transformed this single spatial snapshot into a four-dimensional (3D + time) molecular map of organogenesis. This map revealed developmental dynamics spanning stem cell compartment stratification, emergence of new cell subtypes within the follicle, and cascading structural transformations leading to hair canal formation. Comparative analysis of Foxn1-deficient nude mice, a hairlessness model, revealed organ-wide changes in developmental dynamics, including delayed molecular progression, reduced coordination, and increased developmental instability, preceding overt structural defects. This work demonstrates how deep-tissue spatial transcriptomics can uncover hidden dynamics of organ formation.

Animals↗
PubMed2022

Morphological instability and roughening of growing 3D bacterial colonies.

How do growing bacterial colonies get their shapes? While colony morphogenesis is well studied in two dimensions, many bacteria grow as large colonies in three-dimensional (3D) environments, such as gels and tissues in the body or subsurface soils and sediments. Here, we describe the morphodynamics of large colonies of bacteria growing in three dimensions. Using experiments in transparent 3D granular hydrogel matrices, we show that dense colonies of four different species of bacteria generically become morphologically unstable and roughen as they consume nutrients and grow beyond a critical size-eventually adopting a characteristic branched, broccoli-like morphology independent of variations in the cell type and environmental conditions. This behavior reflects a key difference between two-dimensional (2D) and 3D colonies; while a 2D colony may access the nutrients needed for growth from the third dimension, a 3D colony inevitably becomes nutrient limited in its interior, driving a transition to unstable growth at its surface. We elucidate the onset of the instability using linear stability analysis and numerical simulations of a continuum model that treats the colony as an "active fluid" whose dynamics are driven by nutrient-dependent cellular growth. We find that when all dimensions of the colony substantially exceed the nutrient penetration length, nutrient-limited growth drives a 3D morphological instability that recapitulates essential features of the experimental observations. Our work thus provides a framework to predict and control the organization of growing colonies-as well as other forms of growing active matter, such as tumors and engineered living materials-in 3D environments.

Models, Biological↗
PubMed2025

Knowledge-enhanced protein subcellular localization prediction from 3D fluorescence microscope images.

MOTIVATION: Pinpointing the subcellular location of proteins is essential for studying protein function and related diseases. Advances in spatial proteomics have shown that automatic recognition of protein subcellular localization from images could highly facilitate protein translocation analysis and biomarker discovery, but existing machine-learning works have been mostly limited to processing 2D images. By contrast, 3D images have higher spatial resolution and allow researchers to observe cellular structures in their natural context, but currently, there are only a few studies of 3D image processing for protein distribution analysis due to the lack of data and complexity of modeling. RESULTS: We developed a knowledge-enhanced protein subcellular localization model, KE3DLoc, which could recognize distribution patterns in 3D fluorescence microscope images using deep learning methods. The model designs an image feature extraction module that incorporates information from 3D and 2D projected cells and implements asymmetric loss and confidence weights to address data imbalance and weak cell annotation issues. Besides, considering that the biological knowledge in the Gene Ontology (GO) database can provide valuable support for protein location understanding, the KE3DLoc model incorporates a novel knowledge enhancement module that optimizes the protein representation by related knowledge graphs derived from the GO. Since the image module and the knowledge module calculate features from different levels, KE3DLoc designs protein ID aggregation to enhance the consistency of protein features across different cells. Experimental results on three public datasets have demonstrated that the KE3DLoc significantly outperforms existing methods and provides valuable insights for spatial proteomics research. AVAILABILITY AND IMPLEMENTATION: All datasets and codes used in this study are available at GitHub: https://github.com/PRBioimages/KE3DLoc.

Microscopy, Fluorescence↗