Leukocyte transendothelial migration (TEM) has been modeled as a multistep process beginning with rolling adhesion, followed by firm adhesion, and ending with either transcellular or paracellular passage of the leukocyte across the endothelial monolayer. In the case of paracellular TEM, endothelial cell (EC) junctions are transiently disassembled to allow passage of leukocytes. Numerous lines of evidence demonstrate that tyrosine phosphorylation of adherens junction proteins, such as vascular endothelial cadherin (VE-cadherin) and β-catenin, correlates with the disassembly of junctions. However, the role of tyrosine phosphorylation in the regulation of junctions during leukocyte TEM is not completely understood. Using human leukocytes and EC, we show that ICAM-1 engagement leads to activation of two tyrosine kinases, Src and Pyk2. Using phospho-specific Abs, we show that engagement of ICAM-1 induces phosphorylation of VE-cadherin on tyrosines 658 and 731, which correspond to the p120-catenin and β-catenin binding sites, respectively. These phosphorylation events require the activity of both Src and Pyk2. We find that inhibition of endothelial Src with PP2 or SU6656 blocks neutrophil transmigration (71.1 ± 3.8% and 48.6 ± 3.8% reduction, respectively), whereas inhibition of endothelial Pyk2 also results in decreased neutrophil transmigration (25.5 ± 6.0% reduction). Moreover, overexpression of the nonphosphorylatable Y658F or Y731F mutants of VE-cadherin impairs transmigration of neutrophils compared with overexpression of wild-type VE-cadherin (32.7 ± 7.1% and 38.8 ± 6.5% reduction, respectively). Our results demonstrate that engagement of ICAM-1 by leukocytes results in tyrosine phosphorylation of VE-cadherin, which is required for efficient neutrophil TEM.

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.

During trans-endothelial migration (TEM), leukocytes use adhesion receptors such as intercellular adhesion molecule-1 (ICAM1) to adhere to the endothelium. In response to this interaction, the endothelium throws up dynamic membrane protrusions, forming a cup that partially surrounds the adherent leukocyte. Little is known about the signaling pathways that regulate cup formation. In this study, we show that RhoG is activated downstream from ICAM1 engagement. This activation requires the intracellular domain of ICAM1. ICAM1 colocalizes with RhoG and binds to the RhoG-specific SH3-containing guanine-nucleotide exchange factor (SGEF). The SH3 domain of SGEF mediates this interaction. Depletion of endothelial RhoG by small interfering RNA does not affect leukocyte adhesion but decreases cup formation and inhibits leukocyte TEM. Silencing SGEF also results in a substantial reduction in RhoG activity, cup formation, and TEM. Together, these results identify a new signaling pathway involving RhoG and its exchange factor SGEF downstream from ICAM1 that is critical for leukocyte TEM.

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We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation.