Cellular & Molecular Imaging

"This dataset contains over 20,000 images of dipteran wings, mostly from mosquitoes. Each image is accompanied by extensive metadata available in the 00_metadata.* files. The dataset is intended to support research in wing geometric morphometry and the development of machine-learning approaches for vector surveillance and related studies. It is a retrospective collection that harmonises material gathered from research projects conducted between 2008 and 2026. Detailed metadata for each sample and image are provided in the 00_metadata.* files, and the submission also includes an overview of the scope and range of the collected metadata. Because the dataset was assembled retrospectively, images may vary in lighting, background, and capture conditions across projects. Although extensive effort was made to ensure comprehensive metadata, some entries remain incomplete. Missing metadata entries are marked as MISSING VALUE, while fields intentionally left blank are indicated with ""-"". For a full description of the dataset, see the associated publication at https://www.nature.com/articles/s41597-025-05043-3#Sec5. The dataset will be updated as additional mosquito and other dipteran wing images become available, and contributions from the scientific community are welcome. Because the dataset is too large for practical browser download, users are advised to access it via FTP. On Mac, wget can be installed with Homebrew and used to download the zipped dataset from ftp://ftp.ebi.ac.uk/biostudies/fire/S-BIAD/478/S-BIAD1478/Files/MosquitoWingImages_v2/Files/zipped/ into the WingImages folder in Documents. On Windows, users can install GnuWin32 wget, run the corresponding FTP download command in Command Prompt, and download the same zipped dataset into the WingImages folder in Documents."

To address drug-resistant tuberculosis, the leading single-pathogen infectious killer, drugs with novel modes of action (MoAs) are urgently needed. Phenotypic screening of chemical libraries can identify antimicrobial compounds, but standard screens cannot reveal the MoA of hits, limiting targeted selection of compounds with novel MoAs. Here, we develop a deep learning (DL) model to screen drug-treated Corynebacterium glutamicum (Cglu), a surrogate for Mycobacterium tuberculosis. We train our DL model to distinguish between MoAs directly from high-throughput images. Our model robustly classifies MoAs of established antibiotics and recognises the MoA of previously unseen antibiotics. Inhibitors with a previously unseen MoA cluster together and apart from reference drugs, enabling the detection of novel MoAs. Moreover, our model links images of chemical (drugs) and genetic (mutants) perturbations targeting similar pathways, supporting mutant-based target prediction of compounds with novel MoAs directly from images. Finally, our DL model recovers known biological relationships from images alone using the Cglu cell cycle as a case study.

Citation: [Multi-modal imaging analysis of Parkinsonian mice reveals region-specific alterations in the substantia nigra] The spatiotemporal relationships among α-synuclein inclusion formation, dopaminergic degeneration, and white matter microstructural changes remain poorly defined. To address this, we unilaterally injected preformed fibrils (PFFs) or monomeric α-synuclein into the substantia nigra pars compacta (SNc) of wild-type mice. At 12 and 20 weeks post-injection (wpi), we performed ex vivo magnetic resonance imaging (MRI) at 9.4T to assess microstructural, volumetric and paramagnetic changes, along with light-sheet microscopy (LSM) to map α- synuclein aggregates, and dopaminergic neuron at single-cell resolution across the whole brain. We developed a Python-based, automated registration pipeline that achieves <40 µm alignment error between ex vivo MRI and LSM and computes clearing-induced distortion, enabling scalable deformation analysis and multimodal multiscale data integration. Unilateral SNc injection of α-syn PFFs induced dense phospho-α-syn pathology in the ipsilateral SNc at 12 wpi, which decreased by 20 wpi in parallel with the loss of tyrosine hydroxylasepositive dopaminergic neuron. Pathogenic α-synuclein spreads along with dopaminergic denervation in the nigrostriatal pathway to the ipsilateral striatum and contralateral SNc. Our ex vivo MRI-LSM platform provides a scalable, open-source framework for understanding circuit-level and whole-brain propagation of pathology in an α-synucleinopathy model and beyond.

The Darwin Tree of Life project has the goal to sequence the genomes of 70,000 species of eukaryotic organisms in Britain and Ireland. This is a collection of photographs of the samples included in the study, provided by the National History Museum (NHM).

Phospholipidosis (PLD), a cellular adverse effect that is, among others, caused by numerous cationic amphiphilic drugs. Interest is raised within pharma discovery to predict this phenomenon, as it can impact the outcome of phenotypic cellular screens and significantly delay drug development processes. The development of accurate and validated machine learning models for predicting drug-induced PLD across different cell lines and research centers could provide a valuable early application tool for the pharmaceutical industry, potentially accelerating drug discovery and reducing the risk of late-stage failures. We report here the assembly, curation, testing and modeling of one of the largest datasets of repurposed drugs (5000+) tested for PLD induction on different cell lines. A machine-learning classification method was developed and validated to predict whether molecules are prone to induce PLD effects when applied in cell-based screens. This constitutes the accompanying publication of the acquired image data.

The Darwin Tree of Life project has the goal to sequence the genomes of 70,000 species of eukaryotic organisms in Britain and Ireland. This is a collection of photographs of the samples included in the study, provided by the National History Museum (NHM).

Microscopy data for manuscript: "Palmitoylated Importin Alpha Regulates Mitotic Spindle Orientation Through Interaction NuMA". A study elucidating a novel role of the nuclear transport protein importin alpha in astral microtubule anchoring.

How epigenetic modulators of gene regulation affect the development of animals has been difficult to ascertain. Despite the widespread presence of histone 3 lysine 4 monomethylation (H3K4me1) on enhancers, hypomethylation appears to have minor effects on phenotype and viability. In this study, we performed quantitative and unbiased measurements of key phenotypes in Drosophila melanogaster. Genetically induced hypomethylation reduced transcription factor enrichment in nuclear microenvironments, leading to disrupted gene expression and reduced phenotypic robustness in response to temperature changes. Our developmental phenomics approach further showed widespread, but environmentally and genetically dependent, changes in morphology, metabolism, behavior, and offspring production. Therefore, quantitative phenomics measurements under conditions resembling the natural environments of a species could unravel how pleiotropic modulators of gene expression contribute to developmental robustness. This submission contains the imaging files (confocal imaging and cuticle preparations) of the study.

The antimicrobial resistance crisis urgently calls for antibiotics with novel modes of action (MoAs). While growth inhibition assays can identify antibiotic molecules, they miss promising compounds below inhibitory concentrations and cannot reveal their MoA. Imaging-based profiling of drug-treated bacteria can inform on MoA, but current approaches generally require fluorescent labelling and/or inhibitory concentrations and it remains unclear whether compounds with novel MoAs can be robustly detected. Here, we demonstrate a deep learning approach to recognise antibiotic MoA from unlabeled images. We train a convolutional neural network to predict treatment conditions from brightfield images of Escherichia coli exposed to antibiotics covering eight MoAs. Our approach can detect drug exposure at subinhibitory concentrations and allows near-perfect MoA recognition, even when trained on only eight images per treatment condition. Previously unseen compounds are assigned to the correct MoA with good accuracy if the MoA is represented in the training data, and our method can robustly detect MoA novelty in five out of six considered MoAs, enabling microscopy-based identification of new antibiotic classes. We also achieve near-perfect MoA recognition in Klebsiella pneumoniae, suggesting applicability to other species. Our approach complements growth inhibition assays and is poised to improve the search for innovative antibiotic compounds.

Background Microsporidia sp. MB, a microsporidian symbiont found naturally in Anopheles mosquitoes, has potential as a novel malaria control tool since it can inhibit Plasmodium development and transmission. The most feasible MB-based Plasmodium control strategy would involve dissemination through live mosquito releases, or release of spores infective to mosquito larvae. To implement either strategy, establishment of stable mosquito colonies carrying MB at a high frequency is likely to be essential. The progeny of field caught An. gambiae s.l from Burkina Faso were isolated for individual egg laying and tested for MB. The progeny of the MB positive females were pooled and this process was repeated for multiple generations. The relative density of MB in different life stages and tissues of the An. coluzzii host was examined using a novel duplex qPCR assay. We also examined the impact of MB on fecundity through individualization for egg laying and counting of eggs. Finally, we examined laid eggs for presence of MB spores. Results Three An. coluzzii colonies and one An. gambiae s.l hybrid colony were established with high prevalence and density of MB and were maintained for more than two years with minimal intervention. MB prevalence and density was highest in eggs and adult females and lowest in L4 larvae; in adults density was highest in the gonads. Additionally, MB density increased in ovary samples following blood feeding which was likely due to the activation of sporogony. The production of spores is the reason why MB-carrying females lay more white non-hatching eggs and show a small reduction in fecundity. Conclusions Establishment of several stable MB carrying An. gambiae s.l colonies and understanding the impact of spores on fecundity are significant steps forward in developing MB as a malaria control tool.