Annotation and automated segmentation of single-molecule localisation microscopy data.

Single Molecule Localisation Microscopy (SMLM) is becoming a widely used technique in cell biology. After processing the images, the molecular localisations are typically stored in a table as xy (or xyz) coordinates, with additional information, such as number of photons, etc. This set of coordinates can be used to generate an image to visualise the molecular distribution, for example, a 2D or 3D histogram of localisations. Many different methods have been devised to analyse SMLM data, among which cluster analysis of the localisations is popular. However, it can be useful to first segment the data, to extract the localisations in a specific region of a cell or in individual cells, prior to downstream analysis. Here we describe a pipeline for annotating localisations in an SMLM dataset in which we compared membrane segmentation approaches, including Otsu thresholding and machine learning models, and subsequent cell segmentation. We used an SMLM dataset derived from dSTORM images of sectioned cell pellets, stained for the membrane proteins EGFR (epidermal growth factor receptor) and EREG (epiregulin) as a test dataset. We found that a Cellpose model retrained on our data performed the best in the membrane segmentation task, allowing us to perform downstream cluster analysis of membrane versus cell interior localisations. We anticipate this will be generally useful for SMLM analysis.

Nanoscale Pattern Extraction from Relative Positions of Sparse 3D Localizations.

Inferring the organization of fluorescently labeled nanosized structures from single molecule localization microscopy (SMLM) data, typically obscured by stochastic noise and background, remains challenging. To overcome this, we developed a method to extract high-resolution ordered features from SMLM data that requires only a low fraction of targets to be localized with high precision. First, experimentally measured localizations are analyzed to produce relative position distributions (RPDs). Next, model RPDs are constructed using hypotheses of how the molecule is organized. Finally, a statistical comparison is used to select the most likely model. This approach allows pattern recognition at sub-1% detection efficiencies for target molecules, in large and heterogeneous samples and in 2D and 3D data sets. As a proof-of-concept, we infer ultrastructure of Nup107 within the nuclear pore, DNA origami structures, and α-actinin-2 within the cardiomyocyte Z-disc and assess the quality of images of centrioles to improve the averaged single-particle reconstruction.

Securing the future of research computing in the biosciences.

Improvements in technology often drive scientific discovery. Therefore, research requires sustained investment in the latest equipment and training for the researchers who are going to use it. Prioritising and administering infrastructure investment is challenging because future needs are difficult to predict. In the past, highly computationally demanding research was associated primarily with particle physics and astronomy experiments. However, as biology becomes more quantitative and bioscientists generate more and more data, their computational requirements may ultimately exceed those of physical scientists. Computation has always been central to bioinformatics, but now imaging experiments have rapidly growing data processing and storage requirements. There is also an urgent need for new modelling and simulation tools to provide insight and understanding of these biophysical experiments. Bioscience communities must work together to provide the software and skills training needed in their areas. Research-active institutions need to recognise that computation is now vital in many more areas of discovery and create an environment where it can be embraced. The public must also become aware of both the power and limitations of computing, particularly with respect to their health and personal data.

Literature Review of Automated Grading Systems Utilizing MRI for Neuroforaminal Stenosis.

BACKGROUND: Cervical neural foraminal stenosis is a common and debilitating condition affecting people between the ages 40-60. Although it is established that MRI is the best method of scanning the neural foramen, the question remains whether there is a role for three-dimensional MRIs and whether it is possible to develop a computer-aided automated grading system to establish the degree of clinically relevant cervical foraminal stenosis. OBJECTIVE: The study's objective is to conduct a literature review of existing or recently developed automated grading systems for the cervical neural foramen, including volumetric MRI evaluations of the foramen. METHODS: A systematic search of Cochrane Library, Cochrane Clinical Trials, Ovid MEDLINE, EMBASE, CINAHL, ACM Digital Library and Institute of Electrical and Electronics Engineers (IEEE), and Web of Science was performed for reports examining automated systems and volumetric scanning foraminal stenosis published before 31.07.2021. RESULTS: 3971 articles were identified of which 8 were included in the study. The automated grading systems of the neural foramen focus largely on the lumbar spine with elements that may be applicable to the cervical spine. Although there are established studies on the automated grading of the lumbar spine, it is uncertain whether any of these are reproducible in the cervical spine. Visual grading systems for the cervical spine demonstrate good inter-reader reliability between radiologists and clinicians. CONCLUSION: The Park visual grading method shows strong inter-reader reliability across radiologists and clinicians despite the limited data on the correlation with neurological symptoms or surgical outcome. There is scope for further development of an automated grading system for cervical foraminal stenosis to improve the speed and consistency of image interpretation.

Dynamic virtual simulation of the occurrence and severity of edge loading in hip replacements associated with variation in the rotational and translational surgical position.

Variation in the surgical positioning of total hip replacement can result in edge loading of the femoral head on the rim of the acetabular cup. Previous work has reported the effect of edge loading on the wear of hip replacement bearings with a fixed level of dynamic biomechanical hip separation. Variations in both rotational and translational surgical positioning of the hip joint replacement combine to influence both the biomechanics and the tribology including the severity of edge loading, the amount of dynamic separation, the force acting on the rim of the cup and the resultant wear and torque acting on the cup. In this study, a virtual model of a hip joint simulator has been developed to predict the effect of variations in some surgical positioning (inclination and medial-lateral offset) on the level of dynamic separation and the contact force of the head acting on the rim as a measure of severity of edge loading. The level of dynamic separation and force acting on the rim increased with increased translational mismatch between the centres of the femoral head and the acetabular cup from 0 to 4 mm and with increased cup inclination angle from 45° to 65°. The virtual model closely replicated the dynamics of the experimental hip simulator previously reported, which showed similar dynamic biomechanical trends, with the highest level of separation being found with a mismatch of 4 mm between the centres of the femoral head and acetabular cup and 65° cup inclination angle.