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原位数字化合成策略在海洋生物的发现和描述中的应用。

An in situ digital synthesis strategy for the discovery and description of ocean life.

机构信息

Bigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544, USA.

School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.

出版信息

Sci Adv. 2024 Jan 19;10(3):eadj4960. doi: 10.1126/sciadv.adj4960. Epub 2024 Jan 17.

Abstract

Revolutionary advancements in underwater imaging, robotics, and genomic sequencing have reshaped marine exploration. We present and demonstrate an interdisciplinary approach that uses emerging quantitative imaging technologies, an innovative robotic encapsulation system with in situ RNA preservation and next-generation genomic sequencing to gain comprehensive biological, biophysical, and genomic data from deep-sea organisms. The synthesis of these data provides rich morphological and genetic information for species description, surpassing traditional passive observation methods and preserved specimens, particularly for gelatinous zooplankton. Our approach enhances our ability to study delicate mid-water animals, improving research in the world's oceans.

摘要

水下成像、机器人技术和基因组测序方面的革命性进展改变了海洋探索的格局。我们提出并展示了一种跨学科的方法,该方法利用新兴的定量成像技术、具有原位 RNA 保存功能的创新型机器人封装系统和下一代基因组测序技术,从深海生物中获取全面的生物、生物物理和基因组数据。这些数据的综合为物种描述提供了丰富的形态和遗传信息,超越了传统的被动观察方法和保存标本,特别是对于凝胶状浮游动物。我们的方法提高了研究脆弱的中层水动物的能力,从而改善了对全球海洋的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2751/10793947/33d88732242b/sciadv.adj4960-f1.jpg

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