Biology Methods and Protocols最新文献

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

Mapping protein interaction complexes in their natural state in vivo is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an in vitro pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.

LINE-1 belongs to a family of DNA elements that move to new locations in the genome in a process called "retrotransposition." This is achieved by a copy-and-paste mechanism with the aid of an RNA intermediate. The full-length LINE-1 is responsible for most retrotransposition activity in the human genome. Detecting the active LINE-1 RNA at the endogenous level is challenging due to its small percentage among inactive copies and its different forms of transcripts. Here, we describe a method of designing RNA probes to detect active LINE-1 by northern blotting and use optimized conditions and tools to make the detection practical. This method uses a classical long RNA probe and provides an alternative way to detect LINE-1 RNA using multiple short RNA probes.

Molecular techniques that recover unknown sequences next to a known sequence region have been widely applied in various molecular studies, such as chromosome walking, identification of the insertion site of transposon mutagenesis, fusion gene partner, and chromosomal breakpoints, as well as targeted sequencing library preparation. Although various techniques have been introduced for efficiency enhancement, searching for relevant single molecular event present in a large-sized genome remains challenging. Here, the optimized ligation-mediated polymerase chain reaction (PCR) method was developed and successfully identified chromosomal breakpoints far away from the exon of the new exon junction without the need for nested PCR. In addition to recovering unknown sequences next to a known sequence region, the high efficiency of the method could also improve the performance of targeted  next-generation sequencing (NGS).

Picrosirius red staining constitutes an important and broadly used tool to visualize collagen and fibrosis in various tissues. Although multiple qualitative and quantitative analysis methods to evaluate fibrosis are available, many require specialized devices and software or lack objectivity and scalability. Here, we aimed to develop a versatile and powerful "QuantSeg" macro in the FIJI image processing software capable of automated, robust, and quick collagen quantification in cardiac tissue from light micrographs. To examine different patterns of fibrosis, an optional segmentation algorithm was implemented. To ensure the method's validity, we quantified the collagen content in a set of wild-type versus plakoglobin-knockout murine hearts exhibiting extensive fibrosis using both the macro and an established, fluorescence microscopy-based method, and compared results. To demonstrate the capabilities of the segmentation feature, rat hearts were examined post-myocardial infarction. We found the QuantSeg macro to robustly detect the differences in fibrosis between knockout and control hearts. In sections with low collagen content, the macro yielded more consistent results than using the fluorescence microscopy-based technique. With its wide range of output parameters, ease of use, cost effectiveness, and objectivity, the QuantSeg macro has the potential to become an established method for analysis of PSR-stained tissue. The novel segmentation feature allows for automated evaluation of different patterns of cardiac fibrosis for the first time.

[This corrects the article DOI: 10.1093/biomethods/bpae015.].

Cell replacement in aganglionic intestines is a promising, yet merely experimental tool for the therapy of congenital dysganglionosis of the enteric nervous system like Hirschsprung disease. While the injection of single cells or neurospheres to a defined and very restricted location is trivial, the translation to the clinical application, where large aganglionic or hypoganglionic areas need to be colonized (hundreds of square centimetres), afford a homogeneous distribution of multiple neurospheres all over the affected tissue areas. Reaching the entire aganglionic area in vivo is critical for the restoration of peristaltic function. The latter mainly depends on an intact nervous system that extends throughout the organ. Intra-arterial injection is a common method in cell therapy and may be the key to delivering cells or neurospheres into the capillary bed of the colon with area-wide distribution. We describe an experimental method for monitoring the distribution of a defined number of neurospheres into porcine recta ex vivo, immediately after intra-arterial injection. We designed this method to localize grafting sites of single neurospheres in precise biopsies which can further be examined in explant cultures. The isolated perfused porcine rectum allowed us to continuously monitor the perfusion pressure. A blockage of too many capillaries would lead to an ischaemic situation and an increase of perfusion pressure. Since we could demonstrate that the area-wide delivery of neurospheres did not alter the overall vascular resistance, we showed that the delivery does not significantly impair the local circulation.

Organoid generation from pluripotent stem cells is a cutting-edge technique that has created new possibilities for modelling human organs in vitro, as well as opening avenues for regenerative medicine. Here, we present a protocol for generating skin organoids (SKOs) from human-induced pluripotent stem cells (hiPSCs) via direct embryoid body formation. This method provides a consistent start point for hiPSC differentiation, resulting in SKOs with complex skin architecture and appendages (e.g. hair follicles, sebaceous glands, etc.) across hiPSC lines from two different somatic sources.

Age-related lens opacification (cataract) remains the leading cause of visual impairment and blindness worldwide. In low- and middle-income countries, utilization of cataract surgical services is often limited despite community-based outreach programmes. Community-led research, whereby researchers and community members collaboratively co-design intervention is an approach that ensures the interventions are locally relevant and that their implementation is feasible and socially accepted in the targeted contexts. Community-led interventions have the potential to increase cataract surgery uptake if done appropriately. In this study, once the intervention is co-designed it will be implemented through a cluster-randomized controlled trial (cRCT) with ward as a unit of randomization. This study will utilise both the qualitative methods for co-designing the intervention and the quantitative methods for effective assessment of the developed community-led intervention through a cRCT in 80 rural wards of Dodoma region, Tanzania (40 Intervention). The 'intervention package' will be developed through participatory community meetings and ongoing evaluation and modification of the intervention based on its impact on service utilization. Leask's four stages of intervention co-creation will guide the development within Rifkin's CHOICE framework. The primary outcomes are two: the number of patients attending eye disease screening camps, and the number of patients accepting cataract surgery. NVivo version 12 will be used for qualitative data analysis and Stata version 12 for quantitative data. Independent and paired t-tests will be performed to make comparisons between and within groups. P-values less than 0.05 will be considered statistically significant.

Proteolysis targeting chimera (PROTAC) is a protein degradation technique that has been increasingly used in the development of new drugs in recent years. Akt is a classical serine/threonine kinase, and its role outside of the kinase has gradually gained attention in recent years, making it one of the proteins targeted by PROTACs. Currently, there are many methods used for the evaluation of intracellular protein degradation, but each has its own advantages or disadvantages. This study aimed to investigate the feasibility of evaluating the degradation of pan-Akt proteins in cells by PROTACs (MS21 and MS170) using the NanoLuc luciferase method. After conducting a thorough comparison between this method and the classical western blot assay in various cells, as well as testing the stability of the experiments between multiple batches, we found that NanoLuc luciferase is a highly accurate, stable, low-cost and easy-to-operate method for the evaluation of intracellular pan-Akt degradation by PROTACs with a short cycle time and high cellular expandability. Given the numerous advantages of this method, it is hypothesized that it could be extended to evaluate the degradation of more target proteins of PROTACs. In summary, the NanoLuc luciferase is a suitable method for early protein degradation screening of PROTAC compounds.