The presence of multi-drug resistant biofilms in chronic, persistent infections is a major barrier to successful clinical outcomes of therapy. The production of an extracellular matrix is a characteristic of the biofilm phenotype, intrinsically linked to antimicrobial tolerance. The heterogeneity of the extracellular matrix makes it highly dynamic, with substantial differences in composition between biofilms, even in the same species. This variability poses a major challenge in targeting drug delivery systems to biofilms, as there are few elements both suitably conserved and widely expressed across multiple species. However, the presence of extracellular DNA within the extracellular matrix is ubiquitous across species, which alongside bacterial cell components, gives the biofilm its net negative charge. This research aims to develop a means of targeting biofilms to enhance drug delivery by developing a cationic gas-filled microbubble that non-selectively targets the negatively charged biofilm. Cationic and uncharged microbubbles loaded with different gases were formulated and tested to determine their stability, ability to bind to negatively charged artificial substrates, binding strength, and, subsequently, their ability to adhere to biofilms. It was shown that compared to their uncharged counterparts, cationic microbubbles facilitated a significant increase in the number of microbubbles that could both bind and sustain their interaction with biofilms. This work is the first to demonstrate the utility of charged microbubbles for the non-selective targeting of bacterial biofilms, which could be used to significantly enhance stimuli-mediated drug delivery to the bacterial biofilm.

Electron cryo-tomography is an imaging technique for probing 3D structures with at the nanometer scale. This technique has been used extensively in the biomedical field to study the complex structures of proteins and other macromolecules. With the advancement in technology, microscopes are currently capable of producing images amounting to terabytes of data per day, posing great challenges for scientists as the speed of processing of the images cannot keep up with the ever-higher throughput of the microscopes. Therefore, automation is an essential and natural pathway on which image processing—from individual micrographs to full tomograms—is developing. In this paper, we present Ot2Rec, an open-source pipelining tool which aims to enable scientists to build their own processing workflows in a flexible and automatic manner. The basic building blocks of Ot2Rec are plugins which follow a unified application programming interface structure, making it simple for scientists to contribute to Ot2Rec by adding features which are not already available. In this paper, we also present three case studies of image processing using Ot2Rec, through which we demonstrate the speedup of using a semi-automatic workflow over a manual one, the possibility of writing and using custom (prototype) plugins, and the flexibility of Ot2Rec which enables the mix-and-match of plugins. We also demonstrate, in the Supplementary Material, a built-in reporting feature in Ot2Rec which aggregates the metadata from all process being run, and output them in the Jupyter Notebook and/or HTML formats for quick review of image processing quality. Ot2Rec can be found at https://github.com/rosalindfranklininstitute/ot2rec.

Segmentation of biological images identifies regions of an image which correspond to specific features of interest, which can be analysed quantitatively to answer biological questions. This task has long been a barrier to conducting large-scale biological imaging studies as it is time- and labour-intensive. Modern artificial intelligence segmentation tools can automate this process, but require high quality segmentation data for training, which is challenging to acquire. Biological segmentation data has been produced for many years, but this data is not often reused to develop new tools as it is hard to find, access, and use. Recent disparate efforts (Iudin, et al., 2023; Xu, et al., 2021; Vogelstein, et al., 2018; Ermel, et al., 2024) have been made to facilitate deposition and re-use of these valuable datasets, but more work is needed to increase re-usability. In this work, we review the current state of publicly available annotation and segmentation datasets and make specific recommendations to increase re-usability following FAIR (findable, accessible, interoperable, re-usable) principles (Wilkinson, et al., 2016) for the future.

Electron cryo-tomography (cryo-ET) is an imaging technique for probing 3D structures with at the nanometre scale. This technique has been used extensively in the biomedical field to study the complex structures of proteins and other macromolecules. With the advancement in technology, microscopes are currently capable of producing images amounting to terabytes of data per day, posing great challenges for scientists as the speed of processing of the images cannot keep up with the ever-higher throughput of the microscopes. Therefore, automation is an essential and natural pathway on which image processing – from individual micrographs to full tomograms – is developing. In this paper, we present Ot2Rec, an open-source pipelining tool which aims to enable scientists to build their own processing workflows in a flexible and automatic manner. The basic building blocks of Ot2Rec are plugins which follow a unified API structure, making it simple for scientists to contribute to Ot2Rec by adding features which are not already available. In this paper, we also present three case studies of image processing using Ot2Rec, through which we demonstrate the speedup of using a semi-automatic workflow over a manual one, the possibility of writing and using custom (prototype) plugins, and the flexibility of Ot2Rec which enables the mix-and-match of plugins. We also demonstrate, in the supplementary information, a built-in reporting feature in Ot2Rec which aggregates the metadata from all process being run, and output them in the Jupyter Notebook and/or HTML formats for quick review of image processing quality. Ot2Rec can be found at https://github.com/rosalindfranklininstitute/ot2rec. Impact Statement The field of cryo electron tomography has grown substantially in recent years, bringing about new advances in hardware and software which enable visualisation of cell and tissue architecture and proteins found in their native context. These same advances have, in some ways, stratified the field into those with access and those without. On the software side, this has emphasised the need for open-source options that do not require high levels of computational literacy to access. Additionally, it has highlighted the need for ways to both mix-and-match software for easy prototyping and comparisons between parameters and methods. Ot2Rec addresses these needs through a simple, unified plugin structure allowing the addition of existing software or the development of new and does so in a way which democratises access.

Despite significant investment in High-Performance Computing (HPC) clusters by funding councils, there are still many researchers whose workflows could not benefit from the computation speed that is provided by these clusters. Reducing barriers to entry for these researchers would accelerate their scientific throughput, since they will be able to respond to results in a timely fashion, improving either their protocols or correcting any problems that might have arisen. This improves the quality of science, and therefore the return on investment, in computationally-intensive areas such as Cryogenic Electron Microscopy (cryo-EM). This paper outlines a technique, FlowCron, for users to analyse their data on a HPC facility with reduced training, increasing accessibility. FlowCron transfers the responsibilities of installation and upkeep of data processing pipelines from users to HPC project PIs and/or HPC project managers, simplifies the set up of HPC pipelines, and makes pipelines as reliable as possible once set up. The work described here has software dependencies that are common to the majority of HPC clusters. We achieve this by linking Globus and cron to produce an open-source system that requires little administrative support but provides a very easy way of running an analysis on a HPC system. The user starts the analysis through the Globus website and, when started, the data will be encrypted, uploaded to the HPC, analysed, and returned to the originating machine, along with a record of the analysis. For Globus to transfer any data to and from the HPC, appropriate user authentication is required, thus ensuring that only authorised users can send data in the HPC.

Cryo-electron tomography of 100-200 nm lamellae prepared by focused ion beam milling enables structural analysis of macromolecules within cells, tissues, and small organisms, but lamella preparation remains time-consuming and limits throughput. Although automated workflows enable unsupervised fabrication, they commonly produce variable results because fixed, pre-calibrated parameters are applied without accounting for lamella-to-lamella variability. To overcome these limitations, we developed an Adaptive Milling approach that adapts milling parameters based on imaging feedback. We implemented this approach for the critical polishing stage as Adaptive Polishing, which uses machine-learning-based segmentation of scanning electron microscopy images to identify lamella features predictive of imminent collapse. These are used to periodically assess whether to continue or stop thinning during polishing. We performed a systematic comparison of the quality of lamellae prepared with Adaptive Polishing and conventional automation, and demonstrate that Adaptive Polishing produces thinner lamellae with greater consistency in thickness, and performs well for in situ cryo-electron tomography. Adaptive Polishing was tested on three different microscopes with minimal model retraining required, demonstrating transferability of the approach. We implemented Adaptive Polishing as an open-source plugin for fibsemOS together with machine-learning model training software and pre-trained models.