The vaginal microbiome plays a crucial role in our health. The composition of this community can be classified into five community state types (CSTs), four of which are primarily consisted of Lactobacillus species and considered healthy, while the fifth features non-Lactobacillus populations and signifies a disease state termed Bacterial vaginosis (BV), which is associated with various symptoms and increased susceptibility to diseases. Importantly, however, the exact mechanisms and dynamics underlying BV development are not yet fully understood, including specifically possible routes from a healthy to a BV state. To address this gap, this study set out to characterize the progression from healthy- to BV-associated compositions by analyzing 8026 vaginal samples and using a manifold-detection framework. This approach, inspired by single-cell analysis, aims to identify low-dimensional trajectories in the high-dimensional composition space. It further orders samples along these trajectories and assigns a score (pseudo-time) to each analyzed or new sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV states for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. Several BV indicators can also be successfully predicted based on pseudo-time scores, and key taxa involved in BV development can be identified using this approach. Taken together, these findings demonstrate how manifold detection can be used to successfully characterize the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.

阴道微生物群对我们的健康起着至关重要的作用。该群落的组成可分为五种群落状态类型 (CST)，其中四种主要由乳杆菌属物种组成，被认为是健康的，而第五种则以非乳杆菌群为特征，表示一种称为细菌性阴道病 (BV) 的疾病状态，这与各种症状和疾病易感性增加有关。然而，重要的是，BV 发展的确切机制和动态尚未完全了解，包括从健康状态到 BV 状态的具体可能途径。为了解决这一差距，本研究通过分析 8026 个阴道样本并使用多种检测框架来表征从健康成分到 BV 相关成分的进展。这种方法受到单细胞分析的启发，旨在识别高维组成空间中的低维轨迹。它进一步沿着这些轨迹对样本进行排序，并根据每个分析样本或新样本与 BV 状态的接近程度为其分配分数（伪时间）。我们的结果揭示了每个 CST 的健康状态和 BV 状态之间的不同进展途径，伪时间得分与群落多样性相关并量化每个样本的健康状态。基于伪时间分数还可以成功预测一些 BV 指标，并且可以使用这种方法来识别 BV 发展中涉及的关键分类群。总而言之，这些发现证明了如何使用多种检测来成功表征从健康乳杆菌占优势的群体到 BV 的进展，并准确量化 BV 发展过程中新样本的健康状况。