Automated analysis of whole brain vasculature using machine learning, bioRxiv, 2019-04-19

SUMMARYTissue clearing methods enable imaging of intact biological specimens without sectioning. However, reliable and scalable analysis of such large imaging data in 3D remains a challenge. Towards this goal, we developed a deep learning-based framework to quantify and analyze the brain vasculature, named Vessel Segmentation &amp; Analysis Pipeline (VesSAP). Our pipeline uses a fully convolutional network with a transfer learning approach for segmentation. We systematically analyzed vascular features of the whole brains including their length, bifurcation points and radius at the micrometer scale by registering them to the Allen mouse brain atlas. We reported the first evidence of secondary intracranial collateral vascularization in CD1-Elite mice and found reduced vascularization in the brainstem as compared to the cerebrum. VesSAP thus enables unbiased and scalable quantifications for the angioarchitecture of the cleared intact mouse brain and yields new biological insights related to the vascular brain function.GRAPHICAL ABSTRACT<jatsfig id=ufig1 position=float fig-type=figure orientation=portrait>Supporting material of VesSAP is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpDISCOtechnologies.orgVesSAP>httpDISCOtechnologies.orgVesSAP<jatsext-link><jatsgraphic xmlnsxlink=httpwww.w3.org1999xlink xlinkhref=613257_ufig1 position=float orientation=portrait >

biorxiv neuroscience 200-500-users 2019

Diversity begets diversity in microbiomes, bioRxiv, 2019-04-19

AbstractMicrobes are embedded in complex microbiomes where they engage in a wide array of inter- and intra-specific interactions1–4. However, whether these interactions are a significant driver of natural biodiversity is not well understood. Two contrasting hypotheses have been put forward to explain how species interactions could influence diversification. ‘Ecological Controls’ (EC) predicts a negative diversity-diversification relationship, where the evolution of novel types becomes constrained as available niches become filled5. In contrast, ‘Diversity Begets Diversity’ (DBD) predicts a positive relationship, with diversity promoting diversification via niche construction and other species interactions6. Using the Earth Microbiome Project, the largest standardized survey of global biodiversity to date7, we provide support for DBD as the dominant driver of microbiome diversity. Only in the most diverse microbiomes does DBD reach a plateau, consistent with increasingly saturated niche space. Genera that are strongly associated with a particular biome show a stronger DBD relationship than non-residents, consistent with prolonged evolutionary interactions driving diversification. Genera with larger genomes also experience a stronger DBD response, which could be due to a higher potential for metabolic interactions and niche construction offered by more diverse gene repertoires. Our results demonstrate that the rate at which microbiomes accumulate diversity is crucially dependent on resident diversity. This fits a scenario in which species interactions are important drivers of microbiome diversity. Further (population genomic or metagenomic) data are needed to elucidate the nature of these biotic interactions in order to more fully inform predictive models of biodiversity and ecosystem stability4,5.

biorxiv evolutionary-biology 100-200-users 2019

 

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