No abstract

STORM is a recently developed super-resolution microscopy technique with up to 10 times better resolution than standard fluorescence microscopy techniques. However, as the image is acquired in a very different way than normal, by building up an image molecule-by-molecule, there are some significant challenges for users in trying to optimize their image acquisition. In order to aid this process and gain more insight into how STORM works we present the preparation of 3 test samples and the methodology of acquiring and processing STORM super-resolution images with typical resolutions of between 30-50 nm. By combining the test samples with the use of the freely available rainSTORM processing software it is possible to obtain a great deal of information about image quality and resolution. Using these metrics it is then possible to optimize the imaging procedure from the optics, to sample preparation, dye choice, buffer conditions, and image acquisition settings. We also show examples of some common problems that result in poor image quality, such as lateral drift, where the sample moves during image acquisition and density related problems resulting in the 'mislocalization' phenomenon.

This paper describes the production and characteristics of the nanoparticle test materials prepared for common use in the collaborative research project NanoChOp (Chemical and optical characterization of nanomaterials in biological systems), in casu suspensions of silica nanoparticles and CdSe/CdS/ZnS quantum dots (QDs). This paper is the first to illustrate how to assess whether nanoparticle test materials meet the requirements of a “reference material” (ISO Guide 30, 2015) or rather those of the recently defined category of “representative test material (RTM)” (ISO/TS 16195, 2013). The NanoChOp test materials were investigated with small-angle X-ray scattering (SAXS), dynamic light scattering (DLS), and centrifugal liquid sedimentation (CLS) to establish whether they complied with the required monomodal particle size distribution. The presence of impurities, aggregates, agglomerates, and viable microorganisms in the suspensions was investigated with DLS, CLS, optical and electron microscopy and via plating on nutrient agar. Suitability of surface functionalization was investigated with attenuated total reflection Fourier transform infrared spectrometry (ATR-FTIR) and via the capacity of the nanoparticles to be fluorescently labeled or to bind antibodies. Between-unit homogeneity and stability were investigated in terms of particle size and zeta potential. This paper shows that only based on the outcome of a detailed characterization process one can raise the status of a test material to RTM or reference material, and how this status depends on its intended use.

Here we describe scattering based signal suppression artifacts encountered while developing multiplex lateral flow (LF) immunoassay using surface enhanced Raman spectroscopy (SERS) "nanotags" as analyte labels. Using these SERS nanotags, we have produced a quantitative test for inflammation biomarkers that is transferable to the point of care (POC). The SERS assay shows similar performance when compared with a fluorescent nanoparticle POC test. Here, using cardiac and inflammation biomarkers, we highlight the need to carefully optimize the concentration of assay components when using SERS nanotags and a single-line multiplexing approach. We show that in certain circumstances the SERS signal may be suppressed, leading to a significant underestimation of the analyte concentrations. Using electron microscopy and optical spectroscopy, we demonstrate that the error in the measurement is associated with the light scattering properties of the nanotags. These findings will be applicable to other nanoparticle labels with high light scattering coefficients. Through careful modification of the assay to reduce the impact of light scattering, it is possible to produce quantitative assays, but potentially at the expense of multiplexing capability and assay sensitivity.

The batch-to-batch assay performance ‘activity’ of antibody conjugated particles is often variable, leading to poor reproducibility between different production batches. DCS analysis provides a quantitative method to characterise particle oligomerisation, providing a rationale for variable assay performance of different conjugate batches.