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The knowledge of the blood microvasculature and its functional role in health and disease has grown significantly attributable to decades of research and numerous advances in cell biology and tissue engineering; however, the lymphatics (the secondary vascular system) has not garnered similar attention, in part due to a lack of relevant in vitro models that mimic its pathophysiological functions. Here, a microfluidic‐based approach is adopted to achieve precise control over the biological transport of growth factors and interstitial flow that drive the in vivo growth of lymphatic capillaries (lymphangiogenesis). The engineered on‐chip lymphatics with in vivo‐like morphology exhibit tissue‐scale functionality with drainage rates of interstitial proteins and molecules comparable to in vivo standards. Computational and scaling analyses of the underlying transport phenomena elucidate the critical role of the three‐dimensional geometry and lymphatic endothelium in recapitulating physiological drainage. Finally, the engineered on‐chip lymphatics enabled studies of lymphatic‐immune interactions that revealed inflammation‐driven responses by the lymphatics to recruit immune cells via chemotactic signals similar to in vivo, pathological events. This on‐chip lymphatics platform permits the interrogation of various lymphatic biological functions, as well as screening of lymphatic‐based therapies such as interstitial absorption of protein therapeutics and lymphatic immunomodulation for cancer therapy.

 

A sample of 279 massive red spirals was selected optically by Guo et al., among which 166 galaxies have been observed by the ALFALFA survey. In this work, we observe H i content of the rest 113 massive red spiral galaxies using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). 75 of the 113 galaxies have H i detection with a signal-to-noise ratio (S/N) greater than 4.7. Compared with the red spirals in the same sample that have been observed by the ALFALFA survey, galaxies observed by FAST have on average a higher S/N, and reach to a lower H i mass. To investigate why many red spirals contain a significant amount of H i mass, we check colour profiles of the massive red spirals using images observed by the DESI Legacy Imaging Surveys. We find that galaxies with H i detection have bluer outer discs than the galaxies without H i detection, for both ALFALFA and FAST samples. For galaxies with H i detection, there exists a clear correlation between galaxy H i mass and g-r colour at outer radius: galaxies with higher H i masses have bluer outer discs. The results indicate that optically selected massive red spirals are not fully quenched, and the H i gas observed in many of the galaxies may exist in their outer blue discs.

 

Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ . However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components. 

Abstract Motivation Cryoelectron tomography (cryo-ET) visualizes structure and spatial organization of macromolecules and their interactions with other subcellular components inside single cells in the close-to-native state at submolecular resolution. Such information is critical for the accurate understanding of cellular processes. However, subtomogram classification remains one of the major challenges for the systematic recognition and recovery of the macromolecule structures in cryo-ET because of imaging limits and data quantity. Recently, deep learning has significantly improved the throughput and accuracy of large-scale subtomogram classification. However, often it is difficult to get enough high-quality annotated subtomogram data for supervised training due to the enormous expense of labeling. To tackle this problem, it is beneficial to utilize another already annotated dataset to assist the training process. However, due to the discrepancy of image intensity distribution between source domain and target domain, the model trained on subtomograms in source domain may perform poorly in predicting subtomogram classes in the target domain. Results In this article, we adapt a few shot domain adaptation method for deep learning-based cross-domain subtomogram classification. The essential idea of our method consists of two parts: (i) take full advantage of the distribution of plentiful unlabeled target domain data, and (ii) exploit the correlation between the whole source domain dataset and few labeled target domain data. Experiments conducted on simulated and real datasets show that our method achieves significant improvement on cross domain subtomogram classification compared with baseline methods. Availability and implementation Software is available online https://github.com/xulabs/aitom. Supplementary information Supplementary data are available at Bioinformatics online. 

Microtubules (MTs) mediate mitosis, directional signaling, and are therapeutic targets in cancer. Yet in vivo analysis of cancer cell MT behavior within the tumor microenvironment remains challenging. Here we developed an imaging pipeline using plus-end tip tracking and intravital microscopy to quantify MT dynamics in live xenograft tumor models. Among analyzed features, cancer cells in vivo displayed higher coherent orientation of MT dynamics along their cell major axes compared with 2D in vitro cultures, and distinct from 3D collagen gel cultures. This in vivo MT phenotype was reproduced in vitro when cells were co-cultured with IL4-polarized MΦ. MΦ depletion, MT disruption, targeted kinase inhibition, and altered MΦ polarization via IL10R blockade all reduced MT coherence and/or tumor cell elongation. We show that MT coherence is a defining feature for in vivo tumor cell dynamics and migration, modulated by local signaling from pro-tumor macrophages.