Cluster ToF-SIMS (time-of-flight secondary ion mass spectrometry) operated in the event-by-event bombardment/detection mode has been applied to: 1) evaluate and screen the manufacturing quality of step-wise prepared micropatterned biointerfaces; 2) quantify the binding density of Au nanoparticles (AuNPs)-antiCD4 conjugates selectively attached on the cell surface; 3) elucidate the biological interaction of proteins and molecules by quantifying the fractional coverage of immobilized biomolecules; 4) enhance the accuracy of secondary ion identification of specific molecules. Briefly, our method consists of recording the secondary ions, SIs, individually emitted from a single projectile impact (C60 1,2+, Au400 +4). From the set of individual mass data, we select events where a specific SI was detected. The selected records reveal the SIs co-ejected from the nanovolume impacted by an individual cluster projectile from an emission area of 10-20 nm in diameter and an emission depth of 5-10 nm. The approach for quantifying the number of AuNPs or that of specific nanodomains is via the concept of the fractional coverage. The latter is the ratio of the effective number of projectile impacts on a specified sampling area (Ne) to the total number of impacts (No). The methodology has been validated with the determination of the number of antibody-AuNP conjugates on a cell, i.e. the number of disease related antigens on a cell via their specific binding sites with the AuNP-labeled antibodies. The number of AuNP-antibodies measured, ~42000 per cell, is in good agreement with literature results. The fractional coverage concept was also used to quantify several variants of biointerfaces. An example is the quantification of biotin and avidin immobilization as a function of the composition of silane substrates. The data collected in the event-by-event bombardment/detection mode expands the scope and quality of analytical information. One can identify SIs co-emitted with two specified SIs (double coincidence mass spectrometry) to inspect a specific stratum of a biointerface. A further refinement is the selection of events meeting a double coincidence emission condition. This mode enables the identification of nano-object of a few nm in size, which eliminates (anticoincidence) interferences from substrates.

ToF-SIMS imaging has been drawing attention due to the wide range of applications in the biological and biomedical fields. These applications include the acquisition of quantitative and qualitative data that ranges in scale from single cells to organs, image visualisation and interpretation of biomarkers for diagnosis and development of pharmaceutics. This study focused on molecular imaging of mouse brain tissue sections using cluster primary ion beams. First, cluster ion beams were applied to comparative background studies of biomolecules and brain total lipid extract. Enhancement of the secondary ion signal was observed using water-containing cluster primary ion beams, especially for [M+H]+ type secondary ions. Water-containing clusters were then used to acquire ToF-SIMS images from the cerebellar area of serial mouse brain tissue sections. Again, water-containing cluster beams produced the highest secondary ion yields in both grey and white matter, gaining a new level of insight into the lipid compositions of both types of tissue in the brain. A clinical case was also evaluated with ToF-SIMS imaging, using cluster beams for the analysis of 3xTg-AD mouse brain tissue. SIMS images were registered with fluorescence microscopy images for the in situ identification and co-localisation of the Amyloid-β plaques on the SIMS images. Spectra from regions of interest were analysed to identify possible ion fragments derived from the Aβ protein. The co-localisation of cholesterol was also studied from images obtained with different primary ion beams. The results presented show that cluster ToF-SIMS can be successfully applied to brain tissue imaging. New primary ion beam technologies allow us to acquire data with more useful secondary ion yield for clinical applications and biological research. Nevertheless, future technological improvements are required for specialised applications e.g. cellular imaging. Moreover, processing the data obtained is still challenging and more data processing tools are also needed for interpretation.