Recent work using micro-Fourier transform infrared (μFTIR) imaging has revealed that a lipid-rich layer surrounds many plaques in post-mortem Alzheimer's brain. However, the origin of this lipid layer is not known, nor is its role in the pathogenesis of Alzheimer's disease (AD). Here, we studied the biochemistry of plaques in situ using a model of AD. We combined FTIR, Raman and immunofluorescence images, showing that astrocyte processes co-localise with the lipid ring surrounding many plaques. We used μFTIR imaging to rapidly measure chemical signatures of plaques over large fields of view, and selected plaques for higher resolution analysis with Raman microscopy. Raman maps showed similar lipid rings and dense protein cores as in FTIR images, but also revealed cell bodies. We confirmed the presence of plaques using amylo-glo staining, and detected astrocytes using immunohistochemistry, revealing astrocyte co-localisation with lipid rings. This work is important because it correlates biochemical changes surrounding the plaque with the biological process of astrogliosis.

Photothermal therapy using nanoparticles is a promising new approach for the treatment of cancer. The principle is to utilise plasmonic nanoparticle light interaction for efficient heat conversion. However, there are many hurdles to overcome before it can be accepted in clinical practice. One issue is a current poor characterization of the thermal dose that is distributed over the tumour region and the surrounding normal tissue. Here, we use Monte Carlo simulations of photon radiative transfer through tissue and subsequent heat diffusion calculations, to model the spatial thermal dose in a skin cancer model. We validate our heat rise simulations against experimental data from the literature and estimate the concentration of nanorods in the tumor that are associated with the heat rise. We use the cumulative equivalent minutes at 43 °C (CEM43) metric to analyse the percentage cell kill across the tumour and the surrounding normal tissue. Overall, we show that computer simulations of photothermal therapy are an invaluable tool to fully characterize thermal dose within tumour and normal tissue.

Techniques to analyse human telomeres are imperative in studying the molecular mechanism of ageing and related diseases. Two important aspects of telomeres are their length in DNA base pairs (bps), and their biophysical nanometer dimensions. However, there are currently no techniques that can simultaneously measure these quantities in individual cell nuclei. Here, we develop and evaluate a telomere “dual” gold nanoparticle-fluorescent probe simultaneously compatible with both X-ray fluorescence (XRF) and super resolution microscopy. We used silver enhancement to independently visualise the spatial locations of gold nanoparticles inside nuclei, comparing to a standard QFISH (quantitative fluorescence in-situ hybridisation) probe, and showed good specificity at ~90%. For sensitivity, we calculated telomere length based on a DNA:gold binding ratio using XRF, and compared to quantitative polymerase chain reaction (qPCR) measurements. The sensitivity was low (~10%), probably because of steric interference prohibiting the relatively large 10 nm gold nanoparticles access to DNA space. We then measured the biophysical characteristics of individual telomeres using super resolution microscopy. Telomeres that have an average length of ~10 kbps, have diameters ranging between ~60-300 nm. Further, we treated cells with a telomere-shortening drug, and showed there was a small but significant difference in telomere diameter in drug-treated vs control cells. We discuss our results in relation to the current debate surrounding telomere compaction.

There are many possible biomedical applications for titania nanoparticles (NPs) doped with rare earth elements (REEs), from dose enhancement and diagnostic imaging in radiotherapy, to biosensing. However, there are concerns that the NPs could disintegrate in the body thus releasing toxic REE ions to undesired locations. As a first step, we investigate how accurately the Ti/REE ratio from the NPs can be measured inside human cells. A quantitative analysis of whole, unsectioned, individual human cells was performed using proton microprobe elemental microscopy. This method is unique in being able to quantitatively analyse all the elements in an unsectioned individual cell with micron resolution, while also scanning large fields of view. We compared the Ti/REE signal inside cells to NPs that were outside the cells, non-specifically absorbed onto the polypropylene substrate. We show that the REE signal in individual cells co-localises with the titanium signal, indicating that the NPs have remained intact. Within the uncertainty of the measurement, there is no difference between the Ti/REE ratio inside and outside the cells. Interestingly, we also show that there is considerable variation in the uptake of the NPs from cell-to-cell, by a factor of more than 10. We conclude that the NPs enter the cells and remain intact. The large heterogeneity in NP concentrations from cell-to-cell should be considered if they are to be used therapeutically.

One area of active research is the use of natural language processing (NLP) to mine biomedical texts for sets of triples (subject-predicate-object) for knowledge graph (KG) construction. While statistical methods to mine co-occurrences of entities within sentences are relatively robust, accurate relationship extraction is more challenging. Herein, we evaluate the Global Network of Biomedical Relationships (GNBR), a dataset that uses distributional semantics to model relationships between biomedical entities. The focus of our paper is an evaluation of a subset of the GNBR data; the relationships between chemicals and genes/proteins. We use Evotec’s structured ‘Nexus’ database of >2.76M chemical-protein interactions as a ground truth to compare with GNBRs relationships and find a micro-averaged precision-recall area under the curve (AUC) of 0.50 and a micro-averaged receiver operating characteristic (ROC) curve AUC of 0.71 across the relationship classes ‘inhibits’, ‘binding’, ‘agonism’ and ‘antagonism’, when a comparison is made on a sentence-by-sentence basis. We conclude that, even though these micro-average scores are modest, using a high threshold on certain relationship classes like ‘inhibits’ could yield high fidelity triples that are not reported in structured datasets. We discuss how different methods of processing GNBR data, and the factuality of triples could affect the accuracy of NLP data incorporated into knowledge graphs. We provide a GNBR-Nexus(ChEMBL-subset) merged datafile that contains over 20,000 sentences where a protein/gene-chemical co-occur and includes both the GNBR relationship scores as well as the ChEMBL (manually curated) relationships (e.g., ‘agonist’, ‘inhibitor’) —this can be accessed at https://doi.org/10.5281/zenodo.8136752. We envisage this being used to aid curation efforts by the drug discovery community.