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

Guided-deconvolution for correlative light and electron microscopy

Correlative light and electron microscopy is a powerful tool to study the internal structure of cells. It combines the mutual benefit of correlating light (LM) and electron (EM) microscopy information. The EM images only contain contrast information. Therefore, some of the detailed structures cannot be specified from these images alone, especially when different cell organelle are contacted. However, the classical approach of overlaying LM onto EM images to assign functional to structural information is hampered by the large discrepancy in structural detail visible in the LM images. This paper aims at investigating an optimized approach which we call EM-guided deconvolution. This applies to living cells structures before fixation as well as previously fixed sample. It attempts to automatically assign fluorescence-labeled structures to structural details visible in the EM image to bridge the gaps in both resolution and specificity between the two imaging modes. We tested our approach on simulations, correlative data of multi-color beads and previously published data of biological samples.

Analysis of HDACi-Coupled Nanoparticles: Opportunities and Challenges

Systemic administration of histone deacetylase inhibitors (HDACi), like valproic acid (VPA), is often associated with rapid drug metabolization and untargeted tissue distribution. This requires high-dose application that can lead to unintended side effects. Hence, drug carrier systems such as nanoparticles (NPs) are developed to circumvent these disadvantages by enhancing serum half-life as well as organ specificity.This chapter gives a summary of the biological characterization of HDACi-coupled NPs in vitro, including investigation of cellular uptake, biocompatibility, as well as intracellular drug release and activity. Suitable methods, opportunities, and challenges will be discussed to provide general guidelines for the analysis of HDACi drug carrier systems with a special focus on recently developed cellulose-based VPA-coupled NPs.

An LED-Based structured illumination microscope using a digital micromirror device and GPU accelerated image reconstruction

When combined with computational approaches, fluorescence imaging becomes one of the most powerful tools in biomedical research. It is possible to achieve resolution figures beyond the diffraction limit, and improve the performance and flexibility of high-resolution imaging systems with techniques such as structured illumination microscopy (SIM) reconstruction. In this study, the hardware and software implementation of an LED-based super-resolution imaging system using SIM employing GPU accelerated parallel image reconstruction is presented. The sample is illuminated with two-dimensional sinusoidal patterns with various orientations and lateral phase shifts generated using a digital micromirror device (DMD). SIM reconstruction is carried out in frequency space using parallel CUDA kernel functions. Furthermore, a general purpose toolbox for the parallel image reconstruction algorithm and an infrastructure that allows all users to perform parallel operations on images without developing any CUDA kernel code is presented. The developed image reconstruction algorithm was run separately on a CPU and a GPU. Two different SIM reconstruction algorithms have been developed for the CPU as mono-thread CPU algorithm and multi-thread OpenMP CPU algorithm. SIM reconstruction of 1024 × 1024 px images was achieved in 1.49 s using GPU computation, indicating an enhancement by ∼28 and ∼20 in computation time when compared with mono-thread CPU computation and multi-thread OpenMP CPU computation, respectively.