Microscope Point Spread Research Articles

DeconvTest: Simulation framework for quantifying errors and selecting optimal parameters of image deconvolution.

Deconvolution is an essential step of image processing that aims to compensate for the image blur caused by the microscope's point spread function. With many existing deconvolution methods, it is challenging to choose the method and its parameters most appropriate for particular image data at hand. To facilitate this task, we developed DeconvTest: an open-source Python-based framework for generating synthetic microscopy images, deconvolving them with different algorithms, and quantifying reconstruction errors. In contrast to existing software, DeconvTest combines all components required to analyze deconvolution performance in a systematic, high-throughput and quantitative manner. We demonstrate the power of the framework by using it to identify the optimal deconvolution settings for synthetic and real image data. Based on this, we provide a guideline for (a) choosing optimal values of deconvolution parameters for image data at hand and (b) optimizing imaging conditions for best results in combination with subsequent image deconvolution.

Self-interference 3D super-resolution microscopy for deep tissue investigations.

Fluorescence localization microscopy has achieved near-molecular resolution capable of revealing ultra-structures, with a broad range of applications, especially in cellular biology. However, it remains challenging to attain such resolution in three dimensions and inside biological tissues beyond the first cell layer. Here we introduce SELFI, a framework for 3D single-molecule localization within multicellular specimens and tissues. The approach relies on self-interference generated within the microscope's point spread function (PSF) to simultaneously encode equiphase and intensity fluorescence signals, which together provide the 3D position of an emitter. We combined SELFI with conventional localization microscopy to visualize F-actin 3D filament networks and reveal the spatial distribution of the transcription factor OCT4 in human induced pluripotent stem cells at depths up to 50 µm inside uncleared tissue spheroids. SELFI paves the way to nanoscale investigations of native cellular processes in intact tissues.

Reflection across plant cell boundaries in confocal laser scanning microscopy

The fluorescence patterns of proteins tagged with the green fluorescent protein (GFP) and its derivatives are routinely used in conjunction with confocal laser scanning microscopy to identify their sub-cellular localization in plant cells. GFP-tagged proteins localized to plasmodesmata, the intercellular junctions of plants, are often identified by single or paired punctate labelling across the cell wall. The observation of paired puncta, or 'doublets', across cell boundaries in tissues that have been transformed through biolistic bombardment is unexpected if there is no intercellular movement of the GFP-tagged protein, since bombardment usually leads to the transformation of single, isolated cells. We expressed a putative plasmodesmal protein tagged with GFP by bombarding Allium porrum epidermal cells and assessed the nature of the doublets observed at the cell boundaries. Doublets were formed when fluorescent spots were abutting a cell boundary and were only observable at certain focal planes. Fluorescence emitted from the half of a doublet lying outside the transformed cells was polarized. Optical simulations performed using finite-difference time-domain computations showed a dramatic distortion of the confocal microscope's point spread function when imaging voxels close to the plant cell wall due to refractive index differences between the wall and the cytosol. Consequently, axially and radially out-of-focus light could be detected. A model of this phenomenon suggests how a doublet may form when imaging only a single real fluorescent body in the vicinity of a plant cell wall using confocal microscopy. We suggest, therefore, that the appearance of doublets across cell boundaries is insufficient evidence for plasmodesmal localization due to the effects of the cell wall on the reflection and scattering of light.

Model-Based 2.5-D Deconvolution for Extended Depth of Field in Brightfield Microscopy

Due to the limited depth of field of brightfield microscopes, it is usually impossible to image thick specimens entirely in focus. By optically sectioning the specimen, the in-focus information at the specimen's surface can be acquired over a range of images. Commonly based on a high-pass criterion, extended-depth-of-field methods aim at combining the in-focus information from these images into a single image of the texture on the specimen's surface. The topography provided by such methods is usually limited to a map of selected in-focus pixel positions and is inherently discretized along the axial direction, which limits its use for quantitative evaluation. In this paper, we propose a method that jointly estimates the texture and topography of a specimen from a series of brightfield optical sections; it is based on an image formation model that is described by the convolution of a thick specimen model with the microscope's point spread function. The problem is stated as a least-squares minimization where the texture and topography are updated alternately. This method also acts as a deconvolution when the in-focus PSF has a blurring effect, or when the true in-focus position falls in between two optical sections. Comparisons to state-of-the-art algorithms and experimental results demonstrate the potential of the proposed approach.