The total number of glomeruli is a fundamental parameter of kidney function but very difficult to determine using standard methodology. Here, we counted all individual glomeruli in murine kidneys and sized the capillary tufts by combining in vivo fluorescence labeling of endothelial cells, a novel tissue-clearing technique, lightsheet microscopy, and automated registration by image analysis. Total hands-on time per organ was <1 hour, and automated counting/sizing was finished in <3 hours. We also investigated the novel use of ethyl-3-phenylprop-2-enoate (ethyl cinnamate) as a nontoxic solvent-based clearing reagent that can be handled without specific safety measures. Ethyl cinnamate rapidly cleared all tested organs, including calcified bone, but the fluorescence of proteins and immunohistochemical labels was maintained over weeks. Using ethyl cinnamate-cleared kidneys, we also quantified the average creatinine clearance rate per glomerulus. This parameter decreased in the first week of experimental nephrotoxic nephritis, whereas reduction in glomerular numbers occurred much later. Our approach delivers fundamental parameters of renal function, and because of its ease of use and speed, it is suitable for high-throughput analysis and could greatly facilitate studies of the effect of kidney diseases on whole-organ physiology.

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Abbreviations: Acute myeloid leukemia (AML), dense phase (DP), fluorescence recovery after photobleaching (FRAP), fusion oncoprotein (FO), Gibbs free energy of transfer (ΔG Tr ), Gle2binding-sequence (GLEBS), human CD34-positive hematopoietic stem and progenitor cells (hCD34+ cells), immunofluorescence (IF), intrinsically disordered region (IDR), light phase (LP), lineage-negative hematopoietic stem and progenitor cells (lin-HSPCs), liquid-liquid phase separation (LLPS), mEGFP-tagged NHA9 (G-NHA9), mobile fraction (M f ), monomeric enhanced green fluorescent protein (mEGFP), nuclear pore complex (NPC), NUP98-HOXA9 (NHA9), partition coefficient (K p ), patient-derived xenograft (PDX), Pearson correlation coefficient (PCC), Principal component analysis (PCA), RNA sequencing (RNA-seq), and saturation concentration (C sat ).

Polymorphonuclear granulocytes (PMNs) are indispensable for controlling life-threatening fungal infections. In addition to various effector mechanisms, PMNs also produce extracellular vesicles (EVs). Their contribution to antifungal defense has remained unexplored. We reveal that the clinically important human-pathogenic fungus Aspergillus fumigatus triggers PMNs to release a distinct set of antifungal EVs (afEVs). Proteome analyses indicated that afEVs are enriched in antimicrobial proteins. The cargo and the release kinetics of EVs are modulated by the fungal strain confronted. Tracking of afEVs indicated that they associated with fungal cells and even entered fungal hyphae, resulting in alterations in the morphology of the fungal cell wall and dose-dependent antifungal effects. To assess as a proof of concept whether the antimicrobial proteins found in afEVs might contribute to growth inhibition of hyphae when present in the fungal cytoplasm, two human proteins enriched in afEVs, cathepsin G and azurocidin, were heterologously expressed in fungal hyphae. This led to reduced fungal growth relative to that of a control strain producing the human retinol binding protein 7. In conclusion, extracellular vesicles produced by neutrophils in response to A. fumigatus infection are able to associate with the fungus, limit growth, and elicit cell damage by delivering antifungal cargo. This finding offers an intriguing, previously overlooked mechanism of antifungal defense against A. fumigatus. IMPORTANCE Invasive fungal infections caused by the mold Aspergillus fumigatus are a growing concern in the clinic due to the increasing use of immunosuppressive therapies and increasing antifungal drug resistance. These infections result in high rates of mortality, as treatment and diagnostic options remain limited. In healthy individuals, neutrophilic granulocytes are critical for elimination of A. fumigatus from the host; however, the exact extracellular mechanism of neutrophil-mediated antifungal activity remains unresolved. Here, we present a mode of antifungal defense employed by human neutrophils against A. fumigatus not previously described. We found that extracellular vesicles produced by neutrophils in response to A. fumigatus infection are able to associate with the fungus, limit growth, and elicit cell damage by delivering antifungal cargo. In the end, antifungal extracellular vesicle biology provides a significant step forward in our understanding of A. fumigatus host pathogenesis and opens up novel diagnostic and therapeutic possibilities.

Dudeck et al. demonstrate that inflammatory conditions induce dynamic interactions between mast cells (MCs) and dendritic cells (DCs) culminating in protein exchange. Resident MCs are equipped with DC MHCII and empowered to initiate T cell–driven inflammation during migration-based DC absence.

Automated microscopy has given researchers access to great amounts of live cell imaging data from in vitro and in vivo experiments. Much focus has been put on extracting cell tracks from such data using a plethora of segmentation and tracking algorithms, but further analysis is normally required to draw biologically relevant conclusions. Such relevant conclusions may be whether the migration is directed or not, whether the population has homogeneous or heterogeneous migration patterns. This review focuses on the analysis of cell migration data that are extracted from time lapse images. We discuss a range of measures and models used to analyze cell tracks independent of the biological system or the way the tracks were obtained. For single-cell migration, we focus on measures and models giving examples of biological systems where they have been applied, for example, migration of bacteria, fibroblasts, and immune cells. For collective migration, we describe the model systems wound healing, neural crest migration, and Drosophila gastrulation and discuss methods for cell migration within these systems. We also discuss the role of the extracellular matrix and subsequent differences between track analysis in vitro and in vivo. Besides methods and measures, we are putting special focus on the need for openly available data and code, as well as a lack of common vocabulary in cell track analysis. © 2017 International Society for Advancement of Cytometry.

All-optical microspectroscopic and tomographic tools reveal great potential for clinical dermatologic diagnostics, i.e., investigation of human skin and skin diseases. While optical-coherence tomography has been complemented by twophoton fluorescence tomography and second-harmonic generation tomography, a joint study of various nonlinear optical microspectroscopies, i.e., application of the recently developed multimodal imaging approach, to sizable human-tissue samples has not been evaluated up to now. Here, we present such multimodal approach combining different nonlinear optical contrast mechanisms for imaging, namely two-photon excited fluorescence (TPF), second-harmonic generation (SHG), and coherent anti-Stokes Raman scattering (CARS) into a joint microscopic experiment. We show the potential of imaging large skin areas and discuss the information obtained in a case study comparing normal skin and keloid tissue.

Cell migration involves dynamic changes in cell shape. Intricate patterns of cell shape can be analyzed and classified using advanced shape descriptors, including spherical harmonics (SPHARM). Though SPHARM have been used to analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their dynamics. Here, we examine whether additional information from dynamic SPHARM improves classification of cell migration patterns. We combine the static and dynamic SPHARM approach with a support-vector-machine classifier and compare their classification accuracies. We demonstrate that the dynamic SPHARM analysis classifies cell migration patterns more accurately than the static one for both synthetic and experimental data. Furthermore, by comparing the computed accuracies with that of a naive classifier, we can identify the experimental conditions and model parameters that significantly affect cell shape. This capability should-in the future-help to pinpoint factors that play an essential role in cell migration. A cell's migration behavior depends on the state of the cell, extracellular environment, and signals from other cells 1. We can study the mechanisms of cell migration by, e.g., knocking out a certain gene or altering the structure of the extracellular matrix (ECM) and testing whether these changes affect cell migration patterns, such as cell trajectory, shape, or shape dynamics (Fig. 1). To compare migration patterns in an objective and statistically sound way, they have to be automatically analyzed and quantified 2. Whereas both cell trajectories 3,4 and cell shape 5,6 can be quantified with a multitude of available methods, the analysis of shape dynamics-especially in 3D-received considerably less attention. When analyzing cell shape in a static fashion, we look at just one snapshot of the cell's migration history. Depending on how we choose this snapshot, we may either miss important differences in cell shape-e.g., if cells transiently appear similar but have different migration patterns-or detect spurious differences-e.g., if cells occur in different phases of the same migration pattern. Even averaging cell shape descriptors over time 7 may not always be sufficient to distinguish some migration patterns, for example when all cells evolve through similar phases of cell shape but different cells do this with different frequencies (Fig. S1) 8. To distinguish such details of migration behavior we need dynamic shape analysis that takes into account relative changes in cell shape between consecutive time points. While such dynamic shape analysis has been done in 2D 8-11 , 3D shape descriptors have not been applied to characterize and compare the full dynamic migration patterns of cells. The ultimate goal, however, is to understand how cells migrate in living organisms 12. Due to advances in intravital microscopy 13-15 , we have increasingly more 3D + time data of cells migrating in vivo and we should exploit the potential of 3D methods to analyze these data 16. Although there are many simpl...