Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences

Image informatics approaches to advance cancer drug discovery

High content image-based screening assays utilise cell based models to extract and quantify morphological phenotypes induced by small molecules. The rich datasets produced can be used to identify lead compounds in drug discovery efforts, infer compound mechanism of action, or aid biological understanding with the use of tool compounds. Here I present my work developing and applying high-content image based screens of small molecules across a panel of eight genetically and morphologically distinct breast cancer cell lines. I implemented machine learning models to predict compound mechanism of action from morphological data and assessed how well these models transfer to unseen cell lines, comparing the use of numeric morphological features extracted using computer vision techniques against more modern convolutional neural networks acting on raw image data. The application of cell line panels have been widely used in pharmacogenomics in order to compare the sensitivity between genetically distinct cell lines to drug treatments and identify molecular biomarkers that predict response. I applied dimensional reduction techniques and distance metrics to develop a measure of differential morphological response between cell lines to small molecule treatment, which controls for the inherent morphological differences between untreated cell lines. These methods were then applied to a screen of 13,000 lead-like small molecules across the eight cell lines to identify compounds which produced distinct phenotypic responses between cell lines. Putative hits from a subset of approved compounds were then validated in a three-dimensional tumour spheroid assay to determine the functional effect of these compounds in more complex models, as well as proteomics to determine the responsible pathways. Using data generated from the compound screen, I carried out work towards integrating knowledge of chemical structures with morphological data to infer mechanistic information of the unannotated compounds, and assess structure activity relationships from cell-based imaging data

Learning to prescribe - pharmacists' experiences of supplementary prescribing training in England

Background: The introduction of non-medical prescribing for professions such as pharmacy and nursing in recent years offers additional responsibilities and opportunities but attendant training issues. In the UK and in contrast to some international models, becoming a non-medical prescriber involves the completion of an accredited training course offered by many higher education institutions, where the skills and knowledge necessary for prescribing are learnt. Aims: to explore pharmacists' perceptions and experiences of learning to prescribe on supplementary prescribing (SP) courses, particularly in relation to inter-professional learning, course content and subsequent use of prescribing in practice. Methods: A postal questionnaire survey was sent to all 808 SP registered pharmacists in England in April 2007, exploring demographic, training, prescribing, safety culture and general perceptions of SP. Results: After one follow-up, 411 (51%) of pharmacists responded. 82% agreed SP training was useful, 58% agreed courses provided appropriate knowledge and 62% agreed that the necessary prescribing skills were gained. Clinical examination, consultation skills training and practical experience with doctors were valued highly; pharmacology training and some aspects of course delivery were criticised. Mixed views on inter-professional learning were reported – insights into other professions being valued but knowledge and skills differences considered problematic. 67% believed SP and recent independent prescribing (IP) should be taught together, with more diagnostic training wanted; few pharmacists trained in IP, but many were training or intending to train. There was no association between pharmacists' attitudes towards prescribing training and when they undertook training between 2004 and 2007 but earlier cohorts were more likely to be using supplementary prescribing in practice. Conclusion: Pharmacists appeared to value their SP training and suggested improvements that could inform future courses. The benefits of inter-professional learning, however, may conflict with providing professionspecific training. SP training may be perceived to be an instrumental 'stepping stone' in pharmacists' professional project of gaining full IP status

Reduced antibody cross-reactivity following infection with B.1.1.7 than with parental SARS-CoV-2 strains

Background: The degree of heterotypic immunity induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains is a major determinant of the spread of emerging variants and the success of vaccination campaigns, but remains incompletely understood. Methods: We examined the immunogenicity of SARS-CoV-2 variant B.1.1.7 (Alpha) that arose in the United Kingdom and spread globally. We determined titres of spike glycoprotein-binding antibodies and authentic virus neutralising antibodies induced by B.1.1.7 infection to infer homotypic and heterotypic immunity. Results: Antibodies elicited by B.1.1.7 infection exhibited significantly reduced recognition and neutralisation of parental strains or of the South Africa variant B.1.351 (Beta) than of the infecting variant. The drop in cross-reactivity was significantly more pronounced following B.1.1.7 than parental strain infection. Conclusions: The results indicate that heterotypic immunity induced by SARS-CoV-2 variants is asymmetric

Neutralising immunity to omicron sublineages BQ.1.1, XBB, and XBB.1.5 in healthy adults is boosted by bivalent BA.1-containing mRNA vaccination and previous Omicron infection

The global COVID-19 landscape is increasingly complex; emerging new variants rapidly cause waves of infection in people with variably induced immunity. Most individuals now have so-called hybrid immunity from both infection and vaccination. However, sequential infecting variants, induction of immunity, and subsequent waning are interlinked, and immune protection against new variants is unclear