Single-molecule localization microscopy (SMLM) has revolutionized our ability to visualize cellular structures, offering unprecedented detail. However, the intricate biophysical principles that underlie SMLM can be daunting for newcomers, particularly undergraduate and graduate students. To address this challenge, we introduce the fundamental concepts of SMLM, providing a solid theoretical foundation. In addition, we have developed an intuitive graphical interface APP that simplifies these core concepts, making them more accessible for students. This APP clarifies how super-resolved images are fitted and highlights the crucial factors determining image quality. Our approach deepens students\' understanding of SMLM by combining theoretical instruction with practical learning. This development equips them with the skills to carry out single-molecule super-resolved experiments and explore the microscopic world beyond the diffraction limit.

BACKGROUND: Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) - a locally adaptive image filter - allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering.
METHODS: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( [ 18 F ] FDG, [ 18 F ] L-DOPA, [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the \"optimal\" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs.
RESULTS: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUV max values of the unfiltered images with a low mean ± SD difference of δ SUV max CNN , STD = (-3.9 ± 5.2)% and δ SUV max BF , STD = (-4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUV max values in the vast majority of cases with an overall average difference of δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed.
CONCLUSIONS: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

OBJECTIVE: Contrast-enhanced digital mammography (CEDM) is a relatively new imaging technique recombining low- and high-energy mammograms to emphasise iodine contrast. This work aims to perform a multicentric physical and dosimetric characterisation of four state-of-the-art CEDM systems.
METHODS: We evaluated tube output, half-value-layer (HVL) for low- and high-energy and average glandular dose (AGD) in a wide range of equivalent breast thicknesses. CIRS phantom 022 was used to estimate the overall performance of a CEDM examination in the subtracted image in terms of the iodine difference signal (S). To calculate dosimetric impact of CEDM examination, we collected 4542 acquisitions on patients.
RESULTS: Even if CEDM acquisition strategies differ, all the systems presented a linear behaviour between S and iodine concentration. The curve fit slopes expressed in PV/mg/cm2 were in the range [92-97] for Fujifilm, [31-32] for GE Healthcare, [35-36] for Hologic, and [114-130] for IMS. Dosimetric data from patients were matched with AGD values calculated using equivalent PMMA thicknesses. Fujifilm exhibited the lowest values, while GE Healthcare showed the highest.
CONCLUSIONS: The subtracted image showed the ability of all the systems to give important information about the linearity of the signal with the iodine concentrations. All the patient-collected doses were under the AGD EUREF 2D Acceptable limit, except for patients with thicknesses ≤35 mm belonging to GE Healthcare and Hologic, which were slightly over. This work demonstrates the importance of testing each CEDM system to know how it performs regarding dose and the relationship between PV and iodine concentration.

DNA fluorescence in situ hybridization (FISH) enables the visualization of chromatin architecture and the interactions between genomic loci at a single-cell level, complementary to genome-wide methods such as Hi-C. DNA FISH uses fluorescent-labeled DNA probes targeted to the loci of interest, allowing for the analysis of their spatial positioning and proximity with microscopy. Here, we describe an optimized experimental procedure for DNA FISH, from probe design and sample preparation through imaging and image quantification. This protocol can be readily applied to querying the spatial positioning of genomic loci of interest.

Automated image quantification workflows have dramatically improved over the past decade, enriching image analysis and enhancing the ability to achieve statistical power. These analyses have proved especially useful for studies in organisms such as Drosophila melanogaster, where it is relatively simple to obtain high sample numbers for downstream analyses. However, the developing wing, an intensively utilized structure in developmental biology, has eluded efficient cell counting workflows due to its highly dense cellular population. Here, we present efficient automated cell counting workflows capable of quantifying cells in the developing wing. Our workflows can count the total number of cells or count cells in clones labeled with a fluorescent nuclear marker in imaginal discs. Moreover, by training a machine-learning algorithm we have developed a workflow capable of segmenting and counting twin-spot labeled nuclei, a challenging problem requiring distinguishing heterozygous and homozygous cells in a background of regionally varying intensity. Our workflows could potentially be applied to any tissue with high cellular density, as they are structure-agnostic, and only require a nuclear label to segment and count cells.

Capillary density in skeletal muscles is key to estimate exercise capacity in healthy individuals, athletes, and those with muscle-related pathologies. Here, we present a step-by-step, high-throughput semi-automated method for quantifying capillary density from whole human skeletal muscle cross-sections, in areas of the muscle occupied by myofibers. We provide a detailed protocol for immunofluorescence staining, image acquisition, processing, and quantification. Image processing is performed in ImageJ, and data analysis is conducted in R. The provided protocol allows high-throughput quantification of capillary density. Key features • This protocol builds upon the method and results described in Abbassi-Daloii et al. (2023b). • It includes step-by-step details on image acquisition and image processing of the entire muscle section. • It enables high-throughput and semi-automated image quantification of capillary density. • It provides a robust analysis for determining capillary density over the entire muscle cross section.

Deep Learning (DL) is becoming an increasingly popular technology being employed in life sciences research due to its ability to perform complex and time-consuming tasks with significantly greater speed, accuracy, and reproducibility than human researchers - allowing them to dedicate their time to more complex tasks. One potential application of DL is to analyze cell images taken by microscopes. Quantitative analysis of cell microscopy images remain a challenge - with manual cell characterization requiring excessive amounts of time and effort. DL can address these issues, by quickly extracting such data and enabling rigorous, empirical analysis of images. Here, DL is used to quantitively analyze images of Mesenchymal Stem Cells (MSCs) differentiating into Osteoblasts (OBs), tracking morphological changes throughout this transition. The changes in morphology throughout the differentiation protocol provide evidence for a distinct path of morphological transformations that the cells undergo in their transition, with changes in perimeter being observable before changes in eceentricity. Subsequent differentiation experiments can be quantitatively compared with our dataset to concretely evaluate how different conditions affect differentiation and this paper can also be used as a guide for researchers on how to utilize DL workflows in their own labs.

Many proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution limit of conventional optical microscopy, single molecule localisation microscopy (SMLM) can map molecule locations with nanometre precision. The data from SMLM is not a conventional pixelated image and instead takes the form of a point-pattern-a list of the x, y coordinates of the localised molecules. To extract the biological insights that researchers require cluster analysis is often performed on these data sets, quantifying such parameters as the size of clusters, the percentage of monomers and so on. Here, we provide some guidance on how SMLM clustering should best be performed.

The zygote is the first cell of a multicellular organism. In most angiosperms, the zygote divides asymmetrically to produce an embryo-precursor apical cell and a supporting basal cell. Zygotic division should properly segregate symbiotic organelles, because they cannot be synthesized de novo. In this study, we revealed the real-time dynamics of the principle source of ATP biogenesis, mitochondria, in Arabidopsis thaliana zygotes using live-cell observations and image quantifications. In the zygote, the mitochondria formed the extended structure associated with the longitudinal array of actin filaments (F-actins) and were polarly distributed along the apical-basal axis. The mitochondria were then temporally fragmented during zygotic division, and the resulting apical cells inherited mitochondria at higher concentration compared to the basal cells. Further observation of postembryonic organs showed that these mitochondrial behaviours are characteristic of the zygote. Overall, our results showed that the zygote has spatiotemporal regulation that unequally distributes the mitochondria.

The Drosophila visual center shows columnar structures, basic structural and functional units of the brain, that are shared with the mammalian cerebral cortex. Visual information received in the ommatidia in the compound eye is transmitted to the columns in the brain. However, the developmental mechanisms of column formation are largely unknown. The Irre Cell Recognition Module (IRM) proteins are a family of immunoglobulin cell adhesion molecules. The four Drosophila IRM proteins are localized to the developing columns, the structure of which is affected in IRM mutants, suggesting that IRM proteins are essential for column formation. Since IRM proteins are cell adhesion molecules, they may regulate cell adhesion between columnar neurons. To test this possibility, we specifically knocked down IRM genes in columnar neurons and examined the defects in column formation. We developed a system that automatically extracts the individual column images and quantifies the column shape. Using this system, we demonstrated that IRM genes play critical roles in regulating column shape in a core columnar neuron, Mi1. We also show that their expression in the other columnar neurons, Mi4 and T4/5, is essential, suggesting that the interactions between IRM proteins and multiple neurons shape the columns in the fly brain.