Photoswitchable Inhibitors of Microtubule Dynamics Optically Control Mitosis and Cell Death.

Small molecules that interfere with microtubule dynamics, such as Taxol and the Vinca alkaloids, are widely used in cell biology research and as clinical anticancer drugs. However, their activity cannot be restricted to specific target cells, which also causes severe side effects in chemotherapy. Here, we introduce the photostatins, inhibitors that can be switched on and off in vivo by visible light, to optically control microtubule dynamics. Photostatins modulate microtubule dynamics with a subsecond response time and control mitosis in living organisms with single-cell spatial precision. In longer-term applications in cell culture, photostatins are up to 250 times more cytotoxic when switched on with blue light than when kept in the dark. Therefore, photostatins are both valuable tools for cell biology, and are promising as a new class of precision chemotherapeutics whose toxicity may be spatiotemporally constrained using light.

Graph-based description of tertiary lymphoid organs at single-cell level.

Our aim is to complement observer-dependent approaches of immune cell evaluation in microscopy images with reproducible measures for spatial composition of lymphocytic infiltrates. Analyzing such patterns of inflammation is becoming increasingly important for therapeutic decisions, for example in transplantation medicine or cancer immunology. We developed a graph-based assessment of lymphocyte clustering in full whole slide images. Based on cell coordinates detected in the full image, a Delaunay triangulation and distance criteria are used to build neighborhood graphs. The composition of nodes and edges are used for classification, e.g. using a support vector machine. We describe the variability of these infiltrates on CD3/CD20 duplex staining in renal biopsies of long-term functioning allografts, in breast cancer cases, and in lung tissue of cystic fibrosis patients. The assessment includes automated cell detection, identification of regions of interest, and classification of lymphocytic clusters according to their degree of organization. We propose a neighborhood feature which considers the occurrence of edges with a certain type in the graph to distinguish between phenotypically different immune infiltrates. Our work addresses a medical need and provides a scalable framework that can be easily adjusted to the requirements of different research questions.

Estimating hemodynamic shear stress in murine peripheral collateral arteries by two-photon line scanning.

Changes in wall shear stress of blood vessels are assumed to be an important component of many physiological and pathophysiological processes. However, due to technical limitations experimental in vivo data are rarely available. Here, we investigated two-photon excitation fluorescence microscopy as an option to measure vessel diameter as well as blood flow velocities in a murine hindlimb model of arteriogenesis (collateral artery growth). Using line scanning at high frequencies, we measured the movement of blood cells along the vessel axis. We found that peak systolic blood flow velocity averaged 9 mm/s and vessel diameter 42 µm in resting collaterals. Induction of arteriogenesis by femoral artery ligation resulted in a significant increase in centerline peak systolic velocity after 1 day with an average of 51 mm/s, whereas the averaged luminal diameter of collaterals (52 µm) changed much less. Thereof calculations revealed a significant fourfold increase in hemodynamic wall shear rate. Our results indicate that two-photon line scanning is a suitable tool to estimate wall shear stress e.g., in experimental animal models, such as of arteriogenesis, which may not only help to understand the relevance of mechanical forces in vivo, but also to adjust wall shear stress in ex vivo investigations on isolated vessels as well as cell culture experiments.

Aged neutrophils contribute to the first line of defense in the acute inflammatory response.

Under steady-state conditions, aged neutrophils are removed from the circulation in bone marrow, liver, and spleen, thereby maintaining myeloid cell homeostasis. The fate of these aged immune cells under inflammatory conditions, however, remains largely obscure. Here, we demonstrate that in the acute inflammatory response during endotoxemia, aged neutrophils cease returning to the bone marrow and instead rapidly migrate to the site of inflammation. Having arrived in inflamed tissue, aged neutrophils were found to exhibit a higher phagocytic activity as compared with the subsequently recruited nonaged neutrophils. This distinct behavior of aged neutrophils under inflammatory conditions is dependent on specific age-related changes in their molecular repertoire that enable these "experienced" immune cells to instantly translate inflammatory signals into immune responses. In particular, aged neutrophils engage Toll-like receptor-4- and p38 MAPK-dependent pathways to induce conformational changes in ß2 integrins that allow these phagocytes to effectively accomplish their mission in the front line of the inflammatory response. Hence, ageing in the circulation might represent a critical process for neutrophils that enables these immune cells to properly unfold their functional properties for host defense.

New View on Endothelial Cell Migration: Switching Modes of Migration Based on Matrix Composition.

OBJECTIVE: Cell-matrix interactions are crucial for regulating cellular activities, such as migration. This is of special importance for morphogenic processes, such as angiogenesis (the development of new blood vessels). Most of our understanding of cell migration relies on 2-dimensional (2D) experiments. However, the awareness that 3D settings might elicit different results has increased. Knowledge about endothelial cell (EC) behavior in 3D environments and the influence of matrix composition on EC migration, in particular, is still limited. APPROACH AND RESULTS: We characterize the migration of single ECs through 2 structurally different hydrogels: spongy Matrigel and fibrillar collagen I. Our observations reveal an elongated migration phenotype in Matrigel and a rounded phenotype with pronounced cell blebs (blebs >2 µm) in collagen I, which have not previously been described in ECs. Directed migration seems to depend on Rac1 and Cdc42 in collagen, but not in Matrigel (shown using appropriate pharmacological inhibitors). By applying anti-integrin antibodies and supplementing laminin in collagen gels, we identify laminin as the main determinant of the elongated phenotype. Laminin seems to induce a morphological switch between modes of migration. As an in situ proof of principle, we performed live imaging of EC migration during vascular growth in a murine retina in the absence and presence of anti-integrin antibodies. CONCLUSIONS: We show that, surprisingly, ECs can evade the pharmacological inhibition of central signaling pathways involved in migration (contractility, small GTPases, and proteolysis) by shifting gears between modes of migration. This finding indicates an unexpected contextual plasticity of EC behavior.

Intercellular Transport of Nanomaterials is Mediated by Membrane Nanotubes In Vivo.

So-called membrane nanotubes are cellular protrusions between cells whose functions include cell communication, environmental sampling, and protein transfer. It has been previously reported that systemically administered carboxyl-modified quantum dots (cQDs) are rapidly taken up by perivascular macrophages in skeletal muscle of healthy mice. Expanding these studies, it is found, by means of in vivo fluorescence microscopy on the mouse cremaster muscle, rapid uptake of cQDs not only by perivascular macrophages but also by tissue-resident cells, which are localized more than 100 µm distant from the closest vessel. Confocal microscopy on muscle tissue, immunostained for the membrane dye DiI, reveals the presence of continuous membranous structures between MHC-II-positive, F4/80-positive cells. These structures contain microtubules, components of the cytoskeleton, which clearly colocalize with cQDs. The cQDs are exclusively found inside endosomal vesicles. Most importantly, by using in vivo fluorescence microscopy, this study detected fast (0.8 µm s(-1) , mean velocity), bidirectional movement of cQDs in such structures, indicating transport of cQD-containing vesicles along microtubule tracks by the action of molecular motors. The findings are the first to demonstrate membrane nanotube function in vivo and they suggest a previously unknown route for the distribution of nanomaterials in tissue.

Influence of Surface Modifications on the Spatiotemporal Microdistribution of Quantum Dots In Vivo.

For biomedical applications of nanoconstructs, it is a general prerequisite to efficiently reach the desired target site. In this regard, it is crucial to determine the spatiotemporal distribution of nanomaterials at the microscopic tissue level. Therefore, the effect of different surface modifications on the distribution of microinjected quantum dots (QDs) in mouse skeletal muscle tissue has been investigated. In vivo real-time fluorescence microscopy and particle tracking reveal that carboxyl QDs preferentially attach to components of the extracellular matrix (ECM), whereas QDs coated with polyethylene glycol (PEG) show little interaction with tissue constituents. Transmission electron microscopy elucidates that carboxyl QDs adhere to collagen fibers as well as basement membranes, a type of ECM located on the basolateral side of blood vessel walls. Moreover, carboxyl QDs have been found in endothelial junctions as well as in caveolae of endothelial cells, enabling them to translocate into the vessel lumen. The in vivo QD distribution is confirmed by in vitro experiments. The data suggest that ECM components act as a selective barrier depending on QD surface modification. For future biomedical applications, such as targeting of blood vessel walls, the findings of this study offer design criteria for nanoconstructs that meet the requirements of the respective application.

Derivation of a respiration trigger signal in small animal list-mode PET based on respiration-induced variations of the ECG signal.

BACKGROUND: Raw PET list-mode data contains motion artifacts causing image blurring and decreased spatial resolution. Unless corrected, this leads to underestimation of the tracer uptake and overestimation of the lesion size, as well as inaccuracies with regard to left ventricular volume and ejection fraction (LVEF), especially in small animal imaging. METHODS AND RESULTS: A respiratory trigger signal from respiration-induced variations in the electro-cardiogram (ECG) was detected. Original and revised list-mode PET data were used for calculation of left ventricular function parameters using both respiratory gating techniques. For adequately triggered datasets we saw no difference in mean respiratory cycle period between the reference standard (RRS) and the ECG-based (ERS) methods (1120 ± 159 ms vs 1120 ± 159 ms; P = n.s.). While the ECG-based method showed somewhat higher signal noise (66 ± 22 ms vs 51 ± 29 ms; P < .001), both respiratory triggering techniques yielded similar estimates for EDV, ESV, LVEF (RRS: 387 ± 56 µL, 162 ± 34 µL, 59 ± 5%; ERS: 389 ± 59 µL, 163 ± 35 µL, 59 ± 4%; P = n.s.). CONCLUSIONS: This study showed that respiratory gating signals can be accurately derived from cardiac trigger information alone, without the additional requirement for dedicated measurement of the respiratory motion in rats.

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images.

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification. The second system is free from assumptions related to TC scoring and directly learns patient stratification from the overall survival time and event information. Both systems are built on a novel unpaired domain adaptation deep learning solution for epithelial region segmentation. This approach significantly reduces the need for large pixel-precise manually annotated datasets while superseding serial sectioning or re-staining of slides to obtain ground truth by cytokeratin staining. The capacity of the first system to replicate the TC scoring by pathologists is evaluated on 703 unseen cases, with an addition of 97 cases from an independent cohort. Our results show Lin's concordance values of 0.93 and 0.96 against pathologist scoring, respectively. The ability of the first and second system to stratify anti-PD-L1 treated patients is evaluated on 151 clinical samples. Both systems show similar stratification powers (first system: HR = 0.539, p = 0.004 and second system: HR = 0.525, p = 0.003) compared to TC scoring by pathologists (HR = 0.574, p = 0.01).

Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.

The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cancer-specific death from digital images of immunohistochemically (IHC) stained tissue microarrays (TMAs). Specifically, we evaluated a cohort of 248 gastric cancer patients using convolutional neural networks (CNNs) in an end-to-end weakly supervised scheme independent of subjective pathologist input. To account for the time-to-event characteristic of the outcome data, we developed new survival models to guide the network training. In addition to the standard H&E staining, we investigated the prognostic value of a panel of immune cell markers (CD8, CD20, CD68) and a proliferation marker (Ki67). Our CNN-derived risk scores provided additional prognostic value when compared to the gold standard prognostic tool TNM stage. The CNN-derived risk scores were also shown to be superior when systematically compared to cell density measurements or a CNN score derived from binary 5-year survival classification, which ignores time-to-event. To better understand the underlying biological mechanisms, we qualitatively investigated risk heat maps for each marker which visualised the network output. We identified patterns of biological interest that were related to low risk of cancer-specific death such as the presence of B-cell predominated clusters and Ki67 positive sub-regions and showed that the corresponding risk scores had prognostic value in multivariate Cox regression analyses (Ki67&CD20 risks: hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.15-1.89, p = 0.002; CD20&CD68 risks: HR = 1.33, 95% CI = 1.07-1.67, p = 0.009). Our study demonstrates the potential additional value that deep learning in combination with a panel of IHC markers can bring to the field of precision oncology.

Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma.

In the context of precision medicine with immunotherapies there is an increasing need for companion diagnostic tests to identify potential therapy responders and avoid treatment coming along with severe adverse events for non-responders. Here, we present a retrospective case study to discover image-based signatures for developing a potential companion diagnostic test for ipilimumab (IPI) in malignant melanoma. Signature discovery is based on digital pathology and fully automatic quantitative image analysis using virtual multiplexing as well as machine learning and deep learning on whole-slide images. We systematically correlated the patient outcome data with potentially relevant local image features using a Tissue Phenomics approach with a sound cross validation procedure for reliable performance evaluation. Besides uni-variate models we also studied combinations of signatures in several multi-variate models. The most robust and best performing model was a decision tree model based on relative densities of CD8+ tumor infiltrating lymphocytes in the intra-tumoral infiltration region. Our results are well in agreement with observations described in previously published studies regarding the predictive value of the immune contexture, and thus, provide predictive potential for future development of a companion diagnostic test.

Dexamethasone-conjugated DNA nanotubes as anti-inflammatory agents in vivo.

The biopolymer DNA allows to create nanoscale, biocompatible structures, which can be designed in a target-specific and stimuli-responsive manner. DNA carrier systems with these characteristics hold a great potential for nanomedical applications, such as for the treatment of inflammatory diseases. Here we used a DNA-based drug carrier system for the pH-dependent delivery of the glucocorticoid dexamethasone into macrophages, a cell type with a key role in the regulation of inflammation. Dexamethasone (Dex) nanotubes were internalized within minutes by MH-S macrophages in vitro and by tissue resident macrophages in the mouse cremaster muscle in vivo and localized in their endosomes. Treatment with Dex nanotubes in vitro significantly reduced the LPS-induced TNF secretion by macrophages, as compared to equivalent amounts of free dexamethasone without affecting cell viability. Microinjection of Dex nanotubes into postischemic muscle tissue of anesthetized mice resulted in a marked reduction of ischemia-reperfusion-elicited leukocyte transmigration and diminished vascular expression of the endothelial adhesion molecules VCAM-1 and ICAM-1. Taken together, our results demonstrate that DNA nanotubes can be used as a platform for the targeted delivery of glucocorticoids and could thus foster the development of nanomedical therapeutics with reduced off-target effects.

DNA nanotubes as intracellular delivery vehicles in vivo.

DNA-based nanoconstructs possess great potential for biomedical applications. However, the in vivo behavior of such constructs at the microscopic tissue/cell level as well as their inflammatory potential is largely unknown. Unmethylated CpG sequences of DNA are recognized by Toll-like receptor 9 (TLR9), and thus initiate an innate immune response. In this study, we investigated the use of DNA-based nanotubes as carrier systems for CpG delivery and their effect on immune cells in vivo and in real time. DNA nanotubes were microinjected into skeletal muscle of anesthetized mice. Using in vivo microscopy, we observed that the DNA tubes were internalized within minutes by tissue-resident macrophages and localized in their endosomes. Only microinjection of CpG-decorated DNA nanotubes but not of plain DNA nanotubes or CpG oligonucleotides induced a significant recruitment of leukocytes into the muscle tissue as well as activation of the NF-ĸB pathway in surrounding cells. These results suggest that DNA nanotubes are promising delivery vehicles to target tissue macrophages, whereupon the immunogenic potential depends on the decoration with CpG oligonucleotides.