Droplet microfluidics is a core technology that powers high-throughput single cell sequencing. However, the current generation of single-cell microfluidics faces notable limitations, including cell aggregation, suboptimal on-chip reactions that compromise experimental outcomes and elevate background noise, as well as a dependence on costly commercial barcode beads. To address these challenges, we present MusTer, an integrated next-generation platform with multi-parametric singlet droplet sorting and triple-droplet merging capability. MusTer's multi-parametric singlet sorting module enables in-line droplet analysis of intrinsic fluorescence peak amplitude, width and interval from single-nucleus- (singlet) or multiple-nuclei (multiplet)-encapsulating droplets, subsequently allowing an effective separation of the singlet droplets from multiplet droplets and empty droplets. MusTer's triple-droplet merging module enables precise multi-step reactions, with each step performed under its own optimal conditions, thereby significantly enhancing experimental flexibility and efficiency. We validated MusTer's performance by performing single-cell ATAC-seq on maize leaves. The results demonstrate that MusTer significantly reduces the doublet rate, enhances the signal-to-noise ratio, and yields improved cell clustering compared with traditional methods. These results validate MusTer's capability to overcome key limitations in droplet-based single-cell analysis, effectively enhancing data quality and reliability, and also paves the way for its use in other challenging sample types and multi-step single-cell assays.

Temperature offers a simple yet powerful signal to program cellular behavior. Here, we engineered and characterized a set of temperature-dependent genetic circuits that integrate RNA thermometers with site-specific DNA recombinases to achieve precise, irreversible control of gene expression. Using the serine recombinase Bxb1 placed under the control of the Salmonella FourU RNA thermometer, we demonstrate how promoter strength critically defines thermal sensitivity: weak promoters’ activity clears ON/OFF transitions, while strong promoters lead to continuous, quasi-temperature-independent recombination. Furthermore, temperature pulse duration and growth phase of cell culture were found to modulate recombination efficiency, providing additional layers of control. We illustrate the potential of this framework through proof-of-concept applications, including (i) the generation of spatial expression patterns on 2D surfaces via localized heating, (ii) a paper-based device capable of recording temperature gradients as stable genetic outputs, and (iii) a temperature-triggered lysis system for controlled cellular release. Together, these results establish temperature-regulated recombinase circuits as versatile and robust tools for programmable, spatially resolved, and irreversible control of gene expression, paving the way for new applications in synthetic biology, biosensing, and bioproduction.

Design of experiments (DOE) principles are increasingly applied to biological assays, yet it remains unclear whether their foundational assumption, orthogonal decomposition, holds in nonlinear biological systems. We addressed this question using Perturb-seq as a case study. By benchmarking a design commonly used in Perturb-seq and related experiments against the orthogonal Plackett-Burman (PB) design via simulations, we uncovered a counter-intuitive phenomenon: while orthogonal designs generally excel, the multicollinearity inherent to the common design is functionally advantageous in systems with significant signal amplification. This challenges the blind application of DOE to biology. Based on these findings, we developed the PB suitability index (PBSI), a simple, parameter-free metric that predicts the optimal design solely from network structure. Our work not only provides practical guidelines for Perturb-seq but also establishes a "biology-oriented DOE" framework, bridging the gap between statistical rigor and biological complexity.

Microorganisms underpin global biogeochemical cycles and represent a vast, underexplored reservoir for sustainable biotechnology and human health. Their diversity, arising from the interplay of numerous genes within holistic genomic contexts, dictates complex ecophysiological traits across varied environments. To bridge the gap between complex genomic sequences and associated biological functions and phenotypic outcomes, we present MicroGenomer, a foundation model for transferable microbial genome representations enabling multi-scale genomic understanding and ecophysiological trait prediction. MicroGenomer leverages a hierarchical training strategy comprising pre-training on large-scale genomic sequences (234.5 billion base pairs), domain-specific mid-training using the GTDB-curated marker gene set, and task-specific post-training. Despite a streamlined architecture of 470 million parameters, MicroGenomer achieves performance on key tasks competitive with models nearly 85 times its size. Advancing beyond established gene-scale encoders, MicroGenomer generates robust embeddings at the genome scale for downstream modeling. Extensive evaluations demonstrate that MicroGenomer effectively captures phylogenetic structures in species space, excelling in gene- and genome-scale understanding as well as ecophysiological trait prediction. The practical utility of these capabilities is further demonstrated by targeted wet-lab validation on newly isolated strains, indicating that MicroGenomer's predictions provide reliable guidance for biological experiments. Collectively, MicroGenomer offers a high-performance, resource-efficient framework that transforms raw sequence data into actionable biological insights, providing a powerful foundation for microbiome research and biotechnology.

Sporulation is a strategy employed by many bacteria to survive harsh environmental conditions. The genus Paenibacillus includes spore-forming species notorious for spoiling pasteurized dairy products and for causing American foulbrood in honeybee larvae, leading to colony collapse. Human pathogens within Paenibacillus  are also a growing threat, causing fatal opportunistic infections. Here, we present a comprehensive survey of sporulation genes across 1,460 high-quality Paenibacillus genomes. We find that all members of the sporulation-initiating phosphorelay are well conserved, but that the Spo0B phosphotransferase contains a predicted transmembrane domain. We confirm that this domain localizes Spo0B to the cell membrane and therefore refer to this Spo0B variant as Spo0B-TM. Spo0B-TM is present in 92% of surveyed Paenibacillus genomes. Consistent with its high level of conservation, we find that the transmembrane domain is important for detecting its interaction with its phosphorelay partners Spo0A and Spo0F. Moreover, we find that Spo0B exhibits low sequence identity across Bacillota when compared with other members of the phosphorelay. Altogether, this work highlights the potential for diversity even within the highly conserved phosphorelay that initiates sporulation in Bacillota.

3'-5' translation (termed "backward translation") mediated by eukaryotic circular RNAs has been reported recently. Whether linear mRNA can produce proteins through backward translation remained elusive. Here, we demonstrate that linear mRNAs can mediate backward translation in vitro. Backward translation was detected in human cells transfected with expression vectors used to produce linear mRNAs. Importantly, synthetic linear mRNAs could produce backward translation signal in multiple in vitro translation systems, with a level comparable to that of conventional 5' to 3' forward translation. Furthermore, we demonstrated that a single translation initiating sequence (TIS) was able to drive backward and forward translation in an in vitro experimental setup, with the efficiency of backward translation found to be significant lower than that of forward translation. In summary, this study further reinforces the notion of translation flexibility, demonstrates a broader applicability of backward translation and heralds a new strategy in synthetic biology.

MicroRNAs (miRNAs) are key posttranscriptional regulators of gene expression that influence cancer initiation, progression, and therapeutic response. While most studies have focused on endogenous miRNAs, emerging evidence has highlighted the role of plant-derived miRNAs as exogenous dietary regulators capable of cross-kingdom gene modulation. This review summarises current knowledge regarding plant-derived miRNAs and their ability to regulate human cancer-related genes. Experimental findings indicate that plant miRNAs can withstand gastrointestinal digestion, enter the circulation, and regulate the expression of oncogenes, tumor suppressors, long noncoding RNAs, and immune checkpoint molecules via canonical RNA-induced silencing mechanisms. Specific examples include miR-156a, miR-159a-3p, miR-166a, miR-167e-5p, miR-171, miR-395e, miR-2911, miR-4995 and miR-5754, which exhibit anticancer activities across various cancer types and modulate key signalling pathways in mammalian cells, highlighting their potential as cross-kingdom regulators with therapeutic relevance. In addition to these characterised miRNAs, certain plant groups, which are rich in bioactive compounds, remain unexplored as sources of functional miRNAs, representing a promising avenue for future research. Collectively, these studies underscore the ability of plant-derived miRNAs to modulate mammalian gene expression and suggest their potential as diet-based or synthetic therapeutic agents. Further investigations into their bioavailability, target specificity, and functional relevance could inform innovative strategies for cancer prevention, integrating nutritional, molecular biological, and therapeutic approaches.

Solanoeclepin A (SolA) is a triterpenoid exuded from the roots of Solanaceae plants, originally identified as hatching stimulant for plant parasitic cyst nematodes. Assuming that evolution would have selected against the production of such a fitness lowering molecule, we postulated that SolA must serve another, beneficial, role for the plant. In this study, we demonstrate that nitrogen (N) deficiency strongly increases the SolA concentration in tomato root exudate. Moreover, SolA is produced only under non-sterile conditions, indicating that soil microbiota are involved in its production. Time-resolved RNAseq analysis revealed several candidate genes for SolA biosynthesis, which were all upregulated under N deficiency. Transient silencing of two SolA biosynthetic genes (e.g., CYP749A19 and CYP749A20) significantly reduced SolA production. Microbiome analysis on the rhizosphere of these plants demonstrated that the recruitment of beneficial Massilia spp. was inhibited in the transiently silenced plants. Isolation of a Massilia strain, identified as Massilia cellulosiltytica, allowed us to show that it has growth-promoting activity under N deficiency, likely via indole-3-acetic acid production and enhanced N acquisition. Root exudate of N-starved tomato displayed strong chemotactic activity towards this strain. Together, these findings demonstrate that SolA production under N deficiency relies on the interaction between tomato and soil microbiota that convert a plant-produced precursor that is a recruitment signal for beneficial, growth-promoting microbes to SolA, which was hijacked by cyst nematodes as a reliable host presence cue. This dual functionality resembles the microbial transformation of primary into secondary bile acids, important signalling molecules, in the gut of animals, and suggests convergent evolution in host microbe co-metabolism. Overall, our study positions SolA as a multifunctional signaling molecule in rhizosphere interactions, with potential application for enhancing crop resilience and sustainable pest management.

Coordinating multiple liquid handling robots is a complex logistical task when designing biological experiments. Protocol designers must consider the capabilities and constraints of each robot to distribute work optimally across multiple instruments. We developed an optimization framework that finds optimal liquid handling solutions that leverage an arbitrary number of robots. Our algorithm, called Pourfecto, abstracts the capabilities of each robot and their labware, allowing us to plan and schedule a wide range of biological experiments using commercial instruments and custom-built hardware. Pourfecto can optimize multiple objectives (minimum transfers, fewest reagents, fewest labware swaps) and scales to experiments with hundreds of thousands of liquid transfers.

Microbial consortia show promise for bioremediation of environmental pollution, but performance optimization and risk assessment remain challenging due to unculturable species and limitations of traditional biochemical and sequencing tools. This study demonstrates how a multi-omics approach can provide deeper insight into the performance and risks of using a model aerobic ammonia-oxidizing consortium under conditions representative of wastewater treatment. Long-read DNA sequencing recovered several high-quality genomes, revealing dominance by an unclassified Nitrosospira species with expected ammonia oxidation capabilities. Lower-abundance taxa with nitrogen cycling potential were also detected, though species-level identification was limited by poor taxonomic database representation. Multi-omics and nitrogen analyses showed shifts in community composition and nitrogen cycling activity when the consortium was grown along a redox gradient typical of wastewater. All cultures accumulated ammonia over 4 weeks, with only aerobic cultures reducing ammonia levels thereafter. The dominant Nitrosospira population declined in abundance and activity in aerobic cultures while shifting toward nitrogen reduction under anoxic conditions. This metabolic shift would not have been detected using amplicon sequencing alone. Multi-omics also supported risk assessment through detection of waterborne pathogens from the Legionella genus and other lineages harboring virulence genes resembling those from known pathogens. This study highlights the value of multi-omics for optimizing microbial consortia and assessing biosafety risks but also underscores challenges related to effective data analyses and the feasibility of risk assessment under realistic conditions. Addressing these challenges will be essential to support the broader adoption of multi-omics strategies by stakeholders working with microbial consortia across diverse environmental applications.