Angiogenesis is associated with increased vessel sprouting and permeability. Important mediators of these angiogenic responses include local environment of signaling molecules and supporting extracellular matrix (ECM). However, dissecting the interplay of these instructive signals in vivo with multiple cells and extracellular molecules remains a central challenge. Here, microfluidic biomimicry is integrated with 3D ECM hydrogels that are well‐characterized for molecular‐binding and mechanical properties to reconstitute vessel‐like analogues in vitro. This study focuses on three distinct isoforms of the pro‐metastatic chemokine CXCL12. In collagen‐only hydrogel, CXCL12‐α is the most potent isoform in promoting sprouting and permeability, followed by CXCL12‐β and CXCL12‐γ. Strikingly, addition of hyaluronan (HA), a large and negatively charged glycosaminoglycan, with collagen matrices selectively increases vessel sprouting and permeability conferred by CXCL12‐γ. This outcome is supported by the measured binding affinities to collagen/HA ECM, suggesting that negatively charged HA increases the binding of CXCL12‐γ to augment its angiogenic potency. Moreover, it is shown that addition of HA to collagen matrices on its own decreases vessel sprouting and permeability, and these responses are nullified by blocking the HA receptor CD44. Collectively, these results demonstrate that differences in binding to extracellular HA help underlie CXCL12 isoform‐specific responses toward directing angiogenesis.

Immense progress in microscale engineering technologies has significantly expanded the capabilities of in vitro cell culture systems for reconstituting physiological microenvironments that are mediated by biomolecular gradients, fluid transport, and mechanical forces. Here, we examine the innovative approaches based on microfabricated vessels for studying lymphatic biology. To help understand the necessary design requirements for microfluidic models, we first summarize lymphatic vessel structure and function. Next, we provide an overview of the molecular and biomechanical mediators of lymphatic vessel function. Then we discuss the past achievements and new opportunities for microfluidic culture models to a broad range of applications pertaining to lymphatic vessel physiology. We emphasize the unique attributes of microfluidic systems that enable the recapitulation of multiple physicochemical cues in vitro for studying lymphatic pathophysiology. Current challenges and future outlooks of microscale technology for studying lymphatics are also discussed. Collectively, we make the assertion that further progress in the development of microscale models will continue to enrich our mechanistic understanding of lymphatic biology and physiology to help realize the promise of the lymphatic vasculature as a therapeutic target for a broad spectrum of diseases.

Summary How the extracellular matrix (ECM) affects the progression of a localized tumor to invasion of the ECM and eventually to vascular dissemination remains unclear. Although many studies have examined the role of the ECM in early stages of tumor progression, few have considered the subsequent stages that culminate in intravasation. In the current study, we have developed a three-dimensional (3D) microfluidic culture system that captures the entire process of invasion from an engineered human micro-tumor of MDA-MB-231 breast cancer cells through a type I collagen matrix and escape into a lymphatic-like cavity. By varying the physical properties of the collagen, we have found that MDA-MB-231 tumor cells invade and escape faster in lower-density ECM. These effects are mediated by the ECM pore size, rather than by the elastic modulus or interstitial flow speed. Our results underscore the importance of ECM structure in the vascular escape of human breast cancer cells.

Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here we develop a generalizable deep-learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. In addition, the technique is computationally efficient, making it ideal for large-scale neurovascular analysis. Introduction: Vascular segmentation from 2PM angiograms is usually an important first step in hemodynamic modeling of brain vasculature. Existing state-of-the-art segmentation methods based on deep learning either lack the ability to generalize to data from various imaging systems, or are computationally infeasible for large-scale angiograms. In this work, we present a method which improves upon both these limitations by being generalizable to various imaging systems, and also being able to segment very large-scale angiograms. Methods: We employ a computationally efficient deep learning framework based on a semi-supervised learning strategy, whose effectiveness we demonstrate on experimentally acquired in-vivo angiograms from mouse brains of dimensions up to 808x808x702 micrometers. Results: After training on data from only one 2PM microscope, we perform vascular segmentation on data from another microscope without any network tuning. Our method demonstrates 10x faster computation in terms of voxels-segmented-persecond and 3x larger depth compared to the state-of-the-art. Conclusion: Our work provides a generalizable and computationally efficient anatomical modeling framework for the brain vasculature, which consists of deeplearning based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.