A new liquid metal ion gun (LMIG) filled with bismuth has been fitted to a time-of-flight-secondary ion mass spectrometer (TOF-SIMS). This source provides beams of Bi(n)q+ clusters with n = 1-7 and q = 1 and 2. The appropriate clusters have much better intensities and efficiencies than the Au3+ gold clusters recently used in TOF-SIMS imaging, and allow better lateral and mass resolution. The different beams delivered by this ion source have been tested for biological imaging of rat brain sections. The results show a great improvement of the imaging capabilities in terms of accessible mass range and useful lateral resolution. Secondary ion yields Y, disappearance cross sections sigma, efficiencies E = Y/sigma , and useful lateral resolutions deltaL have been compared using the different bismuth clusters, directly onto the surface of rat brain sections and for several positive and negative secondary ions with m/z ranging from 23 up to more than 750. The efficiency and the imaging capabilities of the different primary ions are compared by taking into account the primary ion current for reasonable acquisition times. The two best primary ions are Bi3+ and Bi5(2+). The Bi3+ ion beam has a current at least five times larger than Au3+ and therefore is an excellent beam for large-area imaging. Bi5(2+) ions exhibit large secondary ions yields and a reasonable intensity making them suitable for small-area images with an excellent sensitivity and a possible useful lateral resolution<400 nm.

It is a common problem in natural product therapeutic lead discovery programs that despite good bioassay results in the initial extract, the active compound(s) may not be isolated during subsequent bioassay-guided purification. Herein, we present the concept of bioactive molecular networking to find candidate active molecules directly from fractionated bioactive extracts. By employing tandem mass spectrometry, it is possible to accelerate the dereplication of molecules using molecular networking prior to subsequent isolation of the compounds, and it is also possible to expose potentially bioactive molecules using bioactivity score prediction. Indeed, bioactivity score prediction can be calculated with the relative abundance of a molecule in fractions and the bioactivity level of each fraction. For that reason, we have developed a bioinformatic workflow able to map bioactivity score in molecular networks and applied it for discovery of antiviral compounds from a previously investigated extract of Euphorbia dendroides where the bioactive candidate molecules were not discovered following a classical bioassay-guided fractionation procedure. It can be expected that this approach will be implemented as a systematic strategy, not only in current and future bioactive lead discovery from natural extract collections but also for the reinvestigation of the untapped reservoir of bioactive analogues in previous bioassay-guided fractionation efforts.