Super-resolution fluorescence microscopy methods, which can circumvent the diffraction limit in light microscopy, have found widespread use in biological research in the past decade and their popularity continues to grow. Imaging methods based on the localisation of individual fluorophores (STORM/PALM) are powerful tools for imaging cell structures at a length scale of few tens of nm, but their usefulness for live cell imaging is limited, as it takes a long time to acquire the data needed for a super-resolution image.
Data acquisition time for localisation microscopy can be cut by more than two orders of magnitude by collecting high density data where the individual fluorophores overlap in each frame. Many specialised algorithms exist for processing this type of data, but they all tend to produce artefacts in the reconstructed image under certain conditions. The nature of these artefacts depends on the local structure and can resemble true biological features, making them very difficult to detect. The origin of these artefacts, as well as the assessment of resolution in localisation microscopy, which is made difficult by the presence of false structure in the reconstructed image, will be discussed.
I will also present our new and simple analysis tool, Haar Wavelet Kernel Analysis (HAWK),[1] and demonstrate that these artifacts can be eliminated by the combination of HAWK with standard single-frame fitting. We have tested the performance of this method on synthetic, fixed-cell, and live-cell data, and found that HAWK preprocessing yielded reconstructions that reflected the structure of the sample, thus enabling high-speed, artifact-free super-resolution imaging of live cells.