Biology Methods and Protocols最新文献

Oligonucleotide integrity is a critical quality attribute for many new therapeutic modalities. Current assays often measure attributes such as length using capillary electrophoresis or liquid chromatography. The length is then corroborated with sequencing data to ensure oligonucleotide quality. An orthogonal measure to these classical separations is to measure intact mass, which traditional mass spectrometry cannot. Herein, we report the use of mass photometry to directly measure RNA length using RNA ladders as a calibrant.

Regardless of origin and localization, macrophages are the major immune cells that maintain homeostasis in both healthy and diseased states. However, there is no consensus on the phenotypes, functions and fates of macrophages. Existing studies clarify macrophage biology from different biomedical research perspectives, but the heterogeneity of induction methods hinders reproducibility and comparability. To address this problem, we validated a novel generalized in vitro protocol for the induction of M2-like macrophages from mice and rats bone marrow mononuclear cells. Our approach improves reliability and cross-species applicability, providing a valuable tool for macrophage research.

In recent years, there has been a notable increasing interest surrounding the identification and quantification of nano-sized particles, including extracellular vesicles (EVs) and viruses. The challenge posed by the nano-sized dimension of these particles makes precise examination a significant undertaking. Among the different techniques for the accurate study of EVs, flow cytometry stands out as the ideal method. It is characterized by high sensitivity, low time consumption, non-destructive sampling, and high throughput. In this article, we propose the optimization of flow cytometry procedures to identify, quantify, and purify EVs and virus-like particles. The protocol aims to reduce artefacts and errors in nano-sized particles counting, overall caused by the swarming effect. Different threshold strategies were compared to ensure result specificity. Additionally, the critical parameters to consider when using conventional flow cytometry outside of the common experimental context of use have also been identified. Finally, fluorescent-EVs sorting protocol was also developed with highly reliable results using a conventional cell sorter.

CRISPR/Cas9-mediated homology-directed repair (HDR) allows precise gene editing, but its efficiency remains low for certain cell types, such as human induced pluripotent stem cells (hiPSCs). In this study, we aimed to introduce the CTNNA1: c.2023C>T (p.Q675*) genetic alteration, which is associated with Hereditary Diffuse Gastric Cancer, into hiPSCs using CRISPR/Cas9. We designed a single-guide RNA targeting the alteration site and a single-stranded oligonucleotide donor DNA template for HDR-based repair. Herein, we report the successful introduction of the CTNNA1: c.2023C>T homozygous alteration in one hiPSC line, which resulted in severe phenotypic changes, including impaired colony formation and cell proliferation. Additionally, we established a straightforward protocol to assess hiPSCs karyotype integrity, ensuring the chromosomal stability required for the gene-editing process. This protocol involves routine G-banding analysis that is required for regular quality controls during handling of hiPSCs. This study demonstrates an efficient approach to precisely edit hiPSCs by CRISPR/Cas9 and highlights the essential role of CTNNA1 expression in maintaining hiPSC viability. Our methodology provides a valuable framework for modeling disease-associated alterations in human-derived cellular models that can be reproduced for other genes and other types of cell lines.

Ontologies are highly prevalent in biology and medicine and are always evolving. Annotating biological text, such as observed phenotype descriptions, with ontology terms is a challenging and tedious task. The process of annotation requires a contextual understanding of the input text and of the ontological terms available. While text-mining tools are available to assist, they are largely based on directly matching words and phrases and so lack understanding of the meaning of the query item and of the ontology term labels. Large Language Models (LLMs), however, excel at tasks that require semantic understanding of input text and therefore may provide an improvement for the auto-annotation of text with ontological terms. Here we describe a series of workflows incorporating OpenAI GPT's capabilities to annotate Arabidopsis thaliana and forest tree phenotypic observations with ontology terms, aiming for results that resemble manually curated annotations. These workflows make use of an LLM to intelligently parse phenotypes into short concepts, followed by finding appropriate ontology terms via embedding vector similarity or via Retrieval-Augmented Generation (RAG). The RAG model is a state-of-the-art approach that augments conversational prompts to the LLM with context-specific data to empower it beyond its pre-trained parameter space. We show that the RAG produces the most accurate automated annotations that are often highly similar or identical to expert-curated annotations.

Natural killer (NK) cells have emerged as promising candidates for novel immunotherapy strategies against various malignancies. Their unique ability to recognize and eliminate tumour cells without prior sensitization, coupled with the secretion of pro-inflammatory cytokines such as interferon-gamma and tumour necrosis factor, position them as promising agents in cancer therapy. Adoptive NK cell transfer has shown particular promise in haematological malignancies, where NK cell infusions could achieve remission in a high proportion of patients. Moreover, the possibility to engineer NK cells to express chimeric antigen receptors can further enhance their efficacy, thereby broadening their applicability to include solid tumours. Ongoing research is crucial to optimize NK cell therapies and enhance their efficacy to expand their clinical applications. However, this research hinges on robust protocols and experimental methodology for the isolation, expansion, and genetic engineering of NK cells. In an attempt to set up a standardized protocol for NK cell isolation and expansion, we present a thoroughly tested and validated protocol that can produce highly pure, viable, and potent NK cells that can be used for research and development of NK cell therapies. The protocol is highly reproducible, closely aligned to comply with Good Manufacturing Practice regulations, and tested for scalability to produce NK cells at clinically relevant dosages to support the development of off-the-shelf NK products.

Subjective variability in human interpretation of diagnostic imaging presents significant clinical limitations, potentially resulting in diagnostic errors and increased healthcare costs. While artificial intelligence (AI) algorithms offer promising solutions to reduce interpreter subjectivity, they frequently demonstrate poor generalizability across different healthcare settings. To address these issues, we introduce Retrieval Augmented Medical Diagnosis System (RAMDS), which integrates an AI classification model with a similar image model. This approach retrieves historical cases and their diagnoses to provide context for the AI predictions. By weighing similar image diagnoses alongside AI predictions, RAMDS produces a final weighted prediction, aiding physicians in understanding the diagnosis process. Moreover, RAMDS does not require complete retraining when applied to new datasets; rather, it simply necessitates re-calibration of the weighing system. When RAMDS fine-tuned for negative predictive value was evaluated on breast ultrasounds for cancer classification, RAMDS improved sensitivity by 21% and negative predictive value by 9% compared to ResNet-34. Offering enhanced metrics, explainability, and adaptability, RAMDS represents a notable advancement in medical AI. RAMDS is a new approach in medical AI that has the potential for pan-pathological uses, though further research is needed to optimize its performance and integrate multimodal data.

Polysaccharide quantification plays a vital role in understanding ecological and nutritional processes in microbes, plants, and animals. Traditional methods typically hydrolyze these large molecules into monomers using chemical methods, but such approaches do not work for all polysaccharides. Enzymatic degradation is a promising alternative but typically requires the use of characterized recombinant enzymes or characterized microbial isolates that secrete enzymes. In this study, we introduce a versatile method that employs undefined enzyme cocktails secreted by individual microbes or complex environmental microbial communities for the hydrolysis of polysaccharides. We focus on colloidal chitin and laminarin as representative polysaccharides of ecological relevance. Our results demonstrate that colloidal chitin can be effectively digested with an enzyme cocktail derived from a chitin-degrading Psychromonas sp. isolate. Utilizing a 3,5-dinitrosalicylic acid reducing sugar assay or liquid chromatography-mass spectrometry for monomer and oligomer detection, we successfully determined chitin concentrations as low as 62 and 15 mg/l, respectively. This allows for effective monitoring of microbial chitin degradation. To extend the applicability of our method, we also leveraged complex, undefined microbial communities as sources of enzyme cocktails capable of degrading laminarin. With this approach, we achieved a detection limit of 30 mg/l laminarin through the reducing sugar assay. Our findings highlight the potential of utilizing enzyme cocktails from both individual microbes and, notably, from undefined microbial communities for polysaccharide quantification. This advancement addresses limitations associated with traditional chemical hydrolysis methods.

Glyoxalase I (Glo I) is an enzyme essential for detoxifying methylglyoxal, a toxic compound associated with advanced glycation end products. Given Glo I's multifaceted roles in various physiological and pathological processes, accurately measuring its activity is crucial for understanding its implications in metabolic disorders. The current assay utilizes 2,4-dinitrophenylhydrazine (2,4-DNPH) to measure Glo I activity. This reagent has previously been employed to evaluate a group of enzyme protocols. The procedure involves incubating Glo I enzyme samples in a controlled phosphate buffer at pH 6.6, optimizing conditions for enzymatic activity. Glutathione and methylglyoxal serve as substrates, with Glo I catalyzing the conversion of the hemithioacetal adduct into S-D-lactoylglutathione. Unreacted methylglyoxal is quantified by forming a colored hydrazone complex with 2,4-DNPH. The 2,4-DNPH method is rigorously validated for linearity, stability, resistance to interference, and sensitivity from several chemicals. It strongly correlates with the existing ultraviolet method, offering enhanced simplicity and cost-effectiveness. The protocol allows precise quantification of Glo I activity, with potential in research and diagnostics. Intra- and inter-day analyses confirm accuracy as percentage relative error, ensuring reliable measurement activity. The DNPH-Glo I method exhibited excellent sensitivity, with low limits of detection and quantification at 0.006 U/L and 0.018 U/L, respectively. This research provides valuable insights into the quantification of Glo I, highlighting significant implications for future studies in metabolic disorders and related health fields. This study contributes to a deeper understanding of its role in health and disease management by advancing the methods available for measuring Glo I activity.

Hymenolepis diminuta is a parasitic tapeworm that utilizes rats as hosts and offers advantages over human parasitic tapeworms and free-living flatworms as a model system to study the biology and pathology of helminth infections. H. diminuta is minimally infectious to humans, easy to maintain in the lab, demonstrates impressive growth, regeneration, and reproductive capabilities, and is amenable to loss-of-function manipulations. As an emerging model, tool development is critical to increasing the utility of this system. This study introduces a novel protocol for H. diminuta that combines fluorescent in situ hybridization (FISH) and 2'-Deoxy-2'-fluoro-5-ethynyluridine (F-ara-EdU) uptake and staining. Our protocol allows for the spatial detection of gene expression and simultaneous identification of proliferating cells. Dual labeling of F-ara-EdU and stem cell markers revealed a distinct expression pattern in different anatomical regions, especially in the head and neck. We demonstrate optimal labeling without permeabilization, streamlining the protocol. We also demonstrate generalizability using FISH for other tissue markers. The protocol was applied to perform bulk lineage tracing, revealing that stem cells can differentiate into neuronal and tegumental cells within 3 days. Our protocol provides an important tool in the arsenal for investigating gene expression and cell proliferation in H. diminuta, contributing valuable insights into the biology of parasitic tapeworms and potentially opening new avenues for the study of human parasitic tapeworms.