We present a robust, low-cost neural network-based optical autofocus system that can operate over a range of ±100μm with submicron precision, enabling automated high-content super-resolved imaging with a 1.3 NA objective lens.
Super-resolved microscopy techniques have overcome the diffraction limit to provide image resolutions approaching the scale of fluorescent labels. However, many of these techniques require significant experimental resources and expertise and impose long image data acquisition times, making it difficult to acquire super-resolved data from sufficiently large sample numbers to overcome intrinsic biological variation. We have worked to make stimulated emission depletion (STED) microscopy and single molecule localisation microscopy (SMLM) more straightforward to implement and more practical to image larger numbers of cells. Here we present work in progress developing easySLM STED and easySTORM, including a new modular microscope frame that we believe can make it easier to prototype microscopy techniques and to implement and maintain them in lower resourced settings.
Among super-resolved microscopy (SRM) methods, single molecule localisation microscopy techniques, such as photo-activated localisation microscopy (PALM) [1] and stochastic optical reconstruction microscopy (STORM) [2], enable imaging beyond the classical diffraction limit to gain new insights in subcellular biological processes with relatively simple instrumentation. This has led to a number of low-cost instruments, e.g. for STORM microscopy [3-6], which can benefit from an array of software tools for the single molecule localisation microscopy (SMLM) data analysis [7]. Our low-cost “easySTORM” approach [4] implements dSTORM [8] with multimode diode lasers and optical fibres to provide STORM images with fields of view up to ~125 μm diameter using μManager [9] to control the image data acquisition and ThunderSTORM [10] to analyse the SMLM data. We and others [11,12] are motivated to develop automated SMLM for high content analysis (HCA) that enable rapid imaging of sample arrays, allows statistical analysis of samples that may vary in terms of labelling and biological heterogeneity and enable moderate throughput screening applications.
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