Moment-preserving thresolding: A new approach

Apoptosis Quantification in Tissue: Development of a Semi-Automatic Protocol and Assessment of Critical Steps of Image Processing

Apoptosis is associated with numerous phenotypical characteristics, and is thus studied with many tools. In this study, we compared two broadly used apoptotic assays: TUNEL and staining with an antibody targeting the activated form of an effector caspase. To compare them, we developed a protocol based on commonly used tools such as image filtering, z-projection, and thresholding. Even though it is commonly used in image-processing protocols, thresholding remains a recurring problem. Here, we analyzed the impact of processing parameters and readout choice on the accuracy of apoptotic signal quantification. Our results show that TUNEL is quite robust, even if image processing parameters may not always allow to detect subtle differences of the apoptotic rate. On the contrary, images from anti-cleaved caspase staining are more sensitive to handle and necessitate being processed more carefully. We then developed an open-source Fiji macro automatizing most steps of the image processing and quantification protocol. It is noteworthy that the field of application of this macro is wider than apoptosis and it can be used to treat and quantify other kind of images.

A New Validation Methodology for In Silico Tools Based on X-ray Computed Tomography Images of Tablets and a Performance Analysis of One Tool

In silico tools which predict the dissolution of pharmaceutical dosage forms using virtual matrices can be validated with virtual matrices based on X-ray micro-computed tomography images of real pharmaceutical formulations. Final processed images of 3 different tablet batches were used to check the performance of the in silico tool F-CAD. The goal of this work was to prove the performance of the software by comparing the predicted dissolution profiles to the experimental ones and to check the feasibility and application of the validation concept for in silico tools. Both virtual matrices based on X-ray micro-computed tomography images and designed by the software itself were used. The resulting dissolution curves were compared regarding their similarity to the experimental curve. The kinetics were analysed with the Higuchi and Korsmeyers-Peppas plot. The whole validation concept as such was feasible and worked well. It was possible to identify prediction errors of the software F-CAD and issues with the virtual tablets designed within the software.

Quantitative 3D microscopy highlights altered von Willebrand factor α-granule storage in patients with von Willebrand disease with distinct pathogenic mechanisms

BACKGROUND

Platelets play a key role in hemostasis through plug formation and secretion of their granule contents at sites of endothelial injury. Defects in von Willebrand factor (VWF), a platelet α-granule protein, are implicated in von Willebrand disease (VWD), and may lead to defective platelet adhesion and/or aggregation. Studying VWF quantity and subcellular localization may help us better understand the pathophysiology of VWD.

OBJECTIVE

Quantitative analysis of the platelet α-granule compartment and VWF storage in healthy individuals and VWD patients.

PATIENTS/METHODS

Structured illumination microscopy (SIM) was used to study VWF content and organization in platelets of healthy individuals and patients with VWD in combination with established techniques.

RESULTS

SIM capably quantified clear morphological and granular changes in platelets stimulated with proteinase-activated receptor 1 (PAR-1) activating peptide and revealed a large intra- and interdonor variability in VWF-positive object numbers within healthy resting platelets, similar to variation in secreted protein acidic and rich in cysteine (SPARC). We subsequently characterized VWD platelets to identify changes in the α-granule compartment of patients with different VWF defects, and were able to stratify two patients with type 3 VWD rising from different pathological mechanisms. We further analyzed VWF storage in α-granules of a patient with homozygous p.C1190R using electron microscopy and found discrepant VWF levels and different degrees of multimerization in platelets of patients with heterozygous p.C1190 in comparison to VWF in plasma.

CONCLUSIONS

Our findings highlight the utility of quantitative imaging approaches in assessing platelet granule content, which may help to better understand VWF storage in α-granules and to gain new insights in the etiology of VWD.

The impact of different thresholds on optical coherence tomography angiography images binarization and quantitative metrics

Optical coherence tomography angiography (OCTA) provides several data regarding the status of retinal capillaries. This information can be further enlarged by employing quantitative metrics, such as vessel density (VD). A mandatory preliminary step of OCTA quantification is image binarization, a procedure used to highlight retinal capillaries on empty background. Although several binarization thresholds exist, no consensus is reached about the thresholding technique to be used. In this study, we tested thirteen binarization thresholds on a dataset made by thirty volunteers. The aim was to assess the impact of binarization techniques on: (I) detection of retinal capillaries, assessed by the calculation of overlapping percentages between binarized and original images; (II) quantitative OCTA metrics, including VD, vessel tortuosity (VT) and vessel dispersion (Vdisp); (III) foveal avascular zone (FAZ) detection. Our findings showed Huang, Li, Mean and Percentile as highly reliable binarization thresholds (p < 0.05), whereas the worst binarization thresholds were Intermodes, MaxEntropy, RenylEntropy and Yen (p < 0.05). All the thresholds variably underestimated VD metric and FAZ detection, with respect to the original OCTA images, whereas VT and Vdisp turned out to be more stable. The usage of a Fixed threshold resulted extremely useful to reduce VD and FAZ underestimations, although bound to operators' experience.

Evaluation of different segmentation methods of X-ray micro computed tomography images

In silico tools for the prediction of disintegration and/or dissolution of tablets can be validated using adequate images of real pharmaceutical formulations. X-ray micro-computed tomography images of 12 different tablet batches prepared from binary mixtures of API and excipient were used. The goal of this work was to compare different segmentation methods to improve the results and processing time of an evaluation of pre-processing methods. The open source software ImageJ was utilised for the image processing. Different threshold algorithms were applied as well as different cluster numbers for the k-means clustering. The pathways were analysed regarding their desirability which was calculated from the recovery rates and their ratios. It was possible to identify suitable pathways for each single batch as well as for combinations of several batches. The recovery rates of the best pathways were always approximately 100%. It was possible to confirm the correctness of the image processing by visual perception. The image processing could be improved and sped up.

Quantitative structural imaging of keratoconic corneas using polarization-resolved SHG microscopy

The human cornea is mainly composed of collagen fibrils aligned together within stacked lamellae. This lamellar structure can be affected in pathologies such as keratoconus, which is characterized by progressive corneal thinning and local steepening. In this study, we use polarization-resolved second harmonic generation (P-SHG) microscopy to characterize 8 control and 6 keratoconic human corneas. Automated processing of P-SHG images of transverse sections provides the collagen orientation in every pixel with sub-micrometer resolution. Series of P-SHG images recorded in the most anterior region of the stroma evidence sutural lamellae inclined at 22° ± 5° to the corneal surface, but show no significant difference between control and keratoconic corneas. In contrast, series of P-SHG images acquired along the full thickness of the stroma show a loss of order in the lamellar structure of keratoconic corneas, in agreement with their defective mechanical properties. This structural difference is analyzed quantitatively by computing the entropy and the orientation index of the collagen orientation distribution and significant differences are obtained along the full thickness of the stroma. This study shows that P-SHG is an effective tool for automatic quantitative analysis of structural defects of human corneas and should be applied to other collagen-rich tissues.

Quantitative assessment of muscle echogenicity in Charcot-Marie-Tooth disease type 1A by automatic thresholding methods

OBJECTIVE

To investigate the utility of automatic thresholding methods for quantitative muscle echogenicity assessment as a marker of disease severity in Charcot-Marie-Tooth disease type 1A (CMT1A).

METHODS

Muscle ultrasound was performed in 15 CMT1A patients and 7 healthy controls. Muscle echogenicity of six limb muscles in each subject was assessed by 16 automatic thresholding methods and conventional grey-scale analysis. Echogenicity of each method in CMT1A patients was compared with that in controls. A correlation between the echogenicity and CMT neuropathy score (CMTNS) was also analysed in CMT1A patients.

RESULTS

Significant differences in mean echogenicity of the 6 muscles between CMT1A patients and controls were found both in grey-scale analysis (p < 0.01) and 11 of the 16 automatic thresholding methods (p < 0.05 in each method). In CMT1A patients, mean echogenicity of the 6 muscles was positively correlated with CMTNS in 8 of the 16 automatic thresholding methods, but not in grey-scale analysis.

CONCLUSION

Automatic thresholding methods can be used to detect the difference in muscle echogenicity between CMT1A patients and controls. Echogenicity parameters correlate with the disease severity.

SIGNIFICANCE

Quantitative muscle echogenicity assessment by automatic thresholding methods shows potential as a surrogate marker of disease progression in CMT1A.

Microglia RAGE exacerbates the progression of neurodegeneration within the SOD1G93A murine model of amyotrophic lateral sclerosis in a sex-dependent manner

Background

Burgeoning evidence highlights seminal roles for microglia in the pathogenesis of neurodegenerative diseases including amyotrophic lateral sclerosis (ALS). The receptor for advanced glycation end products (RAGE) binds ligands relevant to ALS that accumulate in the diseased spinal cord and RAGE has been previously implicated in the progression of ALS pathology.

Methods

We generated a novel mouse model to temporally delete Ager from microglia in the murine SOD1^G93A model of ALS. Microglia Ager deficient SOD1^G93A mice and controls were examined for changes in survival, motor function, gliosis, motor neuron numbers, and transcriptomic analyses of lumbar spinal cord. Furthermore, we examined bulk-RNA-sequencing transcriptomic analyses of human ALS cervical spinal cord.

Results

Transcriptomic analysis of human cervical spinal cord reveals a range of AGER expression in ALS patients, which was negatively correlated with age at disease onset and death or tracheostomy. The degree of AGER expression related to differential expression of pathways involved in extracellular matrix, lipid metabolism, and intercellular communication. Microglia display increased RAGE immunoreactivity in the spinal cords of high AGER expressing patients and in the SOD1^G93A murine model of ALS vs. respective controls. We demonstrate that microglia Ager deletion at the age of symptomatic onset, day 90, in SOD1^G93A mice extends survival in male but not female mice. Critically, many of the pathways identified in human ALS patients that accompanied increased AGER expression were significantly ameliorated by microglia Ager deletion in male SOD1^G93A mice.

Conclusions

Our results indicate that microglia RAGE disrupts communications with cell types including astrocytes and neurons, intercellular communication pathways that divert microglia from a homeostatic to an inflammatory and tissue-injurious program. In totality, microglia RAGE contributes to the progression of SOD1^G93A murine pathology in male mice and may be relevant in human disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-021-02191-2.

RNA editing-mediated regulation of calcium-dependent activator protein for secretion (CAPS1) localization and its impact on synaptic transmission

Calcium-dependent activator protein for secretion 1 (CAPS1) is a SNARE accessory protein that facilitates formation of the SNARE complex to enable neurotransmitter release. Messenger RNAs encoding CAPS1 are subject to a site-specific adenosine-to-inosine (A-to-I) editing event resulting in a glutamate-to-glycine (E-to-G) substitution in the C-terminal domain of the encoded protein product. The C-terminal domain of CAPS1 is necessary for its synaptic enrichment and Cadps RNA editing has been shown previously to enhance the release of neuromodulatory transmitters. Using mutant mouse lines engineered to solely express CAPS1 protein isoforms encoded by either the non-edited or edited Cadps transcript, primary neuronal cultures from mouse hippocampus were used to explore the effect of Cadps editing on neurotransmission and CAPS1 synaptic localization at both glutamatergic and GABAergic synapses. While the editing of Cadps does not alter baseline evoked neurotransmission, it enhances short-term synaptic plasticity, specifically short-term depression, at inhibitory synapses. Cadps editing also alters spontaneous inhibitory neurotransmission. Neurons that solely express edited Cadps have a greater proportion of synapses that contain CAPS1 than neurons that solely express non-edited Cadps for both glutamatergic and GABAergic synapses. Editing of Cadps transcripts is regulated by neuronal activity, as global network stimulation increases the extent of transcripts edited in wild-type hippocampal neurons, whereas chronic network silencing decreases the level of Cadps editing. Taken together, these results provide key insights into the importance of Cadps editing in modulating its own synaptic localization, as well as the modulation of neurotransmission at inhibitory synapses in hippocampal neurons.

Calibration-Free Single-Molecule Absolute Quantification Using Super-resolution Microscopy

Single-molecule (SM) quantification has become a powerful analytical technique in the fields of physics, chemistry, and biology. SM imaging, especially with super-resolution (SR) techniques, has dramatically facilitated the study of individual molecules that may function as disease-related biomarkers. Although multiple properties can be used for quantitative imaging analysis, counting may be the simplest and most direct way. Consequently, how to utilize the greater spatial resolution to overcome undercounting or overcounting errors in certain conditions shows promising potential to unravel intracellular mechanisms of isolated biomolecules. From this perspective, we present an absolute quantification approach, termed crucial connected-component entropy (CCCE), with subresolution accuracy for the SR SM detection platform without the need for prior knowledge of calibration, and a cross-validation analytical pipeline based on SM profiling for nanoscale performance assessments. Considering its high efficiency, accuracy, and robustness for routine SM quantification compared with commonly used strategies, we believe that this protocol will indubitably find wide applications in biochemistry research, drug discovery, and clinical diagnostics, especially molecular diagnostics.

Cdc42 GTPase regulates ESCRTs in nuclear envelope sealing and ER remodeling

Small GTPases of the Rho family are binary molecular switches that regulate a variety of processes including cell migration and oriented cell divisions. Known Cdc42 effectors include proteins involved in cytoskeletal remodeling and kinase-dependent transcription induction, but none are involved in the maintenance of nuclear envelope integrity or ER morphology. Maintenance of nuclear envelope integrity requires the EndoSomal Complexes Required for Transport (ESCRT) proteins, but how they are regulated in this process remains unknown. Here, we show by live-cell imaging a novel Cdc42 localization with ESCRT proteins at sites of nuclear envelope and ER fission and, by genetic analysis of cdc42 mutant yeast, uncover a unique Cdc42 function in regulation of ESCRT proteins at the nuclear envelope and sites of ER tubule fission. Our findings implicate Cdc42 in nuclear envelope sealing and ER remodeling, where it regulates ESCRT disassembly to maintain nuclear envelope integrity and proper ER architecture.

Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells

Monitoring of adherent cells in culture is routinely performed in biological and clinical laboratories, and it is crucial for large-scale manufacturing of cells needed in cell-based clinical trials and therapies. However, the lack of reliable and easily implementable label-free techniques makes this task laborious and prone to human subjectivity. We present a deep-learning-based processing pipeline that locates and characterizes mesenchymal stem cell nuclei from a few bright-field images captured at various levels of defocus under collimated illumination. Our approach builds upon phase-from-defocus methods in the optics literature and is easily applicable without the need for special microscopy hardware, for example, phase contrast objectives, or explicit phase reconstruction methods that rely on potentially bias-inducing priors. Experiments show that this label-free method can produce accurate cell counts as well as nuclei shape statistics without the need for invasive staining or ultraviolet radiation. We also provide detailed information on how the deep-learning pipeline was designed, built and validated, making it straightforward to adapt our methodology to different types of cells. Finally, we discuss the limitations of our technique and potential future avenues for exploration.

Quantitative analysis of metal artefacts of dental implant in CBCT image by correlation analysis to micro-CT: A microstructural study

OBJECTIVES

Quantification of dental implant metal artefacts in CBCT images using correlation analysis of trabecular microstructural parameters from CBCT and micro-CT, and analysis of the effect of varying the angular position of the subject.

METHODS

Polyurethane synthetic bone blocks were first scanned without implants by micro-CT and CBCT. Two dental implants were then placed parallel in the bone blocks and these specimens were scanned by CBCT with different alpha angles. Three volumes of interest (VOI) were set for further analysis. Six microstructural parameters were measured: trabecular thickness (Tb_Th), trabecular spacing (Th_Sp), bone volume per total volume (BV/TV), bone surface per total volume (BS/TV), connectivity density (CD) andfractal dimension (FD). Micro-CT measurements were used as a gold standard for CBCT. Spearman correlation coefficients for each microstructural parameter from CBCT and micro-CT were calculated and compared using Steiger's Z test.

RESULTS

Without the implants, in VOI₁, the Spearman correlation coefficients of Tb_Th, Tb_Sp, BV/TV, BS/TV, CD and FD were 0.599, 0.76, 0.552, 0.566, 0.664 and 0.607, respectively. With the implants, the correlation coefficients decreased sharply in VOI₁. As the alpha angle increased from zero to 90°, the correlation coefficients increased and became significant. Similar results appeared in VOI₂. In contrast, in VOI₃, the correlation coefficient decreased as the alpha angle increased.

CONCLUSIONS

Metal artefacts were successfully quantified using microstructural parameters in terms of the image quality of the CBCT. Changes in alpha angle affected the quality of the CBCT image.

Detection and Classification of Bearing Surface Defects Based on Machine Vision

Surface defects on bearings can directly affect the service life and reduce the performance of equipment. At present, the detection of bearing surface defects is mostly done manually, which is labor-intensive and results in poor stability. To improve the inspection speed and the defect recognition rate, we proposed a bearing surface defect detection and classification method using machine vision technology. The method makes two main contributions. It proposes a local multi-neural network (Lc-MNN) image segmentation algorithm with the wavelet transform as the classification feature. The precision segmentation of the defect image is accomplished in three steps: wavelet feature extraction, Lc-MNN region division, and Lc-MNN classification. It also proposes a feature selection algorithm (SCV) that makes comprehensive use of scalar feature selection, correlation analysis, and vector feature selection to first remove similar features through correlation analysis, further screen the results with a scalar feature selection algorithm, and finally select the classification features using a feature vector selection algorithm. Using 600 test samples with three types of defect in the experiment, an identification rate of 99.5% was achieved without the need for large-scale calculation. The comparison tests indicated that the proposed method can achieve efficient feature selection and defect classification.

Using Sentinel-2 Images to Map the Populus euphratica Distribution Based on the Spectral Difference Acquired at the Key Phenological Stage

Populus euphratica is an important tree species in desert ecosystems. The protection and restoration of natural Populus euphratica forests requires accurate positioning information. The use of Sentinel-2 images to map the Populus euphratica distribution at a large scale faces challenges associated with discriminating between Populus euphratica and Tamarix chinensis. To address this problem, this study selected the Daliyabuyi Oasis in the hinterland of the Taklimakan Desert as the study site and sought to distinguish Populus euphratica from Tamarix chinensis. First, we determined the peak spectral difference period (optimal time window) between Populus euphratica and Tamarix chinensis within monthly Sentinel-2 time-series images. Then, an appropriate vegetation index was selected to represent the spectral difference between Populus euphratica and Tamarix chinensis within the key phenological stage. Finally, the maximum entropy method was used to automatically determine the threshold to map the Populus euphratica distribution. The results indicated that the period from 22 April to 1 May was the optimal time window for mapping the Populus euphratica distribution in the Daliyabuyi Oasis. The combination of the inverted red-edge chlorophyll index (IRECI) and the maximum entropy method can effectively distinguish Populus euphratica from Tamarix chinensis. The user’s accuracy of the Populus euphratica distribution extraction from single-data Sentinel-2 images acquired within the optimal time window was 0.83, the producer’s accuracy was 0.72, and the F1-score was 0.77. This study verified the feasibility of mapping Populus euphratica distribution based on Sentinel-2 images, and analyzed the validity of exploiting spectral differences within the key phenological stage from a single-data image to distinguish between the two species. The results can be used to extract the distribution of Populus euphratica and serve as an auxiliary variable for other plant classification methods, providing a reference for the extraction and classification of desert plants.

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.

Development of the automated software and device for determination of wicking in textiles using open-source tools

The development of automated software and the device for determination of wicking of textile materials, using open-source ImageJ libraries for image processing, and newly designed additional algorithm for the determination of threshold, is presented in this paper. The description of the device, design of the open-source software “Kapilarko”, as well as an explanation of the steps: image processing, threshold determination and reading of wicking height, are provided. We have also investigated the possibility of using the artificial neural networks for automatic recognition of the wicking height. The results showed that the recognition of the wet area of the sample, based on the application of artificial neural networks was in a very good agreement with the experimental data. The device's utility for the measurement of wicking ability of textile materials was proved by testing various knitted fabrics. The constructed device has the advantages of providing automated measurement and minimization of the subjective errors of the operators; extremely fast or long-term measurements; digital recording of results; consistency of experimental conditions; possibility of using water instead of colors and, last but not least, low cost of the device. Considering the importance and frequent measurements of wicking ability of textile materials, the advantages of the presented device, as well as the fact that commercial software without publishing the source-code, are used for most of the available devices, we believe that our idea to design the automated software and device by applying the "open-source" approach, will be of benefit to scientists and engineers in using or improving wicking experiments.

Alterations in corneal biomechanics underlie early stages of autoimmune-mediated dry eye disease

Autoimmune-mediated dry eye disease is a pathological feature of multiple disorders including Sjögren's syndrome, lupus and rheumatoid arthritis that has a life-long, detrimental impact on vision and overall quality of life. Although late stage disease outcomes such as epithelial barrier dysfunction, reduced corneal innervation and chronic inflammation have been well characterized in both human patients and mouse models, there is little to no understanding of early pathological processes. Moreover, the mechanisms underlying the loss of cornea homeostasis and disease progression are unknown. Here, we utilize the autoimmune regulatory (Aire)-deficient mouse model of autoimmune-mediated dry eye disease in combination with genome wide transcriptomics, high-resolution imaging and atomic force microscopy to reveal a potential extracellular matrix (ECM)-biomechanical-based mechanism driving cellular and morphological changes at early disease onset. Early disease in the Aire-deficient mouse model is associated with a mild reduction in tear production and moderate immune cell infiltration, allowing for interrogation of cellular, molecular and biomechanical changes largely independent of chronic inflammation. Using these tools, we demonstrate for the first time that the emergence of autoimmune-mediated dry eye disease is associated with an alteration in the biomechanical properties of the cornea. We reveal a dramatic disruption of the synthesis and organization of the extracellular matrix as well as degradation of the epithelial basement membrane during early disease. Notably, we provide evidence that the nerve supply to the cornea is severely reduced at early disease stages and that this is independent of basement membrane destruction or significant immune cell infiltration. Furthermore, diseased corneas display spatial heterogeneity in mechanical, structural and compositional changes, with the limbal compartment often exhibiting the opposite response compared to the central cornea. Despite these differences, however, epithelial hyperplasia is apparent in both compartments, possibly driven by increased activation of IL-1R1 and YAP signaling pathways. Thus, we reveal novel perturbations in corneal biomechanics, matrix organization and cell behavior during the early phase of dry eye that may underlie disease development and progression, presenting new potential targets for therapeutic intervention.

Development of integrated wetland change detection approach: In case of Erdos Larus Relictus National Nature Reserve, China

Wetlands are threatened by the global warming and the human exploitation pressure, and have been shrinking quickly in recent years. Timely and accurate wetland area change detection is the primary task for wetland conservation and restoration. The objective of this study is to develop an integrated change detection approach which integrates the advantages of spectral mixture analysis (SMA) and change vector analysis (CVA) for the change identification of wetland dynamics. In the proposed approach, water, vegetation and soil fractions of wetlands were derived by SMA; then, the detailed change information (including change magnitude and 12 change direction categories) were calculated through CVA. The proposed approach was applied for the wetlands change in Erdos Larus Relictus National Nature Reserve (ELRNNR), China, using time-series Landsat images during 1977-2017. We found that the wetland faced serious degradation, with water fraction changed to soil (5.79 km²), to vegetation (1.35 km²) and to both soil and vegetation (3.53 km²). From 1977 to 2000, a slight degradation occurred in the northeast edge of Bojiang Lake and a marginal degradation in Bojiang and Houjia Lakes inside the ELRNNR, with water fraction changed to soil and vegetation. During 2000-2010, severe degradation occurred in ELRNNR, and from 2010 to 2017, the wetland was more susceptible to the precipitation change and human activities. Analysis of the result indicated that the long-term drought and effects of mismanagement as well as misuse by human beings were the driving factors of wetland degradation. The proposed approach in this study achieves a higher accuracy than the classification approach to detect wetland change, with the ability to obtain more detailed change information.

Retinal and Choroidal Capillary Perfusion Are Reduced in Hypertensive Crisis Irrespective of Retinopathy

Purpose

Hypertensive crisis causes end-organ damage through small-vessel damage as described histologically. Noninvasive optical coherence tomography angiography (OCTA) makes it possible to image retinal and choroidal capillaries on a microscopic level in vivo. We quantified eye vessel perfusion changes in hypertensive crisis using OCTA.

Methods

Patients with hypertensive crisis (systolic blood pressure ≥180 mm Hg and/or diastolic blood pressure ≥110 mm Hg) and age-matched healthy controls were included in the study. OCTA en face 3 × 3-mm images of the superficial and deep retinal layers and the choriocapillaris were acquired. Outcome parameters included vessel density (VD) and vessel skeleton density (VSD) of the superficial and deep retinal layers, as well as flow voids of the choriocapillaris.

Results

Twenty-eight eyes of 17 patients and 31 age-matched control eyes of 18 healthy subjects were included. VD and VSD of the deep retinal layer were significantly reduced in hypertensive crisis (P ≤ 0.004). Choriocapillaris signal intensity was more heterogeneous in patients, and flow voids exhibited confluence with a larger average area and a lower absolute count (P ≤ 0.045). These changes were independent of time since onset of hypertensive crisis and of the presence and extent of retinopathy. Deep retinal changes were associated with renal end-organ failure (P = 0.045).

Conclusions

Hypertensive crisis is associated with a significant reduction in retinal and choroidal capillary perfusion based on OCTA findings. These alterations are independent of retinopathy and related to end-organ damage.

Translational Relevance

OCTA might help distinguish hypertensive urgency from hypertensive emergency earlier than currently possible.

Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relationship among the organisms to optimize three objective functions: Otsu's between class variance and Kapur's and Tsallis entropy for image segmentation. The performance of the SOS based image segmentation algorithm has been evaluated using a set of benchmark images and has been compared with four recent meta-heuristic algorithms. The algorithms are compared in terms of effectiveness and consistency. The quality of the algorithms has been estimated by some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and, feature similarity index (FSIM). The experimental results of the algorithms reveal that the balance of intensification and diversification of the SOS algorithm to achieve the global optima is better than others.

Automated thresholding algorithms outperform manual thresholding in macular optical coherence tomography angiography image analysis

Introduction

For quantification of Optical Coherence Tomography Angiography (OCTA) images, Vessel Density (VD) and Vessel Skeleton Density (VSD) are well established parameters and different algorithms are in use for their calculation. However, comparability, reliability and ability to discriminate healthy and impaired macular perfusion of different algorithms are unclear, yet, of potential high clinical relevance. Hence, we assessed comparability and test-retest reliability of the most common approaches.

Materials and methods

Two consecutive 3×3mm OCTA en face images of the superficial and deep retinal layer were acquired with swept-source OCTA. VD and VSD were calculated with manual thresholding and six automated thresholding algorithms (Huang, Li, Otsu, Moments, Mean, Percentile) using ImageJ and compared in terms of intra-class correlation coefficients, measurement differences and repeatability coefficients. Receiver operating characteristic analyses (healthy vs. macular pathology) were performed and Area Under the Curve (AUC) values were calculated.

Results

Twenty-six eyes (8 female, mean age: 47 years) of 15 patients were included (thereof 15 eyes with macular pathology). Binarization thresholds, VD and VSD differed significantly between the algorithms and compared to manual thresholding (p < 0.0001). Inter-measurement differences did not differ significantly between patients with healthy versus pathologic maculae (p ≥ 0.685). Reproducibility was higher for the automated algorithms compared to manual thresholding on all measures of reproducibility assessed. AUC was significantly higher for the Mean algorithm compared to the manual approach with respect to the superficial retinal layer.

Conclusions

Automated thresholding algorithms yield a higher reproducibility of OCTA parameters and allow for a more sensitive diagnosis of macular pathology. However, different algorithms are not interchangeable nor results readily comparable. Especially the Mean algorithm should be investigated in further detail. Automated thresholding algorithms are preferable but more standardization is needed for clinical use.

A negative-feedback loop maintains optimal chemokine concentrations for directional cell migration

Chemoattractant gradients frequently guide migrating cells. To achieve the most directional signal, such gradients should be maintained with concentrations around the dissociation constant (Kd)1-6 of the chemoreceptor. Whether this actually occurs in animals is unknown. Here we investigate whether a moving tissue, the zebrafish posterior lateral line primordium, buffers its attractant in this concentration range to achieve robust migration. We find that the Cxcl12 (also known as Sdf1) attractant gradient ranges from 0 to 12 nM, values similar to the 3.4 nM Kd of its receptor Cxcr4. When we increase the Kd of Cxcl12 for Cxcr4, primordium migration is less directional. Furthermore, a negative-feedback loop between Cxcl12 and its clearance receptor Ackr3 (also known as Cxcr7) regulates the Cxcl12 concentrations. Breaking this negative feedback by blocking the phosphorylation of the cytoplasmic tail of Ackr3 also results in less directional primordium migration. Thus, directed migration of the primordium is dependent on a close match between the Cxcl12 concentration and the Kd of Cxcl12 for Cxcr4, which is maintained by buffering of the chemokine levels. Quantitative modelling confirms the plausibility of this mechanism. We anticipate that buffering of attractant concentration is a general mechanism for ensuring robust cell migration.

Human endothelial cells size-select their secretory granules for exocytosis to modulate their functional output

BACKGROUND

The secretory granules of endothelial cells, Weibel-Palade bodies, are released in response to numerous extracellular signals. Their cargo is critical to many vascular functions including hemostasis and inflammation. This presents a fundamental problem: how can these cells initiate tailor-made responses from the release of a single type of organelle, each with similar cargo? Each cell contains Weibel-Palade bodies in a wide range of sizes, and we have shown that experimentally shortening these organelles disproportionately reduces their ability to initiate hemostasis in vitro, leaving leukocyte recruitment unaffected. Could the production of this range of sizes underpin differential responses?

OBJECTIVES

To determine whether different agonists drive the exocytosis of different sizes of Weibel-Palade bodies.

METHODS

We used a high-throughput automated unbiased imaging workflow to analyze the sizes of Weibel-Palade bodies within human umbilical vein endothelial cells (HUVECs) before and after agonist activation to determine changes in organelle size distributions.

RESULTS

We found that a subset of agonists differentially evoke the release of the longest, most pro-hemostatic organelles. Inhibiting the release of these longest organelles by just 15% gives a fall of 60% in an assay of secreted von Willebrand factor (vWF) function.

CONCLUSIONS

The size-selection of granules for exocytosis represents a novel layer of control, allowing endothelial cells to provide diverse responses to different signals via the release of a single type of organelle.

Transforming Growth Factor-beta signaling in αβ thymocytes promotes negative selection

In the thymus, the T lymphocyte repertoire is purged of a substantial portion of highly self-reactive cells. This negative selection process relies on the strength of TCR-signaling in response to self-peptide-MHC complexes, both in the cortex and medulla regions. However, whether cytokine-signaling contributes to negative selection remains unclear. Here, we report that, in the absence of Transforming Growth Factor beta (TGF-β) signaling in thymocytes, negative selection is significantly impaired. Highly autoreactive thymocytes first escape cortical negative selection and acquire a Th1-like-phenotype. They express high levels of CXCR3, aberrantly accumulate at the cortico-medullary junction and subsequently fail to sustain AIRE expression in the medulla, escaping medullary negative selection. Highly autoreactive thymocytes undergo an atypical maturation program, substantially accumulate in the periphery and induce multiple organ-autoimmune-lesions. Thus, these findings reveal TGF-β in thymocytes as crucial for negative selection with implications for understanding T cell self-tolerance mechanisms.

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

This paper presents a novel method for the automatic segmentation of coronary arteries in X-ray angiograms, based on multiscale analysis and neural networks. The multiscale analysis is performed by using Gaussian filters in the spatial domain and Gabor filters in the frequency domain, which are used as inputs by a multilayer perceptron (MLP) for the enhancement of vessel-like structures. The optimal design of the MLP is selected following a statistical comparative analysis, using a training set of 100 angiograms, and the area under the ROC curve ( A z ) for assessment of the detection performance. The detection results of the proposed method are compared with eleven state-of-the-art blood vessel enhancement methods, obtaining the highest performance of A z = 0.9775 , with a test set of 30 angiograms. The database of 130 X-ray coronary angiograms has been outlined by a specialist and approved by a medical ethics committee. On the other hand, the vessel extraction technique was selected from fourteen binary classification algorithms applied to the multiscale filter response. Finally, the proposed segmentation method is compared with twelve state-of-the-art vessel segmentation methods in terms of six binary evaluation metrics, where the proposed method provided the most accurate coronary arteries segmentation with a classification rate of 0.9698 and Dice coefficient of 0.6857 , using the test set of angiograms. In addition to the experimental results, the performance in the detection and segmentation steps of the proposed method have also shown that it can be highly suitable for systems that perform computer-aided diagnosis in X-ray imaging.

A fast DFT method for generally k sparse signals recovery

In this paper, a new method to fast compute DFT of generally sparse signals is presented. Firstly, the original signal is downsampled with different time shifts, and the discrete Fourier Transforms (DFTs) of downsampled signals are calculated by FFT. Then the DFTs are used to determine and measure the K non-zero (significant) freq. grids by combining the moment preserving problem with the BigBand method. The proposed method is hardware-friendly, and simulation results show that the proposed method has better recovery performance compared with other methods.

FcRn mediates fast recycling of endocytosed albumin and IgG from early macropinosomes in primary macrophages

The neonatal Fc receptor (FcRn) rescues albumin and IgG from degradation following endocytosis and thereby extends the half-life of these plasma proteins. However, the pathways for the uptake of these soluble FcRn ligands, and the recycling itinerary of the FcRn-ligand complexes, have not been identified in primary cells. Here, we have defined the recycling of human albumin and IgG in primary mouse macrophages selectively expressing the human FcRn. Albumin is internalised by macropinocytosis; in the absence of FcRn, internalised albumin is rapidly degraded, while in the presence of FcRn albumin colocalises to SNX5-positive membrane domains and is partitioned into tubules emanating from early macropinosomes for delivery in transport carriers to the plasma membrane. Soluble monomeric IgG was also internalised by macropinocytosis and rapidly recycled by the same pathway. In contrast, the fate of IgG bound to surface Fcγ receptors differed from monomeric IgG endocytosed by macropinocytosis. Overall, our findings identify a rapid recycling pathway for FcRn ligands from early macropinosomes to the cell surface of primary cells.

A simple morphometric analysis method for dermal microstructure using color thresholding and moments

BACKGROUND

Proper assessment of dermal collagen fibers by dermatologists and researchers is essential. Histologic evaluation methods have limitations. We present a simple method for measurement of collagen fibers in human skin using Masson's trichrome staining.

MATERIALS AND METHODS

Normal skin specimens from a cadaver were processed with Masson's trichrome, which can effectively stain collagen fibers blue with aniline dye. Optical photomicrographs of these slides were analyzed using ImageJ software. Color image processing, a histogram-based function of ImageJ for image segmentation, was performed with color moments thresholding technique. We selected blue areas by adjusting the blue channel to include specific values. The selected areas were highlighted and evaluated. We divided the image into layers of 0.09-mm² areas from the top to bottom of the dermis. Each area was cropped and evaluated.

RESULTS

Quantitative assessment yielded the quantitative size occupied by collagen fibers in an area of 0.09 mm² . Calculation of the percentage in each area can be used to determine the density of collagen fibers.

CONCLUSION

Measurements obtained with our method can be applied to research on dermal collagen fibers. We present a convenient quantitative assessment method for the dermal constituents in Masson's trichrome-stained slides.

Quantifying Morphology and Diffusion Properties of Mesoporous Carbon From High-Fidelity 3D Reconstructions

A reliable quantitative analysis in electron tomography, which depends on the segmentation of the three-dimensional reconstruction, is challenging because of constraints during tilt-series acquisition (missing wedge) and reconstruction artifacts introduced by reconstruction algorithms such as the Simultaneous Iterative Reconstruction Technique (SIRT) and Discrete Algebraic Reconstruction Technique (DART). We have carefully evaluated the fidelity of segmented reconstructions analyzing a disordered mesoporous carbon used as support in catalysis. Using experimental scanning transmission electron microscopy (STEM) tomography data as well as realistic phantoms, we have quantitatively analyzed the effect on the morphological description as well as on diffusion properties (based on a random-walk particle-tracking simulation) to understand the role of porosity in catalysis. The morphological description of the pore structure can be obtained reliably both using SIRT and DART reconstructions even in the presence of a limited missing wedge. However, the measured pore volume is sensitive to the threshold settings, which are difficult to define globally for SIRT reconstructions. This leads to noticeable variations of the diffusion coefficients in the case of SIRT reconstructions, whereas DART reconstructions resulted in more reliable data. In addition, the anisotropy of the determined diffusion properties was evaluated, which was significant in the presence of a limited missing wedge for SIRT and strongly reduced for DART.

ROCK Inhibitor-Induced Promotion of Retinal Pigment Epithelial Cell Motility during Wound Healing

Purpose

No standard therapy for RPE tear, a complication of neovascular age-related macular degeneration, exists even though RPE tears cause severe vision loss, and promotion of cell proliferation and/or migration could be a candidate RPE tear therapy. The aim of this study is to evaluate the effect of Rho-associated coiled-coil containing kinase (ROCK) inhibitor Y27632 on retinal pigment epithelial (RPE) cell motility during wound healing.

Methods

Human RPE cells were cultured in media with and without 10 μM Y27632. A luminescent cell viability assay and vinculin immunocytochemistry were used to test the Y27632 effect on RPE cell adhesion. The mean size of vinculin puncta was quantified from immunofluorescence images. RPE cell motility during wound healing was evaluated using time-lapse imaging and measuring cell migration distances and cell coverage rate in wound fields.

Results

The number of adhered RPE and mean size of vinculin puncta were, respectively, 20519 cells and 3.65 μm² under nontreatment and 23569 cells and 0.66 μm² under Y27632 treatment. Cell migration distance and cell coverage percentage for untreated and Y27632-treated cells were 98.9 and 59.4% and 203.4 and 92.5%, respectively.

Conclusions

Inhibition of ROCK signaling by using 10 μM Y27632 promoted RPE cell motility during wound healing by reducing RPE cell adhesion strength.

Nuclear pore protein TPR associates with lamin B1 and affects nuclear lamina organization and nuclear pore distribution

The organization of the nuclear periphery is crucial for many nuclear functions. Nuclear lamins form dense network at the nuclear periphery and play a substantial role in chromatin organization, transcription regulation and in organization of nuclear pore complexes (NPCs). Here, we show that TPR, the protein located preferentially within the nuclear baskets of NPCs, associates with lamin B1. The depletion of TPR affects the organization of lamin B1 but not lamin A/C within the nuclear lamina as shown by stimulated emission depletion microscopy. Finally, reduction of TPR affects the distribution of NPCs within the nuclear envelope and the effect can be reversed by simultaneous knock-down of lamin A/C or the overexpression of lamin B1. Our work suggests a novel role for the TPR at the nuclear periphery: the TPR contributes to the organization of the nuclear lamina and in cooperation with lamins guards the interphase assembly of nuclear pore complexes.

Unsupervised quantification of tissue immunofluorescence in animal models of multiple sclerosis - Instructions for use

BACKGROUND

In the analysis of animal models of CNS diseases such as experimental autoimmune encephalomyelitis (EAE), immunostaining and histopathology are important readouts. However, the complex morphological features of a tissue staining are often reduced to a single measure which relies on tedious manual planimetry. Furthermore, the measure itself and co-variables such as the region being analysed are chosen in a human decision-making process, which introduces bias.

NEW METHOD

First aim of the present study is to provide an open-source workflow for the high-throughput, unsupervised quantification of different stainings in the spinal cord. We evaluate different EAE models, spinal cord regions and different time points of disease. By applying random forest classification, we compare different measures.

RESULTS

Exemplified for glial reactivity, we show that measures and variables interact and that their values are non-normally distributed, hampering the common use of parametric tests. Furthermore, we demonstrate that one-dimensional measures are insufficient descriptors for immunofluorescence data in EAE and thus need to be considered as partly invalid.

COMPARISON WITH EXISTING METHODS

We show in a systematic analysis of EAE studies that currently published immunohistological outcomes are highly incompatible regarding methodology and statistics. Furthermore, they lack the report of important information necessary for reproducibility and do not use unsupervised automatic analysis.

CONCLUSIONS

Our results discover relevant caveats in the currently used methods of immunofluorescence analysis. The provided step-by-step instructions and open-source code are intended to serve as a framework for sensitive, unbiased immunofluorescence analysis of tissue sections in translational research.

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

Fractal conceptualization of intumescent fire barriers, toward simulations of virtual morphologies

By limiting the heat spread during a fire hazard, intumescent coatings are important components of passive protection systems. They swell due to heat induced reactions of micro constituents and are transformed into carbonaceous porous-like media, known as intumescent chars. Their multiscale inner structures, key elements of performance, are costly to predict by recurrent and large scale fire testing while numerical simulations are challenging due to complex kinetics. Hence, we propose a novel approach using the fractal theory and the random nature of events to conceptualize the coating expansion. Experimental specimens were obtained from fire protective coatings exposed to bench scale hydrocarbon fire. Mass fractals were evidenced in the slices of 3D sample volumes reconstructed from X-ray microtomography. Consequently, geometrical building blocks were simulated by random walk, active walk, aggregation-like and site percolation: physical-chemical modes of action were inherent in the attribution of the randomness. It is a first demonstration to conceptualize different types of intumescent actions by a generalized approach with dimensionless parameters at multiscale, thus eliminating the simulation of complex kinetics to obtain a realistic morphology. Also, fractal results brought new evidence to former chemical analyses on fire test residues trying to explain the kinetics of expansion. Expected outcomes are to predict virtually the reaction of fire protective systems hence to speed-up the assessment of fire performance through computed properties of virtual volumes.

Improving the prediction of the trabecular bone microarchitectural parameters using dental cone-beam computed tomography

Background

In this study, we explored how various preprocessing approaches can be employed to enhance the capability of dental CBCT to accurately estimate trabecular bone microarchitectural parameters.

Methods

In total, 30 bovine vertebrae cancellous bone specimens were used for in study. Voxel resolution 18-μm micro-computed tomography (micro-CT) and 100-μm dental CBCT were used to scan each specimen. Micro-CT images were used to calculate trabecular bone microarchitectural parameters; the results were set as the gold standard. Subsequently, before the dental CBCT images were converted into binary images to calculate trabecular bone microarchitectural parameters, three preprocessing approaches were used to process the dental CBCT images. For Group 1, no preprocessing approach was applied. For Group 2, images were sharpened and despeckable noises were removed. For Group 3, the function of local thresholding was added to Group 2 to form Group 3. For Group 4, the air pixels was removed from Group 3 to form Group 4. Subsequently, all images were imported into a software package to estimate trabecular bone microarchitectural parameters (bone volume fraction (BV/TV), trabecular thickness (TbTh), trabecular number (TbN), and trabecular separation (TbSp)). Finally, a paired t-test and a Pearson correlation test were performed to compare the capability of micro-CT with the capability of dental CBCT for estimating trabecular bone microarchitectural parameters.

Results

Regardless of whether dental CBCT images underwent image preprocessing (Groups 1 to 4), the four trabecular bone microarchitectural parameters measured using dental CBCT images were significantly different from those measured using micro-CT images. However, after three image preprocessing approaches were applied to the dental CBCT images (Group 4), the BV/TV obtained using dental CBCT was highly positively correlated with that obtained using micro-CT (r = 0.87, p < 0.001); the correlation coefficient was greater than that of Group 1 (r = −0.15, p = 0.412), Group 2 (r = 0.16, p = 0.386), and Group 3 (r = 0.47, p = 0.006). After dental CBCT images underwent image preprocessing, the efficacy of using dental CBCT for estimating TbN and TbSp was enhanced.

Conclusions

Image preprocessing approaches can be used to enhance the efficacy of using dental CBCT for predicting trabecular bone microarchitectural parameters.

Brain abnormality detection using template matching

Magnetic resonance imaging (MRI) is a widely used imaging modality to evaluate brain disorders. MRI generates huge volumes of data, which consist of a sequence of scans taken at different instances of time. As the presence of brain disorders has to be evaluated on all magnetic resonance (MR) sequences, manual brain disorder detection becomes a tedious process and is prone to inter- and intra-rater errors. A technique for detecting abnormalities in brain MRI using template matching is proposed. Bias filed correction is performed on volumetric scans using N4ITK filter, followed by volumetric registration. Normalized cross-correlation template matching is used for image registration taking into account, the rotation and scaling operations. A template of abnormality is selected which is then matched in the volumetric scans, if found, the corresponding image is retrieved. Post-processing of the retrieved images is performed by the thresholding operation; the coordinates and area of the abnormality are reported. The experiments are carried out on the glioma dataset obtained from Brain Tumor Segmentation Challenge 2013 database (BRATS 2013). Glioma dataset consisted of MR scans of 30 real glioma patients and 50 simulated glioma patients. NVIDIA Compute Unified Device Architecture framework is employed in this paper, and it is found that the detection speed using graphics processing unit is almost four times faster than using only central processing unit. The average Dice and Jaccard coefficients for a wide range of trials are found to be 0.91 and 0.83, respectively.

Cellular Uptake and Intracellular Trafficking of Poly( N-(2-Hydroxypropyl) Methacrylamide)

Cellular uptake and intracellular trafficking of polymer conjugates or polymer nanoparticles is typically monitored using fluorescence-based techniques such as confocal microscopy. While these methods have provided a wealth of insight into the internalization and trafficking of polymers and polymer nanoparticles, they require fluorescent labeling of the polymer or polymer nanoparticle. Because in biological media fluorescent dyes may degrade, be cleaved from the polymer or particle, or even change uptake and trafficking pathways, there is an interest in fluorescent label-free methods to study the interactions between cells and polymer nanomedicines. This article presents a first proof-of-concept that demonstrates the feasibility of NanoSIMS to monitor the intracellular localization of polymer conjugates. For the experiments reported here, poly( N-(2-hydroxypropyl) methacrylamide)) (PHPMA) was selected as a prototypical polymer-drug conjugate. This PHPMA polymer contained a ¹⁹F-label at the α-terminus, which was introduced in order to allow NanoSIMS analysis. Prior to the NanoSIMS experiments, the uptake and intracellular trafficking of the polymer was established using confocal microscopy and flow cytometry. These experiments not only provided detailed insight into the kinetics of these processes but were also important to select time points for the NanoSIMS analysis. For the NanoSIMS experiments, HeLa cells were investigated that had been exposed to the PHPMA polymer for a period of 4 or 15 h, which was known to lead to predominant lysosomal accumulation of the polymer. NanoSIMS analysis of resin-embedded and microtomed samples of the cells revealed a punctuated fluorine signal, which was found to colocalize with the sulfur signal that was attributed to the lysosomal compartments. The localization of the polymer in the endolysosomal compartments was confirmed by TEM analysis on the same cell samples. The results of this study illustrate the potential of NanoSIMS to study the uptake and intracellular trafficking of polymer nanomedicines.