One-Cell Metabolic Phenotyping and Sequencing of Soil Microbiome by Raman-Activated Gravity-Driven Encapsulation (RAGE).
One-Cell Metabolic Phenotyping and Sequencing of Soil Microbiome by Raman-Activated Gravity-Driven Encapsulation (RAGE).
复制标题
通过拉曼激活重力驱动封装 (RAGE) 对土壤微生物组进行单细胞代谢表型分析和测序
DOI：
10.1128/msystems.00181-21
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发表时间：
2021-06-29
期刊：
影响因子：
6.4
通讯作者：
Xu J
中科院分区：
文献类型：
--
作者：
Jing X;Gong Y;Xu T;Meng Y;Han X;Su X;Wang J;Ji Y;Li Y;Jia Z;Ma B;Xu J
土壤是一个巨大而复杂的微生物组的家园，其特征可以说是地球上细胞的最高基因组多样性和代谢异质性。它们的原位代谢活动驱动着许多具有关键生态意义的自然过程，或者是许多有价值的生物活性的工业生产的基础。土壤中存在着地球上代谢和遗传上最不均匀的微生物组，然而在精确的单细胞水平上建立代谢功能和基因组之间的联系是困难的。在这里，对于模拟微生物群落，然后是土壤微生物群，我们建立了一个拉曼激活的重力驱动的单细胞封装和测序（RAGE-Seq）平台，该平台通过其合成代谢（从重水中掺入D）和生理（含类胡萝卜素）功能精确识别，分类和测序一个细菌细胞。我们发现（i）来自数量稀少的土壤类群的代谢活性细胞，如棒状杆菌属，梭菌属，莫拉氏菌属，泛菌属，和假单胞菌属，可以容易地基于D2 O摄取进行鉴定和分选，并且它们的单细胞基因组覆盖率可以达到93%以上，以允许高质量的全基因组代谢重建;（ii）类似地，含类胡萝卜素的细胞如泛菌属，军团菌属，Massilia spp.，假单胞菌属，和土壤杆菌属（Pedobacter spp.）被确定和单细胞基因组产生跟踪类胡萝卜素合成途径;和（iii）类胡萝卜素生产细胞可以是代谢活跃或惰性，这表明基于培养的方法可以错过许多这样的细胞。作为拉曼激活细胞分选仪（RACS）家族成员，RAGE-Seq可以从土壤中精确地以单细胞分辨率建立代谢-基因组链接，可以帮助精确地确定复杂生态系统中的“谁在做什么”。土壤是一个巨大而复杂的微生物组的家园，其特征可以说是地球上最高的细胞基因组多样性和代谢异质性。它们的原位代谢活动驱动着许多具有关键生态意义的自然过程，或者是许多有价值的生物活性的工业生产的基础。然而，在主要由尚未培养的物种组成的土壤微生物组中确定“谁在做什么”仍然是一个重大挑战。在这里，对于土壤微生物群，我们建立了一种拉曼激活的重力驱动的单细胞封装和测序（RAGE-Seq）方法，该方法通过其分解代谢和合成代谢功能，以精确的一个微生物细胞的分辨率进行识别，分类和测序。作为拉曼激活细胞分选仪（RACS）家族成员，RAGE-Seq可以从土壤中以单细胞分辨率建立代谢-基因组链接，可以帮助精确确定复杂生态系统中的“谁在做什么”。
Soil is home to an enormous and complex microbiome that features arguably the highest genomic diversity and metabolic heterogeneity of cells on Earth. Their in situ metabolic activities drive many natural processes of pivotal ecological significance or underlie industrial production of numerous valuable bioactivities. ABSTRACT Soil harbors arguably the most metabolically and genetically heterogeneous microbiomes on Earth, yet establishing the link between metabolic functions and genome at the precisely one-cell level has been difficult. Here, for mock microbial communities and then for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) platform, which identifies, sorts, and sequences precisely one bacterial cell via its anabolic (incorporating D from heavy water) and physiological (carotenoid-containing) functions. We showed that (i) metabolically active cells from numerically rare soil taxa, such as Corynebacterium spp., Clostridium spp., Moraxella spp., Pantoea spp., and Pseudomonas spp., can be readily identified and sorted based on D2O uptake, and their one-cell genome coverage can reach ∼93% to allow high-quality genome-wide metabolic reconstruction; (ii) similarly, carotenoid-containing cells such as Pantoea spp., Legionella spp., Massilia spp., Pseudomonas spp., and Pedobacter spp. were identified and one-cell genomes were generated for tracing the carotenoid-synthetic pathways; and (iii) carotenoid-producing cells can be either metabolically active or inert, suggesting culture-based approaches can miss many such cells. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at exactly one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint “who is doing what” in complex ecosystems. IMPORTANCE Soil is home to an enormous and complex microbiome that features arguably the highest genomic diversity and metabolic heterogeneity of cells on Earth. Their in situ metabolic activities drive many natural processes of pivotal ecological significance or underlie industrial production of numerous valuable bioactivities. However, pinpointing “who is doing what” in a soil microbiome, which consists of mainly yet-to-be-cultured species, has remained a major challenge. Here, for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) method, which identifies, sorts, and sequences at the resolution of precisely one microbial cell via its catabolic and anabolic functions. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint “who is doing what” in complex ecosystems.