Biology Methods and Protocols最新文献

Malaria, caused by Plasmodium spp. parasites (P. vivax, P. falciparum, P. ovale, P. malariae, and P. knowlesi), remains a significant global health challenge, with 263 million cases and 567 000 deaths reported in 2023. Diagnosis in endemic regions relies on clinical symptoms, microscopy, and rapid diagnostic tests. Although widely used, microscopy suffers from variability in sensitivity due to operator expertise and low parasitemia. Rapid diagnostic tests, which are favored for their simplicity and speed, show high sensitivity for P. vivax but reduced accuracy (80%) for P. falciparum, which is attributed to deletions in histidine-rich protein 2/3 proteins caused by Pfhrp2/3 gene mutations. Innovative diagnostic and therapeutic technologies, such as aptamers, are gaining attention. Aptamers are single-stranded oligonucleotides that bind specifically to target molecules with high affinity. They have shown promise in disease diagnosis, therapeutics, and environmental monitoring. In malaria, aptamers are being explored as highly sensitive and specific diagnostic tools capable of detecting Plasmodium proteins across all infection stages. Additionally, they offer potential for novel therapeutic strategies, enhancing disease control and treatment options. These advancements highlight the use of aptamers as versatile and innovative approaches for addressing malaria and other infectious diseases. A comprehensive literature search was conducted in the PubMed, ScienceDirect, and SCOPUS databases via the keywords "Aptamers" AND "Malaria" AND "Aptamers" AND "Plasmodium." Additionally, patent searches were carried out in the LENS, WIPO, and LATIPAT databases via the same search terms. In total, 88 relevant articles were selected for this review, providing a comprehensive and evidence-based foundation to discuss emerging aptamer technologies for malaria diagnosis and treatment. The proteins commonly employed in rapid malaria diagnostic tests, such as histidine-rich protein 2, P. lactate dehydrogenase, and prostaglandin dehydrogenase, are highlighted. However, the identification of new targets, such as HMIGB1 and DRX1 (1-deoxy-d-xylulose-5-phosphate reductoisomerase), and the detection of whole cells have also been emphasized.

The study of liver biology and pathology through marker expression analysis and tissue structure visualization is constrained by the high autofluorescence caused by the presence of lipofuscins, vitamin A, and lipid droplets, which traditional staining methods do not effectively quench. This leads to low signal-to-noise ratios, obscured expression levels, and reduced structural resolution. We mitigated liver tissue autofluorescence using Sudan Black B staining, which effectively quenches background signals from lipid and lipofuscin accumulation. Additionally, these protocols typically use thin paraffin sections (5-7 µm), which limit the analysis of larger and more complex liver structures. Liver tissue is highly organized in three dimensions, with large hepatocytes (20-30 µm in diameter) arranged around sinusoids and bile canaliculi, which form intricate branching networks. Thin sections cannot capture this 3D organization, providing only a "snapshot" of the tissue at one plane. Here, we present an optimized immunofluorescence protocol using 100-200 µm vibratome-cut liver sections to enable a more comprehensive 3D-like analysis of liver architecture. Finally, our protocol includes antigen retrieval steps tailored to each antibody, maximizing epitope accessibility and signal clarity. Together, these improvements provide a robust method for detailed liver studies with enhanced specificity and structural resolution in immunofluorescent staining. This protocol is particularly suited for researchers focused on liver regeneration, cancer, chronic disease pathology, and structural analysis. However, other researchers interested in exploring complex tissue structures in other autofluorescent tissues, such as the kidney, brain, pancreas, spleen, and adipose tissue, will also find this method beneficial.

Integrating genomics with diverse data modalities has the potential to revolutionize personalized medicine. However, this integration poses significant challenges due to the fundamental differences in data types and structures. The vast size of the genome necessitates transformation into a condensed representation containing key biomarkers and relevant features to ensure interoperability with other modalities. This commentary explores both conventional and state-of-the-art approaches to genome language modeling (GLM), with a focus on representing and extracting meaningful features from genomic sequences. We focus on the latest trends of applying language modeling techniques on genomics sequence data, treating it as a text modality. Effective feature extraction is essential in enabling machine learning models to effectively analyze large genomic datasets, particularly within multimodal frameworks. We first provide a step-by-step guide to various genomic sequence preprocessing and tokenization techniques. Then we explore feature extraction methods for the transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss machine learning (ML) applications in genomics, focusing on classification, regression, language processing algorithms, and multimodal integration. Additionally, we explore the role of GLM in functional annotation, emphasizing how advanced ML models, such as Bidirectional encoder representations from transformers, enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.

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.