A novel algorithm for optimal image thresholding of biological data

Adaptive tracking algorithm for trajectory analysis of cells and layer-by-layer assessment of motility dynamics

Tracking biological objects such as cells or subcellular components imaged with time-lapse microscopy enables us to understand the molecular principles about the dynamics of cell behaviors. However, automatic object detection, segmentation and extracting trajectories remain as a rate-limiting step due to intrinsic challenges of video processing. This paper presents an adaptive tracking algorithm (Adtari) that automatically finds the optimum search radius and cell linkages to determine trajectories in consecutive frames. A critical assumption in most tracking studies is that displacement remains unchanged throughout the movie and cells in a few frames are usually analyzed to determine its magnitude. Tracking errors and inaccurate association of cells may occur if the user does not correctly evaluate the value or prior knowledge is not present on cell movement. The key novelty of our method is that minimum intercellular distance and maximum displacement of cells between frames are dynamically computed and used to determine the threshold distance. Since the space between cells is highly variable in a given frame, our software recursively alters the magnitude to determine all plausible matches in the trajectory analysis. Our method therefore eliminates a major preprocessing step where a constant distance was used to determine the neighbor cells in tracking methods. Cells having multiple overlaps and splitting events were further evaluated by using the shape attributes including perimeter, area, ellipticity and distance. The features were applied to determine the closest matches by minimizing the difference in their magnitudes. Finally, reporting section of our software were used to generate instant maps by overlaying cell features and trajectories. Adtari was validated by using videos with variable signal-to-noise, contrast ratio and cell density. We compared the adaptive tracking with constant distance and other methods to evaluate performance and its efficiency. Our algorithm yields reduced mismatch ratio, increased ratio of whole cell track, higher frame tracking efficiency and allows layer-by-layer assessment of motility to characterize single-cells. Adaptive tracking provides a reliable, accurate, time efficient and user-friendly open source software that is well suited for analysis of 2D fluorescence microscopy video datasets.

Dynamic Modeling of Mitochondrial Membrane Potential Upon Exposure to Mitochondrial Inhibitors

Mitochondria are the main bioenergetic organelles of cells. Exposure to chemicals targeting mitochondria therefore generally results in the development of toxicity. The cellular response to perturbations in cellular energy production is a balance between adaptation, by reorganisation and organelle biogenesis, and sacrifice, in the form of cell death. In homeostatic conditions, aerobic mitochondrial energy production requires the maintenance of a mitochondrial membrane potential (MMP). Chemicals can perturb this MMP, and the extent of this perturbation depends both on the pharmacokinetics of the chemicals and on downstream MMP dynamics. Here we obtain a quantitative understanding of mitochondrial adaptation upon exposure to various mitochondrial respiration inhibitors by applying mathematical modeling to partially published high-content imaging time-lapse confocal imaging data, focusing on MMP dynamics in HepG2 cells over a period of 24 h. The MMP was perturbed using a set of 24 compounds, either acting as uncoupler or as mitochondrial complex inhibitor targeting complex I, II, III or V. To characterize the effect of chemical exposure on MMP dynamics, we adapted an existing differential equation model and fitted this model to the observed MMP dynamics. Complex III inhibitor data were better described by the model than complex I data. Incorporation of pharmacokinetic decay into the model was required to obtain a proper fit for the uncoupler FCCP. Furthermore, oligomycin (complex V inhibitor) model fits were improved by either combining pharmacokinetic (PK) decay and ion leakage or a concentration-dependent decay. Subsequent mass spectrometry measurements showed that FCCP had a significant decay in its PK profile as predicted by the model. Moreover, the measured oligomycin PK profile exhibited only a limited decay at high concentration, whereas at low concentrations the compound remained below the detection limit within cells. This is consistent with the hypothesis that oligomycin exhibits a concentration-dependent decay, yet awaits further experimental verification with more sensitive detection methods. Overall, we show that there is a complex interplay between PK and MMP dynamics within mitochondria and that data-driven modeling is a powerful combination to unravel such complexity.

TIMP1 Triggers Neutrophil Extracellular Trap Formation in Pancreatic Cancer

Tumor-derived protein tissue inhibitor of metalloproteinases-1 (TIMP1) correlates with poor prognosis in many cancers, including highly lethal pancreatic ductal adenocarcinoma (PDAC). The noncanonical signaling activity of TIMP1 is emerging as one basis for its contribution to cancer progression. However, TIMP1-triggered progression-related biological processes are largely unknown. Formation of neutrophil extracellular traps (NET) in the tumor microenvironment is known to drive progression of PDAC, but factors or molecular mechanisms initiating NET formation in PDAC remain elusive. In this study, gene-set enrichment analysis of a human PDAC proteome dataset revealed that TIMP1 protein expression most prominently correlates with neutrophil activation in patient-derived tumor tissues. TIMP1 directly triggered formation of NETs in primary human neutrophils, which was dependent on the interaction of TIMP1 with its receptor CD63 and subsequent ERK signaling. In genetically engineered PDAC-bearing mice, TIMP1 significantly contributed to NET formation in tumors, and abrogation of TIMP1 or NETs prolonged survival. In patient-derived PDAC tumors, NETs predominantly colocalized with areas of elevated TIMP1 expression. Furthermore, TIMP1 plasma levels correlated with DNA-bound myeloperoxidase, a NET marker, in the blood of patients with PDAC. A combination of plasma levels of TIMP1 and NETs with the clinically established marker CA19-9 allowed improved identification of prognostically distinct PDAC patient subgroups. These observations may have a broader impact, because elevated systemic levels of TIMP1 are associated with the progression of a wide range of neutrophil-involved inflammatory diseases. SIGNIFICANCE: These findings highlight the prognostic relevance of TIMP1 and neutrophil extracellular traps in highly lethal pancreatic cancer, where a noncanonical TIMP1/CD63/ERK signaling axis induces NET formation. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/13/3568/F1.large.jpg.

Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning

Phenotypic profiling of large three-dimensional microscopy data sets has not been widely adopted due to the challenges posed by cell segmentation and feature selection. The computational demands of automated processing further limit analysis of hard-to-segment images such as of neurons and organoids. Here we describe a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and advanced data visualization. We demonstrate the analysis potential on complex 3D images by investigating the phenotypic alterations of: neurons in response to apoptosis-inducing treatments and morphogenesis for oncogene-expressing human mammary gland acinar organoids. Our novel implementation of image analysis algorithms called Phindr3D allowed rapid implementation of data-driven voxel-based feature learning into 3D high content analysis (HCA) operations and constitutes a major practical advance as the computed assignments represent the biology while preserving the heterogeneity of the underlying data. Phindr3D is provided as Matlab code and as a stand-alone program (https://github.com/DWALab/Phindr3D).

Fluorescence microscopy is a fundamental technology for cell biology. However, unbiased quantitative phenotypic analysis of microscopy images of cells grown in 3D organoids or in dense culture conditions in large enough numbers to reach statistical clarity remains a fundamental challenge. Here, we report that using data-driven voxel-based features and machine learning it is possible to analyze complex 3D image data without compressing them to 2D, identifying individual cells or using computationally intensive deep learning techniques. Further, we present methods for analyzing this data by classification or clustering. Together these techniques provide the means for facile discovery and interpretation of meaningful patterns in a high dimensional feature space without complex image processing and prior knowledge or assumptions about the feature space. Our method enables novel opportunities for rapid large-scale multivariate phenotypic microscopy image analysis in 3D using a standard desktop computer.

Quantitative image analysis of microbial communities with BiofilmQ

Biofilms are microbial communities that represent a highly abundant form of microbial life on Earth. Inside biofilms, phenotypic and genotypic variations occur in three-dimensional space and time; microscopy and quantitative image analysis are therefore crucial for elucidating their functions. Here, we present BiofilmQ—a comprehensive image cytometry software tool for the automated and high-throughput quantification, analysis and visualization of numerous biofilm-internal and whole-biofilm properties in three-dimensional space and time.

BiofilmQ is an image cytometry software tool that enables the visualization, quantification and analysis of biofilm properties, providing insights into their structure and function.

Automated Quantification of Mitochondrial Fragmentation in an In Vitro Parkinson's Disease Model

Neuronal mitochondrial fragmentation is a phenotype exhibited in models of neurodegeneration such as Parkinson's disease. Delineating the dysfunction in mitochondrial dynamics found in diseased states can aid our understanding of underlying mechanisms of disease progression and possibly identify novel therapeutic approaches. Advances in microscopy and the availability of intuitive open-access software have accelerated the rate of image acquisition and analysis, respectively. These developments allow routine biology researchers to rapidly turn hypotheses into results. In this protocol, we describe the utilization of cell culture techniques, high-content imaging (HCI), and the subsequent open-source image analysis pipeline for the quantification of mitochondrial fragmentation in the context of a rotenone-based in vitro Parkinson's disease model. © 2020 The Authors. Basic Protocol 1: SN4741 neuron culture and treatment in a rotenone-based model of Parkinson's disease Basic Protocol 2: Identification of cell nuclei, measurement of mitochondrial membrane potential, and measurement of mitochondrial fragmentation in mouse-derived midbrain dopaminergic neurons.

Fluorescence anisotropy imaging in drug discovery

Non-invasive measurement of drug-target engagement can provide critical insights in the molecular pharmacology of small molecule drugs. Fluorescence polarization/fluorescence anisotropy measurements are commonly employed in protein/cell screening assays. However, the expansion of such measurements to the in vivo setting has proven difficult until recently. With the advent of high-resolution fluorescence anisotropy microscopy it is now possible to perform kinetic measurements of intracellular drug distribution and target engagement in commonly used mouse models. In this review we discuss the background, current advances and future perspectives in intravital fluorescence anisotropy measurements to derive pharmacokinetic and pharmacodynamic measurements in single cells and whole organs.

Centrifugal Inputs to the Main Olfactory Bulb Revealed Through Whole Brain Circuit-Mapping

Neuronal activity in sensory regions can be modulated by attention, behavioral state, motor output, learning, and memory. This is often done through direct feedback or centrifugal projections originating from higher processing areas. Though, functionally important, the identity and organization of these feedback connections remain poorly characterized. Using a retrograde monosynaptic g-deleted rabies virus and whole-brain reconstructions, we identified the organization of feedback projecting neurons to the main olfactory bulb of the mouse. In addition to previously described projections from regions such as the Anterior Olfactory Nucleus (AON) and the piriform cortex, we characterized direct projections from pyramidal cells in the ventral CA1 region of hippocampus and the entorhinal cortex to the granule cell layer (GCL) of the main olfactory bulb (MOB). These data suggest that areas involved in stress, anxiety, learning and memory are all tethered to olfactory coding, two synapses away from where chemical compounds are first detected. Consequently, we hypothesize that understanding olfactory perception, even at the earliest stages, may require studying memory and behavior in addition to studying the physiochemical features of odors.

Perfluorononanoic acid (PFNA) alters lipid accumulation in bovine blastocysts after oocyte exposure during in vitro maturation

Perfluorononanoic acid (PFNA) is one of the perfluoroalkyl acids present in human tissues. In this study, effects on early embryo development after PFNA exposure were investigated using the bovine in vitro production system. Oocytes were exposed to PFNA during maturation in vitro (10 μg mL^-1 and 0.1 μg mL^-1), and then fertilized and cultured in parallel with control groups. Developmental parameters (cleavage, blastocyst formation) were followed and embryo quality evaluated (stage, grade). Embryos developed after exposure to 0.1 μg mL^-1 were stained to distinguish nuclei, active mitochondria and neutral lipids. 10 μg mL^-1 of PFNA had a severe negative effect on blastocyst formation (OR: 0.27 p < 0.05), an effect not observed at 0.1 μg mL^-1. However, lipid droplet distribution was significantly altered in embryos exposed to 0.1 μg mL^-1, suggesting a disturbance of lipid metabolism after exposure to sublethal levels of PFNA during oocyte maturation in vitro.

A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos

We present a novel computational framework for the analysis of high-throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made. Spatial, temporal, and cross-sectional variation of the analysed data are accommodated by two key contributions. First, time series analysis is exploited to estimate the temporal cell shape uncertainty in addition to cell trajectory. Second, a fast marching (FM) algorithm is used to integrate the inferred cell properties with the observed image measurements in order to obtain image likelihood for cell segmentation, and association. The proposed approach has been tested on eight different time-lapse microscopy data sets, some of which are high-throughput, demonstrating promising results for the detection, segmentation and association of planar cells. Our results surpass the state of the art for the Fluo-C2DL-MSC data set of the Cell Tracking Challenge (Maška et al., 2014).

Landscape of nuclear transport receptor cargo specificity

Nuclear transport receptors (NTRs) recognize localization signals of cargos to facilitate their passage across the central channel of nuclear pore complexes (NPCs). About 30 different NTRs constitute different transport pathways in humans and bind to a multitude of different cargos. The exact cargo spectrum of the majority of NTRs, their specificity and even the extent to which active nucleocytoplasmic transport contributes to protein localization remains understudied because of the transient nature of these interactions and the wide dynamic range of cargo concentrations. To systematically map cargo-NTR relationships in situ, we used proximity ligation coupled to mass spectrometry (BioID). We systematically fused the engineered biotin ligase BirA* to 16 NTRs. We estimate that a considerable fraction of the human proteome is subject to active nuclear transport. We quantified the specificity and redundancy in NTR interactions and identified transport pathways for cargos. We extended the BioID method by the direct identification of biotinylation sites. This approach enabled us to identify interaction interfaces and to discriminate direct versus piggyback transport mechanisms. Data are available via ProteomeXchange with identifier PXD007976.

Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis

Here, we describe a multiplexed immunohistochemical platform with computational image processing workflows, including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant GVAX vaccination and revealed that response to therapy correlated with degree of mono-myelocytic cell density and percentages of CD8⁺ T cells expressing T cell exhaustion markers. These data highlight the utility of in situ immune monitoring for patient stratification and provide digital image processing pipelines to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to improve biomarker discovery and assessment.

Rapid phenotypic stress-based microfluidic antibiotic susceptibility testing of Gram-negative clinical isolates

Bacteremia is a life-threatening condition for which antibiotics must be prescribed within hours of clinical diagnosis. Since the current gold standard for bacteremia diagnosis is based on conventional methods developed in the mid-1800s-growth on agar or in broth-identification and susceptibility profiling for both Gram-positive and Gram-negative bacterial species requires at least 48-72 h. Recent advancements in accelerated phenotypic antibiotic susceptibility testing have centered on the microscopic growth analysis of small bacterial populations. These approaches are still inherently limited by the bacterial growth rate. Our approach is fundamentally different. By applying environmental stress to bacteria in a microfluidic platform, we can correctly assign antibiotic susceptibility profiles of clinically relevant Gram-negative bacteria within two hours of antibiotic introduction rather than 8-24 h. The substantial expansion to include a number of clinical isolates of important Gram-negative species-Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa-reported here underscores the broad utility of our approach, complementing the method's proven utility for Gram-positive bacteria. We also demonstrate that the platform is compatible with antibiotics that have varying mechanisms of action-meropenem, gentamicin, and ceftazidime-highlighting the versatility of this platform.

Diverse Representations of Olfactory Information in Centrifugal Feedback Projections

Although feedback or centrifugal projections from higher processing centers of the brain to peripheral regions have long been known to play essential functional roles, the anatomical organization of these connections remains largely unknown. Using a virus-based retrograde labeling strategy and 3D whole-brain reconstruction methods, we mapped the spatial organization of centrifugal projections from two olfactory cortical areas, the anterior olfactory nucleus (AON) and the piriform cortex, to the granule cell layer of the main olfactory bulb in the mouse. Both regions are major recipients of information from the bulb and are the largest sources of feedback to the bulb, collectively constituting circuits essential for olfactory coding and olfactory behavior. We found that, although ipsilateral inputs from the AON were uniformly distributed, feedback from the contralateral AON had a strong ventral bias. In addition, we observed that centrifugally projecting neurons were spatially clustered in the piriform cortex, in contrast to the distributed feedforward axonal inputs that these cells receive from the principal neurons of the bulb. Therefore, information carried from the bulb to higher processing structures by anatomically stereotypic projections is likely relayed back to the bulb by organizationally distinct feedback projections that may reflect different coding strategies and therefore different functional roles.

SIGNIFICANCE STATEMENT

Principles of anatomical organization, sometimes instantiated as "maps" in the mammalian brain, have provided key insights into the structure and function of circuits in sensory systems. Generally, these characterizations focus on projections from early sensory processing areas to higher processing structures despite considerable evidence that feedback or centrifugal projections often constitute major conduits of information flow. Our results identify structure in the organization of centrifugal feedback projections to the olfactory bulb that is fundamentally different from the organization of feedforward circuits. Our study suggests that understanding computations performed in the olfactory bulb, and more generally in the olfactory system, requires understanding interactions between feedforward and feedback "maps" both structurally and functionally.

Multiplexed immunohistochemistry image analysis using sparse coding

Multiplexed immunohistochemical (IHC) methods have been developed to evaluate multiple protein biomarkers in a single formalin-fixed paraffin-embedded (FFPE) tissue section. Since distinct populations of resident and recruited immune cells in tissues (and tumors) not only regulate progression of malignant disease, these also represent targets for novel immune-based therapies; thus, improved tissue biomarker assessment evaluating immune responses in situ are needed. To objectively identify distinct cell subsets in tissues and tumors, we adopted sparse coding approaches enabling modeling of data vectors as sparse linear combinations of basis elements, to audit cellular presence and phenotypes using image cytometry datasets with unbiased assessments. By doing comparative analyses between manual gating (ground truth) and sparse coding, we report that results are comparable as obtained by manual gating strategies, and demonstrate robustness and objectivity of this novel bioinformatics approach.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding

BACKGROUND

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility.

NEW METHOD

Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set.

RESULTS

Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings.

COMPARISON WITH EXISTING METHODS

We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples.

CONCLUSION

The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks.

Stretch Injury of Human Induced Pluripotent Stem Cell Derived Neurons in a 96 Well Format

Traumatic brain injury (TBI) is a major cause of mortality and morbidity with limited therapeutic options. Traumatic axonal injury (TAI) is an important component of TBI pathology. It is difficult to reproduce TAI in animal models of closed head injury, but in vitro stretch injury models reproduce clinical TAI pathology. Existing in vitro models employ primary rodent neurons or human cancer cell line cells in low throughput formats. This in vitro neuronal stretch injury model employs human induced pluripotent stem cell-derived neurons (hiPSCNs) in a 96 well format. Silicone membranes were attached to 96 well plate tops to create stretchable, culture substrates. A custom-built device was designed and validated to apply repeatable, biofidelic strains and strain rates to these plates. A high content approach was used to measure injury in a hypothesis-free manner. These measurements are shown to provide a sensitive, dose-dependent, multi-modal description of the response to mechanical insult. hiPSCNs transition from healthy to injured phenotype at approximately 35% Lagrangian strain. Continued development of this model may create novel opportunities for drug discovery and exploration of the role of human genotype in TAI pathology.

High-Content Microscopy Analysis of Subcellular Structures: Assay Development and Application to Focal Adhesion Quantification

High-content analysis (HCA) converts raw light microscopy images to quantitative data through the automated extraction, multiparametric analysis, and classification of the relevant information content. Combined with automated high-throughput image acquisition, HCA applied to the screening of chemicals or RNAi-reagents is termed high-content screening (HCS). Its power in quantifying cell phenotypes makes HCA applicable also to routine microscopy. However, developing effective HCA and bioinformatic analysis pipelines for acquisition of biologically meaningful data in HCS is challenging. Here, the step-by-step development of an HCA assay protocol and an HCS bioinformatics analysis pipeline are described. The protocol's power is demonstrated by application to focal adhesion (FA) detection, quantitative analysis of multiple FA features, and functional annotation of signaling pathways regulating FA size, using primary data of a published RNAi screen. The assay and the underlying strategy are aimed at researchers performing microscopy-based quantitative analysis of subcellular features, on a small scale or in large HCS experiments. © 2016 by John Wiley & Sons, Inc.

CellProfiler: Novel Automated Image Segmentation Procedure for Super-Resolution Microscopy

BACKGROUND

Super resolution (SR) microscopy enabled cell biologists to visualize subcellular details up to 20 nm in resolution. This breakthrough in spatial resolution made image analysis a challenging procedure. Direct and automated segmentation of SR images remains largely unsolved, especially when it comes to providing meaningful biological interpretations.

RESULTS

Here, we introduce a novel automated imaging analysis routine, based on Gaussian, followed by a segmentation procedure using CellProfiler software (www.cellprofiler.org). We tested this method and succeeded to segment individual nuclear pore complexes stained with gp210 and pan-FG proteins and captured by two-color STED microscopy. Test results confirmed accuracy and robustness of the method even in noisy STED images of gp210.

CONCLUSIONS

Our pipeline and novel segmentation procedure may benefit end-users of SR microscopy to analyze their images and extract biologically significant quantitative data about them in user-friendly and fully-automated settings.

Urocortin3 mediates somatostatin-dependent negative feedback control of insulin secretion

The peptide hormone urocortin3 (Ucn3) is abundantly expressed by mature beta cells, yet its physiological role is unknown. Here we demonstrate that Ucn3 is stored and co-released with insulin and potentiates glucose-stimulated somatostatin secretion via cognate receptors on delta cells. Further, we found that islets lacking endogenous Ucn3 have fewer delta cells, reduced somatostatin content, impaired somatostatin secretion, and exaggerated insulin release, and that these defects are rectified by treatment with synthetic Ucn3 in vitro. Our observations indicate that the paracrine actions of Ucn3 activate a negative feedback loop that promotes somatostatin release to ensure the timely reduction of insulin secretion upon normalization of plasma glucose. Moreover, Ucn3 is markedly depleted from beta cells in mouse and macaque models of diabetes and in human diabetic islets. This suggests that Ucn3 is a key contributor to stable glycemic control, whose reduction during diabetes aggravates glycemic volatility and contributes to the pathophysiology of this disease.

In vitro myelin formation using embryonic stem cells

Myelination in the central nervous system is the process by which oligodendrocytes form myelin sheaths around the axons of neurons. Myelination enables neurons to transmit information more quickly and more efficiently and allows for more complex brain functions; yet, remarkably, the underlying mechanism by which myelination occurs is still not fully understood. A reliable in vitro assay is essential to dissect oligodendrocyte and myelin biology. Hence, we developed a protocol to generate myelinating oligodendrocytes from mouse embryonic stem cells and established a myelin formation assay with embryonic stem cell-derived neurons in microfluidic devices. Myelin formation was quantified using a custom semi-automated method that is suitable for larger scale analysis. Finally, early myelination was followed in real time over several days and the results have led us to propose a new model for myelin formation.

Systemic Evaluation of Electrical Stimulation for Ischemic Wound Therapy in a Preclinical In Vivo Model

Objective: In a systematic preclinical investigation of ischemic wound healing, we investigated the hypothesis that electrical stimulation (ES) promotes the healing of ischemic wounds. Approach: The effects of varying clinically relevant ES variables were evaluated using our modified version of the Gould F344 rat ischemic wound model. Stimulation was delivered using the novel lightweight integrated, single-channel, current-controlled modular surface stimulation (MSS) device. Stepwise variation allowed the effects of five different stimulation paradigms within an appropriate current density range to be studied. Within each group, 8-10 animals were treated for 28 days or until the ischemic wounds were healed and 5 animals were treated for 12 days. Eight rats received sham devices. A quantitative multivariable outcomes assessment procedure was used to evaluate the effects of ES. Results: Ischemic wounds treated with a decreased interpulse interval (IPI) had the highest rate of complete wound closure at 3 weeks. Wounds treated with decreased pulse amplitude (PA) had a lower proportion of closed wounds than sham ischemic wounds and showed sustained inflammation with a lack of wound contraction. Innovation: Our systematic study of varying ES paradigms using the novel MSS device provides preliminary insight into potential mechanisms of ES in ischemic wound healing. Conclusion: Clinically appropriate ES can more than double the proportion of ischemic wounds closed by 3 weeks in this model. Ninety percent of wounds treated with a decreased IPI healed by 21 days compared with only 29% of ischemic wounds treated with decreased PA, which appears to inhibit healing.