MATRIX: rapid quantification of total and active microbial cells with single-cell phenotypes for environmental microbiomes

Quantifying the abundance and activity of bacteria within populations and communities is fundamental to systems microbiology and microbiome research. Yet direct microscopic cell counting remains low throughput, labor-intensive, and prone to user variability, leading many researchers to rely on indirect proxies such as optical density or multicopy marker-gene quantification. These indirect approaches do not distinguish between active and inactive cells and can obscure ecological interpretation. Here, we introduce m icrobial a ctivity and t otal cell quantification via r apid i maging and e x traction (MATRIX), an efficient workflow that integrates sample extraction, fluorescence staining, microscopy and automated image analysis, and Bayesian statistical inference to quantify total and redox-active cells and derive single-cell measurements for environmental bacterial populations and communities. We demonstrate its reproducibility and versatility using both cultured isolates and high-diversity soil communities. The resulting quantitative, phenotypic data sets provide rapid, direct measurements of bacterial population and community size and activity, enabling well-powered analyses that strengthen mechanistic insight into microbial responses and improve the ecological grounding of microbiome studies. IMPORTANCE Microbiome studies commonly rely on relative abundance data, which cannot distinguish whether compositional shifts reflect true population growth, declines in total community size, or both. Without explicit measurements of population and community sizes, the mechanistic interpretation of microbiome dynamics remains incomplete. Here, we present a rapid, throughput workflow, m icrobial a ctivity and t otal cell quantification via r apid i maging and e x traction (MATRIX), that quantifies both total and redox‑active bacterial cells from environmental samples. By integrating single‑cell phenotypes with community‑level metrics, this approach anchors microbiome data sets in direct ecological accounting rather than proxies. These measurements can clarify whether the observed changes in community structure represent shifts in abundance, activity, or both, improving inference about microbial responses to stress or environmental change. MATRIX offers an efficient way to incorporate quantitative ecology into systems microbiology and microbiome studies and to strengthen the link between microbial cellular physiology, community dynamics, and ecosystem function.