With the growing demand for accurate yet effortless health monitoring at home, most current approaches to stool analysis rely on self-reported diaries that are prone to recall bias and low adherence. Here we present a fully passive alternative: the Precision Health Integrated Diagnostic (PHIND) system, a smart-toilet-based platform that enables automated defecation monitoring without requiring users to alter their daily routines. By integrating optical and pressure sensors with cloud-based convolutional neural networks, the PHIND system classifies stool form according to the Bristol Stool Form Scale and records key defecatory parameters, including total event time, defecation duration and time to first stool drop. The protocol proceeds in three principal stages: (1) assembling and mounting the hardware onto a conventional toilet; (2) training convolutional neural network models for stool classification and event detection; and (3) image acquisition and deploying cloud infrastructure for real-time analysis, data storage and visualization. Compared with traditional methods that depend on user-reported stool diaries, PHIND provides objective, near real-time data free from recall error, enabling more reliable early detection and long-term management of gastrointestinal conditions. Researchers and clinicians can expect high classification accuracy and robust, longitudinal insights into defecation patterns. The complete protocol-from hardware setup to system validation-can typically be completed within 2 d, excluding printed circuit board manufacturing, which generally requires up to 15 d depending on the manufacturing provider.

Constraint-based modeling can mechanistically simulate the behavior of a biochemical system, permitting hypothesis generation, experimental design and interpretation of experimental data, with numerous applications, especially the modeling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in each context. However, the existing model extraction algorithms are unable to ensure that a context-specific model is thermodynamically flux consistent. Here we introduce XomicsToModel, a semiautomated pipeline that integrates bibliomic, transcriptomic, proteomic and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. One of the key advantages of the XomicsToModel pipeline is its ability to seamlessly incorporate omics data into metabolic reconstructions, ensuring not only mechanistic accuracy but also physicochemical consistency. This functionality enables more accurate metabolic simulations and predictions across different biological contexts, enhancing its utility in diverse research fields, including systems biology, drug development and personalized medicine. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model; it enables omics data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. It can be implemented by anyone who has basic MATLAB programming skills and the fundamentals of constraint-based modeling.

Bioinspired in-sensor computing devices can process information at sensory terminals by leveraging physical principles, thereby reducing latency and energy consumption during computation while simultaneously enhancing the efficiency of data processing and real-time analysis. Optoelectronic devices exhibit in-sensor computing functions, such as feature enhancement and data compression, by tuning the defect states of the semiconductor channels and thereby modulating the photoresponsivity and time constants of the sensors. These functionalities are critically dependent on precise fabrication and testing protocols. Here we present a detailed procedure for fabricating and characterizing in-sensor computing devices based on nanoscale semiconductor thin films. We explain how to test such optoelectronic devices, including the testing of visual adaptation and motion perception responses. When using semiconductor materials obtained from commercial suppliers, this procedure is time efficient and results in highly reproducible device performance. Nevertheless, all device fabrication and testing steps are generalizable and can be extended to other semiconductor thin films grown using different methods. The procedure is intended for researchers experienced in cleanroom operations and microfabrication techniques and can be completed in ~14 d. The use of bioinspired optoelectronic devices enables the development of a framework for advancing in-sensor computing technologies.

Methanogenic archaea play a crucial role in the global carbon cycle and in climate change. Recent metagenomic sequencing has revealed a considerable number of (putative) H2-dependent methylotrophic methanogens (HMMs) across the archaeal tree and in diverse environments. Traditional isolation methods, such as dilution-to-extinction and roll-tube techniques, fail to cultivate fastidious HMMs. Here, we describe a four-stage isolation strategy designed to selectively isolate HMMs by using a flexible combination of methods to systematically reduce microbial complexity to a pure culture. In the initial stage, the growth conditions for the target HMM were optimized through closed-batch cultivation encompassing >50 conditions. Second, HMM-containing cultures were serially diluted in 96-well plates combined with substrate limitation to eliminate non-target archaea. In stage 3, the bacterial diversity in the culture was further decreased to a single bacterium by treatment with antibiotics and lysozyme. Finally, a last bacterial contaminant was removed by repeated addition of antibiotic mixtures and successive dilution transfers, leading to the successful isolation of the first pure culture of Methanosuratincola petrocarbonis LWZ-6, an HMM of the phylum Thermoproteota. This protocol also describes molecular methods, including 16S rRNA gene amplicon sequencing, metagenome sequencing and quantitative PCR, to track microbial community shifts and assess the growth advantage of the target HMM, enabling monitoring of the stepwise elimination of non-target microorganisms and ultimately confirming the purification of the target HMM. The duration of the protocol will vary for different HMMs depending on their substrate utilization, growth rate and method selection.

Projection-based three-dimensional bioprinting offers an approach for manufacturing biomimetic tissues with complex spatial structures and bioactivity, presenting potential for creating implantable organs or organoids to test drug response. Nevertheless, the extended printing times required for organ-scale manufacturing represents a challenge. Here we provide step-by-step instructions to manufacture organ-scale structures using bioinks while preserving high bioactivity. This approach incorporates Ficoll 400 to mitigate the heterogeneity of bioink with respect to refractive index and density, while 4-(2-aminoethyl)benzenesulfonyl fluoride and oil-sealing ensure the stability of the bioink components, thereby allowing extended printing times. This procedure also enables high-cell-viability printing via the calibration of the pH value of the bioink. This Protocol is appropriate for users with basic laboratory skills and fundamental knowledge in biotechnology to manufacture organ-scale structures for utilization in a wide variety of experimental designs. The approach is generalizable, as demonstrated by the successful printing of corpora cavernosa structures with a cell density of 10 million per milliliter, measuring 10 mm × 10 mm × 10 mm. After 7 d of culture, the cell viability was measured at 82.5%, highlighting the potential applicability in tissue engineering. All bioink preparation and printing steps are expected to take 5 h, while the development of printed structures requires 7 d of continuous culture.

Atomic-level manufacturing enables the bottom-up fabrication of nanomaterials with tailored structures and properties. Clusters with atomic precise structures can be used as superatom building blocks to construct superstructures with exceptional properties beyond their individual properties. However, the programmable and large-scale synthesis of cluster assemblies remains challenging. This protocol describes the detailed experimental procedures for the modular assembly of polyoxometalate (POM) clusters into subnanomaterials by programmable interactions under simple and mild conditions. In this approach different types of POM clusters (0.7-1.8 nm in size) are coated with quaternary ammonium or oleylamine ligands using either two-phase or solvothermal methods. The assembly process depends on the interactions between atom clusters, ligands and the reaction matrix, all of which can be modified to generate a library of subnanometer superstructures. The four intercluster connection modes are metal cation-induced coordinative connection, anion bridged covalent connection, synergistic noncovalent interaction and cluster-nucleus co-assembly. A library that includes single-cluster nanowires, clusterphenes and nanosheets with single-cluster thicknesses, can be prepared within 3-12 h. Owing to their ultrahigh surface atom ratio and electron delocalization, the resulting subnanometer POM assemblies with rich structural and compositional diversity exhibit excellent properties and application potential in terms of mechanics, catalysis and chirality. This procedure is suitable for users with prior expertise in the synthesis of inorganic and cluster-based nanomaterials.

RNA polyadenylation is crucial for RNA maturation, stability and function, with poly(A) tail lengths significantly influencing mRNA translation, efficiency and decay. Here, we provide a step-by-step protocol to perform Nanopore 3' end-capture sequencing (Nano3P-seq), a nanopore-based cDNA sequencing method to simultaneously capture RNA abundances and tail-composition and tail-length estimates at single-molecule resolution. Taking advantage of a template switching-based protocol, Nano3P-seq can sequence any RNA-derived molecule from its 3' end, regardless of its polyadenylation status, without the need for PCR amplification or RNA adapter ligation. We provide an updated Nano3P-seq protocol that is compatible with R10.4 flow cells, as well as compatible software for poly(A) tail length and content prediction, which we term 'PolyTailor'. We demonstrate that PolyTailor provides accurate estimates of transcript abundances and tail lengths and composition, while capturing both coding and noncoding RNA biotypes, including mRNAs, small nucleolar RNAs and ribosomal RNAs. Nano3P-seq can be applied to RNA samples prepared by using different methods (e.g., poly(A)-selected, ribodepleted or total RNA) and can be completed in 1 day. The protocol requires experience in molecular biology techniques and nanopore sequencing library preparation and basic knowledge of Linux Bash syntax and R programming. This protocol makes Nano3P-seq accessible and easy to implement by future users aiming to study the tail dynamics and heterogeneity of both coding and noncoding transcriptomes in a comprehensive and reproducible manner.

Assembling and upscaling biomolecular activity to perform work in man-made devices is a challenge in synthetic biology. Here we report the step-by-step process to construct fully protein-based micro-three-dimensional (3D) printed robotic structures, which are coated with and actuated by a minimal actomyosin cortex. This approach can be used to program self-powered soft robots assembled from multiple biomolecular modules, devising biophysical assays to quantify active forces produced in 3D and engineering smart 3D microchips for synthetic cell assembly. The procedure covers the establishment of 3D printing microstructures from protein materials, the assembly of actomyosin-based active coatings and the robotic structure design and characterization. The detailed step-by-step instructions will guide scientists in replicating the preparation procedures, facilitating the adoption of biomolecular microrobots and the development of 3D protein-based robotic technology and their applications. The procedure is suited for users with expertise in biomaterials and requires 15 d to complete.