BioTechniques最新文献

Introduction.- Affordable and accessible Deoxyribonucleic Acids (DNA) detection remains a major challenge in global healthcare because most molecular diagnostic techniques rely on chemical labels, complex reagents, and expensive equipment. Objective.- In this study, we investigate a fundamentally different approach based on Magnetic Induction Spectroscopy (MIS) to detect DNA concentrations in solution without labels, reagents, or physical contact. Methods.- Bulk electrical propertiess of DNA at multiple concentrations informed a three-dimensional electromagnetic simulation model. Results.- Multifrequency analysis revealed clear differences in the inductive phase shift as a function of DNA concentration, with maximum sensitivity near 1 MHz. Conclusions.- Simulated and extrapolated results indicate a potential detection range of 0.06-1000 μMol, encompassing typical post-PCR concentrations. The findings demonstrate modeling-based feasibility for concentration-dependent detection of bulk DNA.

Extracellular vesicles (EVs) are lipid bilayer-bound nanoparticles secreted by nearly all cells, with notable applications in intercellular communication and therapeutic delivery. However, conventional EV isolation techniques, such as ultracentrifugation and size exclusion chromatography, often face challenges like high cost, specialized equipment requirements, and low yield. In this study, we present a modified aqueous two-phase system (ATPS) as an alternative method for isolating EVs from raw bovine milk. This approach is scalable, cost-effective, and requires minimal specialized equipment, making it accessible for small laboratories. We demonstrate the efficiency of this method through the isolation and subsequent characterization of bovine milk-derived EVs, including morphological analysis, protein, and lipid quantification, and nucleic acid presence. The isolated EVs exhibited typical characteristics, morphology, specific protein markers (CD63 and TSG101), and a protein-to-lipid ratio consistent with extracellular vesicles. These findings validate the modified ATPS as a reliable and practical method for isolating milk-derived EVs, offering a promising tool for future research into their therapeutic potential and other applications.

In laboratory mice, the H2-L^d gene confers resistance to encephalitis upon infection with Toxoplasma gondii, a foodborne parasite capable of infecting all endotherm animals tested. H2-L^d is carried by inbred strains with major histocompatibility complex (MHC) haplotypes H-2^a (e.g., A/J) and H-2^d (e.g., BALB/c), but not by those with haplotypes H-2^b (e.g., C57BL/6) and H-2^k (e.g., C3H/He). As H2-L^d is typically detected by serological typing of ex vivo spleen samples, testing is usually terminal. Here we present a polymerase chain reaction (PCR)-based method to reliably detect the H2-L^d gene in ear biopsies from live mice, thus allowing researchers to ascertain the H2-L^d genotype for breeding or other in vivo procedures.

The genetic stability of recombinant CHO cell lines producing therapeutic proteins is critical for ensuring consistent quality in biopharma-ceutical products. Southern blotting remains the gold standard for evaluating transgene integrity and stability in these cell lines. In the biopharmaceutical industry, transposon-based expression systems are widely utilized to generate highly productive and genetically stable CHO cell lines. However, evaluating transgene integration sites and integrity in such cell lines is challenging with standard Southern blotting protocols. This difficulty arises because transposon-mediated transfection often results in multiple independent integration sites in the host genome, each typically harboring a single transgene copy. Upon restriction enzyme digestion, similar-sized DNA fragments are generated, reducing resolution and complicating the separation and detection of the transgenes using standard blotting protocols. Here, we present a modified Southern blotting protocol that significantly improves the resolution of integration banding patterns by refining key steps, including purification of digested DNA prior to electrophoresis and an enhanced DNA transfer method. This protocol was successfully applied to analyze multiple transposon-derived CHO cell lines with high transgene copy numbers, enabling more precise and efficient detection of transgene integration.

RNA interference (RNAi) screening is widely used for systematic gene perturbation, but reproducible readouts depend on careful assay design and consistent performance. Here, we describe a plate-based small interfering RNA (siRNA) screening workflow that combines repeated green fluorescent protein (GFP) fluorescence measurements (0-72 h) with an orthogonal endpoint XTT metabolic readout performed in the same wells. Using a medium-scale siRNA library and technical replicate plates, we establish a quality control (QC)-guided framework for evaluating assay performance, providing a robust basis for downstream gene-level hit identification. Complementary metrics, including coefficient of variation (CV), strictly standardized mean difference (SSMD), and Spearman rank correlation, were used to assess variability, control separation, and replicate concordance across plates and time points. These analyses revealed improved assay performance at later time points, with increased control separation and reproducibility, supporting the selection of 72 h as the most robust endpoint for downstream comparisons in the present screening workflow. Together, the workflow and QC framework provide a transferable strategy for qualifying plate-based RNAi screening experiments, enabling reliable downstream hit selection and follow-up studies.

Mitochondria, with their own DNA, Represent a potential target for nucleic acid-based precision therapies. However, effective delivery of therapeutic oligonucleotides remains challenging due to the dual mitochondrial membranes and the localization of mitochondrial DNA within nucleoid complexes in the matrix. To understand the delivery process and assess the delivery efficiency of potential vectors, such as dendrimers, it is essential to effectively quantify the oligonucleotides that are successfully delivered to and remain within mitochondria. Currently, there are only limited yet inconvenient methods available for this purpose. Here, we describe a method for quantifying the delivery of fluorescent oligonucleotide cargos in isolated mitochondria using a microfiltration apparatus for reliable fluorescent analysis. By working within a range of dilutions, we are able to safeguard the concentration limits. The quantification protocol also enables the visualization of specific localization within mitochondria, allowing for the determination of whether delivery can occur across both membranes. This is particularly useful, as it offers a key insight into improving vectors as they must deliver the cargoes within the mitochondrial matrix. We validate this method in this proof-of-concept study, providing biological data to assess the difference between two amphiphilic dendrimer vectors for oligonucleotide delivery in mitochondria.

Antibiotic resistance genes (ARGs) are important contaminants in water systems, and their detection depends strongly on methodological sensitivity. This study compared three molecular platforms, high-throughput quantitative polymerase chain reaction (HT-qPCR), hydrolysis probe-based qPCR, and droplet digital PCR (ddPCR), for detecting ARGs in wastewater, river water, and seawater in Thailand. HT-qPCR enabled broad resistome profiling, detecting 325-336 ARGs out of 373 targets (87.1-90.1%), with aminoglycoside, beta-lactam, macrolide-lincosamide-streptogramin B, sulfonamide, mobile genetic element, and integron genes most prevalent. qPCR quantified selected ARGs using standard curves constructed from plasmid standards whose absolute copy numbers were calibrated by ddPCR, yielding high efficiency and strong linearity. ddPCR detected the same target genes as qPCR with comparable concentration estimates and additionally identified bla_IND, which was not observed by HT-qPCR or qPCR. Quantifications from qPCR and ddPCR showed high rank-based concordance across matrices. Combining HT-qPCR with qPCR improved coverage by about 1% relative to HT-qPCR alone, while HT-qPCR with ddPCR offered nearly identical values. In conclusion, HT-qPCR was most effective for comprehensive profiling, qPCR ensured reliable quantification, and ddPCR enabled detection of rare targets, supporting a tiered and cost-effective framework for smart ARG monitoring in tropical aquatic environments.

In the past decade, we have witnessed an unprecedented growth of artificial intelligence (AI) techniques for high-resolution microscopy images. More importantly, we are observing a paradigm evolution of these deep learning (DL) methods, from a post-hoc analysis tool to nowadays an essential component in imaging-based biological researches. Building on the foundational success of DL in segmentation, we hope to elucidate in this review how AI is making an era of automated biological discovery embedded throughout the research cycle beyond segmentation. We structure our discussion along different research stages, from assay development, image acquisition, image and data analysis, to biological modeling and interpretation. First, in assay design and image acquisition, we illustrate how integrating DL-based computational strategies at the experimental stage enables efficient and information-rich assay designs, which may not even be possible before in conventional settings. Next, we examine image analysis, highlighting the transition from handcrafted features to the automated discovery of complex phenotypes and dynamic behaviors. Subsequently, we explore interpretation and modeling, discussing how AI facilitates extraction of biological insights from quantitative data. At the end, we discuss the importance of and current efforts in model evaluation and validation, and the rising of closed-loop microscopy concept.