Methods最新文献

This study presents a workflow for rapid production and functional characterization of antibody fragments using an E. coli-based cell-free protein synthesis (CFPS) system. We quantified binding interactions by fluorescence correlation spectroscopy (FCS), which enabled determination of dissociation constants (K_D) between antibodies and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. As an initial validation, two conventionally expressed anti-RBD antibodies were analyzed to establish the reliability of the FCS-based binding measurements. To enable efficient cell-free production, strategies were developed to improve the solubility and yield of single-chain variable fragments (scFvs), including implementation of an established two-stage refolding workflow adapted for CFPS-derived proteins. This approach enabled recovery of functional scFvs from insoluble fractions. In addition, Fab fragments were successfully produced and characterized, with binding measurements confirming retention of antigen recognition. Together, these results demonstrate that CFPS can be combined with FCS to enable rapid, solution-phase evaluation of antibody fragment binding without reliance on conventional cell-based expression systems. This integrated platform provides a scalable approach for antibody screening and characterization, with potential applications in therapeutic antibody development and high-throughput discovery.

Multiparametric flow cytometry (MFC) data analyses still largely rely on expert-dependent subjective manual gating strategies. While unsupervised clustering methods have improved data exploitation, no formal probabilistic decisional framework has yet been proposed for the critical subsequent step of cell subset assignment and quantitation. LBC-Flow (patent pending) is a structured naïve Gaussian Bayesian decisional framework built upon FlowSOM clustering. Unlike black-box machine learning approaches, every classification decision is fully interpretable, explicit and mathematically justified by its posterior probability value. A reference model including all cell distribution parameters must be constructed from normal body fluid, enabling the computation of posterior probabilities for each cell. Cells failing to meet predefined acceptance thresholds are flagged as a new specific class of Non-Classifiable Events (NCE). A leukocyte differential panel was used as proof-of-concept on 36 blood samples. Five analytical strategies were compared: manual gating, FlowSOM-only quantification, and three Bayesian classification approaches-(i.e. two discrete based on nodes (BDN) or cells (BDC) and one gaussian based on cells (BCGC)-. Using intraclass correlation coefficient ICC(3,1) and Z score for comparisons, BCGC demonstrated better performance than the other methods particularly for rare subsets. Complete results were delivered for 26 identified leukocyte subpopulations and NCE, in less than 30 s per sample. LBC-Flow is an original formal Bayesian decisional framework, explicitly addressing the gap between unsupervised clustering output and reproducible cell subset assignment and quantitation. Panel-agnostic by design, this approach provides a methodological foundation to be tested with other flow cytometry datasets in clinical or research contexts.

Nucleoside analogue-based RNA labelling provides powerful approaches for investigating RNA synthesis, turnover, and post-transcriptional regulation. However, conventional detection workflows often require chemical treatment, reagent removal, and repeated RNA purification, which can cause substantial sample loss and limit their application to ultralow-input samples. Here, we present a chemical conversion-coupled RT-PCR method that eliminates post-conversion RNA purification for the detection and quantification of N⁶-allyladenosine (a⁶A)-labelled RNA. Iodine-induced cyclization converts a⁶A into a cyclized adenosine derivative that generates characteristic cDNA mutation signatures during reverse transcription. By eliminating iodine removal, alkaline stabilization, and post-conversion RNA purification, the workflow enables reverse transcription directly from the chemical reaction mixture and reduces handling steps that may otherwise cause sample loss. Optimization of iodine treatment and reverse transcriptase compatibility identified Induro reverse transcriptase as suitable for quantitative analysis because it combined a high mutation rate with the highest estimated read-through efficiency across cyclized a⁶A sites. The method detected a⁶A-labelled RNA from femtogram-level inputs and supported mutation-rate-based quantification across a broad input range. Its applicability was further demonstrated by time-resolved tracking of IVT-generated mRNA carrying different poly(A) tails using ultralow amounts of total RNA. This workflow provides a sensitive platform for the detection and relative quantification of a⁶A-labelled RNA when sample availability is limited.

In the era of miniaturization, the advent of sensors and chips is rapidly increasing for systematic investigation, monitoring and diagnosing unprecedented diseases in preclinical and clinical research. Early-stage detection demands a miniaturized platform/ artificial model in clinical laboratories and healthcare centers. This enables us to investigate and study the physiological processes of the samples and the drug's efficacy on them in a cost-effective and time-efficient manner. The technology of chip systems is rapidly evolving in an attempt to bridge the gaps between preclinical and clinical studies, as well as their wide applications in research laboratories in low-resource settings. Emerging research is focusing on 3D cell culture in microfluidic devices in order to facilitate the uniform distribution of nutrients and in-vitro investigation for assessing various biological processes and phenomena due to limitations in conventional 2D cell culture. It is crucial to understand extensively the significant parameters associated with the development of microfluidic chips for culturing and forming uniform 3D cell masses. The goal of this review is to highlight the various on-chip models that offer precise control over the size of 3D aggregates and their respective microenvironment, making them suitable for drug screening and delivery in therapeutic applications. Additionally, the manuscript discusses the design considerations and feasible microfabrication techniques for generating tumor models, organoids, or organ-on-chips, and the respective computational parameters that govern nutrient flow and permeation, growth conditions, and the microenvironment, cultivating the state-of-the-art microfluidic chips for 3D cell culture.