Quantifying microbial cell abundance in natural environments with taxonomic resolution remains a central challenge in microbial ecology. While epifluorescence microscopy reliably enumerates free-living cells, it lacks taxonomic specificity and is less suitable for quantifying cells in sediments, soils, and on surfaces. Furthermore, while most omics approaches profile microbial communities, they typically provide only relative abundances. Recent advances in quantitative metagenomics using single-copy marker genes and genomic internal standards enable taxon-specific abundance estimates in absolute units (e.g. haploid genome equivalents L-1). Here, we directly compared SYBR Green–based epifluorescence cell counts with spike-in metagenomic estimates derived from bacterial recA and archaeal radA gene abundances in seawater samples collected monthly at five depths (5 to 890 m) from the San Pedro Ocean Time-series (SPOT) during 2023–2024. Across all depths, quantitative metagenomic estimates of prokaryotic abundance closely matched SYBR Green–based counts (slope = 1.05; Pearson’s r = 0.85). In addition to validating total abundance, quantitative metagenomics provided high-resolution, taxon-specific estimates across depths and sampling dates. The strong agreement between microscopy cell counts and metagenomic estimates from unfractionated SPOT samples suggests that most prokaryotes were free-living rather than particle associated. These results validate quantitative metagenomics as a robust approach for estimating absolute prokaryotic abundance, establishing a framework for taxonomically resolved quantification in microbial ecology and ecosystem modeling.

Despite advances in de novo enzyme design, success has been largely limited to low-energy-barrier model reactions. Amide bonds such as those linking amino acids along the peptide backbone are stable for hundreds of years in neutral aqueous solution because of the high energy barrier to hydrolysis. Here we describe the de novo design of enzymes that use an activated cysteine nucleophile to hydrolyse the polypeptide backbone in a sequence-dependent manner, with a success rate of 13/69 = 19% and rate enhancements over the background reaction (kcat/kuncat) of up to 3 × 107. The designed proteases have folds that are very different from proteases in nature (TM score < 0.50), and six crystal structures are very close to the design models (Cα root mean squared deviation of <1.2 Å), highlighting the capacity for generalization and the accuracy of the design methodology. Experimental and computational analyses suggest that the remaining gap in activity to the most active native cysteine proteases arises from imperfections in active-site preorganization and substrate positioning. The designed proteases efficiently cleave their targets in mammalian cells, opening the door to a wide range of synthetic biology applications. De novo-designed enzymes use an activated cysteine nucleophile to hydrolyse polypeptide backbones in a sequence-dependent manner.

Precision fermentation is a key technology for the future sustainable production of food proteins, bulk ingredients, and high-value recombinant products. However, process efficiency is often limited by the coupling of biomass formation and recombinant protein synthesis, leading to substantial carbon losses into non-recyclable biomass. Particularly for cost-sensitive bulk products such as food proteins, economically viable production requires highly efficient and dynamically controllable bioprocesses. RNA thermometers (RNATs) offer a promising inducer-free strategy for temperature-dependent control of recombinant protein production and dynamic bioprocess regulation, with the potential to improve carbon efficiency and product yields. Results In this study, a fluorescent reporter protein (EmGFP) under the control of a fourU (4U) RNA thermometer was evaluated in E. coli BL21 across a temperature range of 25–40 °C. Temperature-dependent regulation enabled maximal recombinant protein production at 35 °C, resulting in a 2.6-fold higher specific productivity compared to the derepressed control. The strongest RNAT-mediated regulatory effect was observed at 35.5 °C, highlighting the relevance of precise temperature control for recombinant protein production. Evaluation of altered carbon partitioning revealed a substantially increased carbon allocation toward product formation, while carbon partitioning into biomass and CO2 remained largely unchanged. Furthermore, the product-per-acetate yield increased more than threefold at induction temperature, indicating controllable carbon partitioning and reveals potential for decoupling approaches of biomass formation from recombinant protein production. This study demonstrates the successful implementation of an RNAT-controlled recombinant expression system enabling temperature-dependent regulation of protein production in E. coli. Quantitative analysis revealed controllable carbon partitioning toward recombinant protein synthesis and defined target temperatures for future process induction and switching. In contrast to conventional inducer-based systems, RNAT-mediated regulation enables dynamic control without chemical additives, supporting more resource-efficient bioprocess operation. However, fully exploiting RNAT-based process control will require advanced model-based strategies integrating temperature and feeding regimes, particularly for cost-sensitive precision fermentation processes such as food protein production. Overall, RNATs represent promising molecular control elements for future high-efficiency precision fermentation processes.

The spatial organization of microbial communities is a critical determinant of host-microbe interactions, yet species-level mapping remains challenging due to high 16S rRNA sequence homology and spectral crosstalk in multiplexed fluorescence in situ hybridization (FISH). To address this challenge, we developed Cross-hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH, a pipeline that integrates strategic probe design with supervised machine learning. CIPHR-FISH transforms probe cross-hybridization, traditionally viewed as experimental noise, into informative molecular signatures. Using a gnotobiotic zebrafish model colonized with a defined mix of 10 zebrafish bacterial strains, we trained a support vector machine (SVM) on empirical hybridization patterns from pure bacterial cultures. CIPHR-FISH achieved 99.2 % macro-averaged accuracy, significantly outperforming standard linear unmixing (62.5 %), and successfully discriminated strains with 99.7% sequence homology. Applying this tool to gnotobiotic zebrafish larvae revealed distinct biogeographies: the intestinal bulb hosted highly structured, multi-layered polymicrobial aggregates, while the skin exhibited sparse, uniformly dispersed individual bacterial cells. Notably, we observed significant variation in spatial community structure that was obscured by traditional bulk 16S rRNA sequencing. CIPHR-FISH provides a robust framework for high-resolution spatial biology by converting the limitations of molecular labeling into a rich data source for taxonomic classification. This approach enables the quantification of micro-scale ecological and stochastic forces that shape the microbiome across hosts.