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Small molecule modulators of epigenetic processes are currently sought as basic probes for biochemical mechanisms, and as starting points for development of therapeutic agents. N(ε)-Methylation of lysine residues on histone tails is one of a number of post-translational modifications that together enable transcriptional regulation. Histone lysine demethylases antagonize the action of histone methyltransferases in a site- and methylation state-specific manner. N(ε)-Methyllysine demethylases that use 2-oxoglutarate as co-factor are associated with diverse human diseases, including cancer, inflammation and X-linked mental retardation; they are proposed as targets for the therapeutic modulation of transcription. There are few reports on the identification of templates that are amenable to development as potent inhibitors in vivo and large diverse collections have yet to be exploited for the discovery of demethylase inhibitors. High-throughput screening of a ∼236,000-member collection of diverse molecules arrayed as dilution series was used to identify inhibitors of the JMJD2 (KDM4) family of 2-oxoglutarate-dependent histone demethylases. Initial screening hits were prioritized by a combination of cheminformatics, counterscreening using a coupled assay enzyme, and orthogonal confirmatory detection of inhibition by mass spectrometric assays. Follow-up studies were carried out on one of the series identified, 8-hydroxyquinolines, which were shown by crystallographic analyses to inhibit by binding to the active site Fe(II) and to modulate demethylation at the H3K9 locus in a cell-based assay. These studies demonstrate that diverse compound screening can yield novel inhibitors of 2OG dependent histone demethylases and provide starting points for the development of potent and selective agents to interrogate epigenetic regulation.

Many bioimaging research projects require objects of interest to be identified, located, and then traced to allow quantitative measurement. Depending on the complexity of the system and imaging, instance segmentation is often done manually, and automated approaches still require weeks to months of an individual’s time to acquire the necessary training data for AI models. As such, there is a strong need to develop approaches for instance segmentation that minimize the use of expert annotation while maintaining quality on challenging image analysis problems. Herein, we present our work on a citizen science project we ran called Science Scribbler: Virus Factory on the Zooniverse platform, in which citizen scientists annotated a cryo-electron tomography volume by locating and categorising viruses using point-based annotations instead of manually drawing outlines. One crowdsourcing workflow produced a database of virus locations, and the other workflow produced a set of classifications of those locations. Together, this allowed mask annotation to be generated for training a deep learning–based segmentation model. From this model, segmentations were produced that allowed for measurements such as counts of the viruses by virus class. The application of citizen science–driven crowdsourcing to the generation of instance segmentations of volumetric bioimages is a step towards developing annotation-efficient segmentation workflows for bioimaging data. This approach aligns with the growing interest in citizen science initiatives that combine the collective intelligence of volunteers with AI to tackle complex problems while involving the public with research that is being undertaken in these important areas of science.

The JmjC histone demethylases (KDMs) are linked to tumour cell proliferation and are current cancer targets; however, very few highly selective inhibitors for these are available. Here we report cyclic peptide inhibitors of the KDM4A-C with selectivity over other KDMs/2OG oxygenases, including closely related KDM4D/E isoforms. Crystal structures and biochemical analyses of one of the inhibitors (CP2) with KDM4A reveals that CP2 binds differently to, but competes with, histone substrates in the active site. Substitution of the active site binding arginine of CP2 to N -ɛ-trimethyl-lysine or methylated arginine results in cyclic peptide substrates, indicating that KDM4s may act on non-histone substrates. Targeted modifications to CP2 based on crystallographic and mass spectrometry analyses results in variants with greater proteolytic robustness. Peptide dosing in cells manifests KDM4A target stabilization. Although further development is required to optimize cellular activity, the results reveal the feasibility of highly selective non-metal chelating, substrate-competitive inhibitors of the JmjC KDMs. JmjC histone demethylases (KDMs) are cancer targets due to their links to cell proliferation, but selective inhibition remains a challenge. Here the authors identify potent inhibitors of KDM4A-C—via in vitro selection from a vast library of cyclic peptides—that show selectivity over other KDMs.

Public participation in research, also known as citizen science, is being increasingly adopted for the analysis of biological volumetric data. Researchers working in this domain are applying online citizen science as a scalable distributed data analysis approach, with recent research demonstrating that non-experts can productively contribute to tasks such as the segmentation of organelles in volume electron microscopy data. This, alongside the growing challenge to rapidly process the large amounts of biological volumetric data now routinely produced, means there is increasing interest within the research community to apply online citizen science for the analysis of data in this context. Here, we synthesise core methodological principles and practices for applying citizen science for analysis of biological volumetric data. We collate and share the knowledge and experience of multiple research teams who have applied online citizen science for the analysis of volumetric biological data using the Zooniverse platform (www.zooniverse.org). We hope this provides inspiration and practical guidance regarding how contributor effort via online citizen science may be usefully applied in this domain.

As sample preparation and imaging techniques have expanded and improved to include a variety of options for larger sized and numbers of samples, the bottleneck in volumetric imaging is now data analysis. Annotation and segmentation are both common, yet difficult, data analysis tasks which are required to bring meaning to the volumetric data. The SuRVoS application has been updated and redesigned to provide access to both manual and machine learning-based segmentation and annotation techniques, including support for crowd sourced data. Combining adjacent, similar voxels (supervoxels) provides a mechanism for speeding up segmentation both in the painting of annotation and by training a segmentation model on a small amount of annotation. The support for layers allows multiple datasets to be viewed and annotated together which, for example, enables the use of correlative data (e.g. crowd-sourced annotations or secondary imaging techniques) to guide segmentation. The ability to work with larger data on high-performance servers with GPUs has been added through a client-server architecture and the Pytorch-based image processing and segmentation server is flexible and extensible, and allows the implementation of deep learning-based segmentation modules. The client side has been built around Napari allowing integration of SuRVoS into an ecosystem for open-source image analysis while the server side has been built with cloud computing and extensibility through plugins in mind. Together these improvements to SuRVoS provide a platform for accelerating the annotation and segmentation of volumetric and correlative imaging data across modalities and scales.

Small molecule modulators of epigenetic processes are currently sought as basic probes for biochemical mechanisms, and as starting points for development of therapeutic agents. N.sup.[epsilon] -Methylation of lysine residues on histone tails is one of a number of post-translational modifications that together enable transcriptional regulation. Histone lysine demethylases antagonize the action of histone methyltransferases in a site- and methylation state-specific manner. N.sup.[epsilon] -Methyllysine demethylases that use 2-oxoglutarate as co-factor are associated with diverse human diseases, including cancer, inflammation and X-linked mental retardation; they are proposed as targets for the therapeutic modulation of transcription. There are few reports on the identification of templates that are amenable to development as potent inhibitors in vivo and large diverse collections have yet to be exploited for the discovery of demethylase inhibitors. These studies demonstrate that diverse compound screening can yield novel inhibitors of 2OG dependent histone demethylases and provide starting points for the development of potent and selective agents to interrogate epigenetic regulation.

Small molecule modulators of epigenetic processes are currently sought as basic probes for biochemical mechanisms, and as starting points for development of therapeutic agents. N.sup.[epsilon] -Methylation of lysine residues on histone tails is one of a number of post-translational modifications that together enable transcriptional regulation. Histone lysine demethylases antagonize the action of histone methyltransferases in a site- and methylation state-specific manner. N.sup.[epsilon] -Methyllysine demethylases that use 2-oxoglutarate as co-factor are associated with diverse human diseases, including cancer, inflammation and X-linked mental retardation; they are proposed as targets for the therapeutic modulation of transcription. There are few reports on the identification of templates that are amenable to development as potent inhibitors in vivo and large diverse collections have yet to be exploited for the discovery of demethylase inhibitors. These studies demonstrate that diverse compound screening can yield novel inhibitors of 2OG dependent histone demethylases and provide starting points for the development of potent and selective agents to interrogate epigenetic regulation.

A group of three deep learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate) were created for analysis of micrographs of protein crystallisation experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case, and masks of crystallisation droplets in addition to crystals in the second case, allowing positions and sizes of these entities to be recorded. Creation of these tools used transfer learning, where weights from a pre-trained deep learning network were used as a starting point and re-purposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS where they absolve the need for any user input both for monitoring crystallisation experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug discovery screening platform, also at DLS, to allow automatic targeting of acoustic compound dispensing into crystallisation droplets.

Methane (CH4) hydrate dissociation and CH4 release are potential geohazards currently investigated using X-ray computed tomography (XCT) imaging in laboratory experiments. Image segmentation constitutes an important data processing step for this type of research, but it is often time consuming, computing resource-intensive and operator-dependent. Furthermore, segmentation procedures are frequently tailored for each XCT data set due to differences in image characteristics, such as greyscale contrast variations. To address these issues, an investigation has been carried out using U-Nets, a novel class of Convolutional Neural Network, to segment synchrotron radiation XCT (SRXCT) images of CH4-bearing sand during hydrate formation. Three U-Net deployment methodologies previously untried for this task were assessed: (1) 3D hierarchical, (2) 2D multilabel and (3) RootPainter, a 2D application that implements interactive corrections. Results show high segmentation accuracy, with RootPainter slightly outperforming the alternative approaches. Greyscale contrast between material phases was found to affect segmentation performance, with the lowest metrics corresponding to data exhibiting the lowest contrast. Segmentation accuracy affected derived parameters such as CH4-saturation and porosity, but errors were small compared with gravimetric methods. It was also found that U-Net models trained on low greyscale contrast images could be used to segment higher-contrast data sets and produce accurate 3D visualizations of CH4 distribution, demonstrating model portability. Such portability is anticipated to be advantageous when the segmentation of large XCT data sets needs to be delivered over short timespans.