Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, 1030, Austria.
Doctoral School in Microbiology and Environmental Science, University of Vienna, Vienna, 1030, Austria.
New Phytol. 2023 Oct;240(1):439-451. doi: 10.1111/nph.19112. Epub 2023 Jun 28.
Bacteria colonize plant roots and engage in reciprocal interactions with their hosts. However, the contribution of individual taxa or groups of bacteria to plant nutrition and fitness is not well characterized due to a lack of in situ evidence of bacterial activity. To address this knowledge gap, we developed an analytical approach that combines the identification and localization of individual bacteria on root surfaces via gold-based in situ hybridization with correlative NanoSIMS imaging of incorporated stable isotopes, indicative of metabolic activity. We incubated Kosakonia strain DS-1-associated, gnotobiotically grown rice plants with N-N gas to detect in situ N fixation activity. Bacterial cells along the rhizoplane showed heterogeneous patterns of N enrichment, ranging from the natural isotope abundance levels up to 12.07 at% N (average and median of 3.36 and 2.85 at% N, respectively, n = 697 cells). The presented correlative optical and chemical imaging analysis is applicable to a broad range of studies investigating plant-microbe interactions. For example, it enables verification of the in situ metabolic activity of host-associated commercialized strains or plant growth-promoting bacteria, thereby disentangling their role in plant nutrition. Such data facilitate the design of plant-microbe combinations for improvement of crop management.
细菌在植物根部定殖,并与宿主进行互惠的相互作用。然而,由于缺乏细菌活性的原位证据,个体分类群或细菌群体对植物营养和适应性的贡献还没有得到很好的描述。为了解决这一知识空白,我们开发了一种分析方法,该方法将通过基于金的原位杂交在根表面识别和定位单个细菌与指示代谢活性的掺入稳定同位素的相关 NanoSIMS 成像相结合。我们用 N-N 气体孵育与 Kosakonia 菌株 DS-1 相关的、无菌生长的水稻植物,以检测原位固氮活性。沿根际平面的细菌细胞显示出不均匀的氮富集模式,从自然同位素丰度水平到 12.07 原子%氮(分别为 697 个细胞的平均值和中位数为 3.36 和 2.85 原子%氮)。所提出的相关光学和化学成像分析适用于广泛的研究,包括调查植物-微生物相互作用的研究。例如,它可以验证与宿主相关的商业化菌株或植物促生菌的原位代谢活性,从而厘清它们在植物营养中的作用。这些数据有助于设计植物-微生物组合,以改善作物管理。