Phylogeny- or activity-based fluorescent labelling of microorganisms coupled with fluorescence activated cell sorting (FACS) is a powerful approach to rapidly isolate cells of interest from complex communities for targeted genomics. However, the method has not been widely adopted because standard paraformaldehyde or ethanol fixation procedures hamper nucleic acid recovery and/or long-term sample storage. Here, we present dithiobis(succinimidyl propionate) (DSP) as a reversible fixative for microbial cells that enables fluorescence imaging and DNA extraction and amplification for sequencing. DSP fixation is compatible with widely used fluorescence in situ hybridization (FISH) approaches and bioorthogonal non-canonical amino acid tagging, and can be applied to diverse sample types, where it consistently performed as well as paraformaldehyde. Furthermore, high-quality genomes were obtained from individual and multiple bacterial cells selectively extracted from complex samples using FISH-FACS. Thus, DSP is a dual-use fixative for microbiome imaging and sequencing, improving targeted microbial genomics.

DNA-PAINT enables super-resolution imaging with high multiplexing capacity, yet it is rarely used in nanoscale bacteriology. A likely reason is the lack of straightforward protocols and of systematic characterization of imager-docking strand pairs inside intact bacterial cells, whose envelope and dense cytoplasm differ markedly from both the DNA origami in vitro systems used to develop them, and eukaryotic cells. In this work, we establish a screening platform based on RNA-FISH, in which we extend the 16S rRNA-directed EUB338 probe with docking sequences of choice. This allows imager-docking pairs to be benchmarked directly inside Escherichia coli cells and characterized for parameters such as specificity, binding kinetics, and localization precision. We screened 12 different DNA-PAINT sequences and used an optimized set to perform four-target 3D Exchange-PAINT in single E. coli cells. We further applied our strategy to image three effector mRNAs in the Gram-negative pathogen Yersinia enterocolitica and quantified RNA clusters and their 3D distribution. Our work provides a framework and practical guidelines for high-quality 3D DNA-PAINT imaging in Gram-negative bacteria and should facilitate its broader adoption in nanoscale bacteriology.

Our ability to efficiently construct synthetic DNA products is essential to studying and engineering biology. There is an abundance of widely adopted DNA assembly technologies suitable for generating synthetic constructs from diverse DNA inputs, but these techniques suffer from low specificity of connection during assembly, which decreases the accuracy, efficiency, and throughput of construction. Here we propose a broadly applicable paradigm for DNA assembly, High Temperature (HighT) assembly, that lowers misconnection rates by ligating complementary single strand DNA overhangs at reaction temperatures significantly higher than their predicted melting temperatures. By expanding the concept of melting temperature to demarcate smaller association fractions of DNA duplexes, we characterize HighT and demonstrate 40 percentage points increase in accuracy, and 4 orders of magnitude increase in yield of transformants when applying the technique to conventional assembly technologies. HighT is then applied to develop cloning pipelines for the development of efficient assembly-to-integration protocols, the construction of specific low-abundance isoforms of medically relevant proteins from human gDNA and oligos, and the de novo construction of a 10 kb, GC rich genomic region from the functionally extinct Northern White Rhinoceros created entirely in vitro.

Fluorescence-based CRISPR diagnostic assays have become a popular platform for nucleic acid detection due to their programmability, configurability, specificity, and compatibility with standard laboratory equipment. However, reported enzymatic kinetic rates and limits of detection for CRISPR trans-cleavage assays vary by several orders of magnitude across the literature. This variation in performance parameters is coupled with, and exacerbated by, inconsistent calibration, incomplete correction of measurement biases, and nonstandardized or incomplete data analysis procedures. We present an experimental protocol and quantitative analysis framework for fluorescence-based enzyme assays using routine laboratory instrumentation, including thermocyclers and fluorescence microplate readers. Building on previous studies of CRISPR enzyme kinetics and fluorescence calibration, we describe procedures for flat-field and background correction; comprehensive fluorescence calibration, including correction for the inner filter effect; quantification and implications of reporter degradation; extraction of Michaelis–Menten kinetic parameters; and determination of assay's limits of detection. We provide step-by-step experimental guidelines and open-source Python implementations for each stage of the workflow. Using representative Cas12 trans-cleavage datasets, we demonstrate that explicit fluorescence calibration and correction procedures substantially reduce systematic bias in measured kinetic rates and improve consistency between experiments. Our framework aims to establish standardized practices for quantitative fluorescence-based CRISPR assays and provides researchers with practical tools for reproducible kinetic characterization and rational assay design.

With the rapid growth of plastic production, plastic pollution has become increasingly widespread and persistent in the environment. This persistent pollution poses potential risks to ecosystems and human health. Microbial biodegradation offers a promising approach to plastic waste management because it operates under mild reaction conditions and has potential for sustainable application. This review summarizes recent advances in the major microbial groups involved in plastic biodegradation, methods for assessing degradation efficiency, underlying mechanisms, and strategies for improving degradation performance. Assessment methods are discussed, from physicochemical characterization to biochemical validation. Meanwhile, it emphasizes that real plastic biodegradation and degradation efficiency require multiple complementary lines of evidence to judge in a more complete and objective way. The complete process and molecular mechanisms of microbial plastic degradation are then described, including microbial attachment and biofilm formation, enzymatic degradation, intracellular metabolism, and mineralization of degradation products. In addition, it summarizes various efficiency optimization strategies for degradation, including plastic pretreatment, bioaugmentation, and regulation of environmental parameters. The lack of standardized assessment, limited practical translation, and ecological risks are the identified key challenges. Finally, standardized efficiency evaluation strategies, plastic upcycling, and emerging technologies for subsequent development were discussed. This review aims to provide a reference for understanding microbial plastic degradation.

Microbial communities are the main drivers of global carbon cycling. Yet, predicting their respiratory responses to warming is challenging because the underlying mechanisms remain elusive. Here, we developed a spatially explicit model, integrating single-cell metabolism with metabolic exchange and endogenous heat production, to disentangle the mechanisms driving community-level temperature responses. We reveal that mutualism amplifies respiratory temperature sensitivity compared to isolated taxa, as exchanged metabolites sustain a secondary growth phase with higher respiratory demand, and this phase creates disproportionately large CO2; emissions under warming (apparent activation energy Ea; increases from 0.65 to 0.77 eV). Spatial structure amplifies this further through threshold effects, whereby diffusion limitation shifts from constraining to enabling mutualistic benefits as metabolic rates increase with temperature (Ea; increases to 1.18 ± 0.09 eV). Endogenous heat production drives additional amplification through positive feedback between local temperature rises and resource turnover rates (Ea; further increasing to 1.78 ± 0.14 eV). Collectively, our results demonstrate that community-level temperature responses are not determined by enzyme kinetics alone but emerge from the interplay of ecological, physical, and metabolic interactions. Our model provides a general framework for understanding the combined effects of the underlying mechanisms on microbial temperature responses. Future climate carbon models should incorporate these considerations to improve predictions.

Conventional bio-diesel synthesis is both chemical- and energy-intensive, whereas lipase-catalyzed transesterification provides a more sustainable alternative. Nevertheless, the industrial application of native Candida antarctica lipase B (CalB) is limited by its low catalytic activity and high production costs. To overcome these challenges, we developed a recombinant Komagataella phaffii expression platform to efficiently secrete recombinant CalB (rCalB). To the best of our knowledge, this study demonstrates a proof-of-concept platform that integrates CalB secretion through a dual-promoter expression system regulated by AOX1/GAP in PDI-assisted and magnetically immobilized K. phaffii cells for biodiesel production. This integrated platform enabled extracellular production of rCalB and magnetic recovery of the engineered yeast cells. The maximum biodiesel yield was reached after 96 h using a methanol-to-oil ratio of 4:1 and a catalyst amount of 8 mL (2.4 EU) at 30 °C. The Gas Chromatography Flame Ionization Detector analysis results indicated that the produced biodiesel had higher amounts of C16 and C18 fatty acid methyl esters. These results highlight the potential to produce biodiesel via transesterification catalyzed by rCalB secreted from magnetically immobilized yeast cells.

Animals colonizing food-limited environments need to overcome growth constraints by adapting to low, inconsistent nutritional inputs. Comparative studies of surface and cave populations of the Mexican tetra fish species Astyanax mexicanus provide a powerful system to explore evolution to food scarcity. In nutrient-scarce caves, evolved traits such as eye loss and increased fat storage have been shown to conserve energy in cavefish. However, the role of microbiota-mediated metabolism in sustaining growth and survival under food limitation remains poorly understood. Here, we demonstrate that surface and cave populations distinctly modulate their gut microbiota, with cavefish recruiting microbes whose predicted metabolites specifically promote growth under nutrient-limited conditions. Using microbiota data from three distinct dietary regimes and a fasting treatment, integrated topological data analysis and sparse modeling identify sugar and fatty acid degradation pathways coupled with growth in cavefish. Notably, the metabolites of these pathways are beneficial only for cavefish growth, and not for surface fish. Fasting in cavefish led to the recruitment of gut microbes capable of metabolizing aromatic compounds inaccessible to the host, whereas it recruited few microbial associates in surface fish. Our findings indicate that cavefish evolved novel host-microbiota coupling to sustain growth and resilience in their nutrient-scarce niche. We anticipate that our findings will broadly inform studies of evolution under extreme nutritional conditions and provide insights into the role of host-microbiota interactions in resilience to food scarcity at varied time scales.

Metabolic cross-feeding is central to microbial life. Theory predicts that spatial structure favors cross-feeding by affording cells preferential access to the exchanged metabolites. Yet recent evidence shows that spatial structure can, in some contexts, disfavor cross-feeding. Here, we resolve this contradiction by structuring cross-feeding interactions into three dimensions: (i) directionality, (ii) investment, and (iii) metabolic dependency. We then investigated their combined effects using a spatially explicit individual-based model. We find that the effect of spatial structure on cross-feeding is determined not by physical proximity alone, but by how microbial growth depends on exchanged metabolites. Metabolic dependence shapes the spatial organization of cells and metabolite fluxes, and this emergent organization, in turn, reshapes the ecological outcome of cross-feeding. Our results reconcile previously conflicting observations and provide a general framework for predicting and engineering metabolically interdependent microbial communities.

Programmable RNA sensors enable sequence-specific detection of RNA inputs for diagnostics, genetic circuit control, and multiplexed molecular readouts. Although readily programmable, their performance varies substantially with the target site selected within a transcript, making target-site selection a major challenge. Here, we report DeSTAR (Detectron Sensor TARget-site ranker), a machine-learning-assisted target-site ranking model for programmable RNA sensors. To train this model, we generated transcript-wide, single-nucleotide-resolution tiling sensor libraries across six diverse RNA targets, enabling generalization to be tested across transcripts rather than within a single RNA. In leave-one-transcript-out evaluation, DeSTAR's top-ranked sites were enriched for high-performing sensors in held-out transcripts, driven predominantly by interpretable sequence grammar in the sensor stem rather than transcript-specific patterns. Prospectively, on an unseen SARS-CoV-2 transcript, the model's top-ranked sites yielded working sensors roughly five-fold more often than random selection. These results support DeSTAR as a generalizable strategy for target-site selection in RNA sensors across transcripts.

tRNA are short, ~76 nucleotide-long, modification-rich molecules that play an important role in protein translation. Nanopore sequencing is the only platform capable of directly sequencing full-length, native tRNA molecules. This capability is enabled by splinted ligation of tRNA-specific adapters. As a result, the measured ionic current signal reflects a composition of both adapter and tRNA sequence features. Despite these advantages, analysis of nanopore tRNA sequencing data poses distinct computational challenges. Existing ionic current-based signal-to-sequence decoding (basecalling) models are optimized for mRNA and perform suboptimally on short, modification-dense RNA such as tRNA. Furthermore, alignment algorithms for reference-guided analysis of short-length reads are not optimized for nanopore reads with lower accuracy than Next-generation sequencing reads. To overcome these limitations, domain-specific computational approaches are required. We present tRNAZAP, a nanopore ionic current signal analysis toolkit designed to enable precise reference-guided sequence alignment. tRNAZAP operates directly on raw ionic current signals and decomposes the analysis into two complementary tasks: 1) segmentation of adapter and tRNA regions, and 2) tRNA classification, both implemented using transformer-based models. The segmentation component isolates the tRNA signal portion and extracts the corresponding nucleotide sequence as the alignment query, while the classification component identifies the tRNA type as the alignment reference without requiring sequence-level information. Together, these outputs allow direct optimal pairwise alignment between the tRNA subsequence of the full query and reference sequences. Trained on in vitro transcribed RNA data and evaluated on yeast tRNA, these models achieved ~98% classification accuracy and intersection-over-union of ~0.98 for segmentation.