藻类生长强烈依赖于磷(P)作为关键营养素,强调监测P水平的重要性。藻类物种对磷可用性的波动表现出敏感的反应,特别是通过碱性磷酸酶(AP)的表达,当用P耗尽攻击。因此,碱性磷酸酶活性(APA)是衡量磷利用率的一个有价值的指标,提供有关藻类如何利用和修复可用P资源的见解。然而,目前的APA定量方法缺乏单细胞分辨率,同时也是时间和试剂消耗。微流体为这些限制提供了一个有前途的成本效益的解决方案,提供精确的单细胞分析平台。在这项研究中,基于陷阱的微流体装置与市售的AP活染色剂集成,以研究模型藻类菌株的单细胞APA反应,莱茵衣藻,当暴露于不同的外源P水平时。三步培养饥饿刺突过程用于在两种不同的基础P水平(1和21mM)下培养的细胞中诱导APA。当用不同的加标P水平(范围从0.1-41mM)挑战时,莱茵衣原体细胞表现出高度异质性的APA反应。双向方差分析证实,这种反应受到加标和基础P水平的影响。利用无监督机器学习方法(HDBSCAN),鉴定了不同的莱茵衣原体细胞亚群,在单细胞水平上表现出不同水平的APA。这些亚群包括具有明显高或低APA的单个细胞的重要群体,有助于队列的整体行为。在具有相似平均行为的队列中观察到APA的相当大的群体内差异。例如,虽然一些队列在总体平均APA周围表现出集中分布,其他显示出分散在更广泛的APA水平范围内的亚群。这强调了通过分析大量细胞而引入的潜在偏见,这可能会通过过度代表极端行为亚群来扭曲结果。这项研究的发现强调了需要考虑APA中单细胞异质性的分析方法,并证明了微流体作为此类研究的非常适合手段的实用性。这项研究阐明了在单细胞水平上APA调节的复杂性,提供重要的见解,促进我们对藻类磷代谢和环境响应的理解。
Algal growth depends strongly on phosphorus (P) as a key nutrient, underscoring the significance of monitoring P levels. Algal species display a sensitive response to fluctuations in P availability, notably through the expression of alkaline phosphatase (AP) when challenged with P-depletion. As such, alkaline phosphatase activity (APA) serves as a valuable metric for P availability, offering insights into how algae utilize and fix available P resources. However, current APA quantification methods lack single cell resolution, while also being time- and reagent consuming. Microfluidics offers a promising cost-effective solution to these limitations, providing a platform for precise single-cell analysis. In this
study, a trap-based microfluidic device was integrated with a commercially available AP live stain to
study the single cell APA response of a model algae strain, Chlamydomonas reinhardtii, when exposed to different exogenous P levels. A three-step culture-starve-spike process was used to induce APA in cells cultured under two different basal P levels (1 and 21 mM). When challenged with different spiked P levels (ranging from 0.1-41 mM), C. reinhardtii cells demonstrated a highly heterogeneous APA response. Two-way ANOVA confirmed that this response is influenced by both spiked and basal P levels. Utilizing an unsupervised machine learning approach (HDBSCAN), distinct subpopulations of C. reinhardtii cells were identified exhibiting varying levels of APA at the single-cell level. These subpopulations encompass significant groups of individual cells with either notably high or low APA, contributing to the overall behavior of the cohorts. Considerable intrapopulation differences in APA were observed across cohorts with similar average behavior. For instance, while some cohorts exhibited a concentrated distribution around the overall average APA, others displayed subpopulations dispersed across a wider range of APA levels. This underscores the potential bias introduced by analyzing a small number of cells in bulk, which may skew results by overrepresenting extreme behavioral subpopulations. The findings if this
study highlight the need for analytical approaches that account for single cell heterogeneity in APA and demonstrate the utility of microfluidics as a well-suited means for such investigations. This
study illuminates the complexities of APA regulation at the single cell level, providing crucial insights that advance our understanding of algal phosphorus metabolism and environmental responses.