Life Science Research Research Articles

KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.

Binding-induced DNA assembly makes Exo III-aided signal amplification universal for highly sensitive protein detection

Exonuclease III has been involved in many signal amplification strategies for bioanalysis. Although Exo III-based signal amplification methods are sensitive and simple compared to other nuclease-based strategies, existing methods do not work for all proteins. In this work, we combine Exo III-aided signal amplification with binding-induced DNA assembly for highly sensitive protein detection. Two separate single-stranded DNA will hybridize after binding to the same target, and trigger the Exo III-aided target recycling amplification. This strategy can realize homogeneous detection of all proteins, and the detection conditions and performance were discussed. Human epididymis protein-4 (HE4) was token as example, a linear detection range of 0.01 ∼ 50 ng/mL, with the LOD of 3 pg/mL for rapid detection is presented. Notably, a linear relationship in range of 0.05 ∼ 1 pg/mL can be obtained with long time incubation. In conclusion, we believe that this novel approach will have significant implications in life science research, disease diagnosis, treatment, and prognosis.

Reimagining bioRxiv and preprint servers as platforms for academic learning.

A popular preprint server, bioRxiv, is important as a tool for increased visibility for life science research. If used properly, however, bioRxiv can also be an important tool for training, as it may expose trainees (degree-seeking students undertaking research or internships directly related to their field of study) to the peer review process. Here, we offer a comprehensive guide to using bioRxiv as a training tool, as well as offer suggestions for improvements in bioRxiv, including confusion that may be caused by bioRxiv articles appearing on PubMed.

Active-matrix digital microfluidics design for field programmable high-throughput digitalized liquid handling

Digital liquid sample handling is an enabling tool for cutting-edge life-sciences research. We present here an active-matrix thin-film transistor (TFT) based digital microfluidics system, referred to as Field Programmable Droplet Array (FPDA). The system contains 256×256 pixels in an active area of 10.65cm2, which can manipulate thousands of addressable liquid droplets simultaneously. By leveraging a novel TFT device and circuits design solution, we manage to programmatically manipulate droplets at single-pixel level. The minimum achievable droplet volume is around 0.5 nL, which is two orders of magnitude smaller than the smallest droplet ever reported on active-matrix digital microfluidics. The movement of droplets can be either pre-programmed or controlled in real-time. The FPDA system shows great potential of the ubiquitous thin-film electronics technology in digital liquid handling. These efforts will make it possible to create a true programmable lab-on-a-chip device to enable great advances in life science research.

Essay: Exploring the Physics of Basic Medical Research.

The 20th century witnessed the emergence of many paradigm-shifting technologies from the physics community, which have revolutionized medical diagnostics and patient care. However, fundamental medical research has been mostly guided by methods from areas such as cell biology, biochemistry, and genetics, with fairly small contributions from physicists. In this Essay, I outline some key phenomena in the human body that are based on physical principles and yet govern our health over a vast range of length and time scales. I advocate that research in life sciences can greatly benefit from the methodology, know-how, and mindset of the physics community and that the pursuit of basic research in medicine is compatible with the mission of physics. Part of a series of Essays that concisely present author visions for the future of their field.

More than just 'added value': The perils of not establishing shared core facilities in resource-constrained communities.

The accelerating pace of technological advancements necessitates specialised expertise and cutting-edge instruments to maintain competitive research in life sciences. Core facilities - collaborative laboratories equipped with state-of-the-art tools and staffed by expert personnel - are vital resources that support diverse scientific endeavours. However, their adoption in lower-income communities has been comparatively stagnant due to both financial and cultural challenges. This paper explores the perils of not supporting core facilities on national research enterprises, underscoring the need for balanced investments in discovery science and crucial infrastructure support. We explore the implications from the perspectives of funders, university leaders and lab heads. We advocate for a paradigm shift to recognise these facilities as essential components of national research efforts. Core facilities are positioned not as optional but as strategic investments that can catalyse breakthroughs, particularly in environments with limited resources.

Preclinical magnetic resonance imaging and spectroscopy in the fields of radiological technology, medical physics, and radiology.

Magnetic resonance imaging (MRI) is an indispensable diagnostic imaging technique used in the clinical setting. MRI is advantageous over X-ray and computed tomography (CT), because the contrast provided depends on differences in the density of various organ tissues. In addition to MRI systems in hospitals, more than 100 systems are used for research purposes in Japan in various fields, including basic scientific research, molecular and clinical investigations, and life science research, such as drug discovery, veterinary medicine, and food testing. For many years, additional preclinical imaging studies have been conducted in basic research in the fields of radiation technology, medical physics, and radiology. The preclinical MRI research includes studies using small-bore and whole-body MRI systems. In this review, we focus on the animal study using small-bore MRI systems as "preclinical MRI". The preclinical MRI can be used to elucidate the pathophysiology of diseases and for translational research. This review will provide an overview of previous preclinical MRI studies such as brain, heart, and liver disease assessments. Also, we provide an overview of the utility of preclinical MRI studies in radiological physics and technology.

Bioinformatics – Supporting modern life science research, applications, and challenges

Bioinformatics is an interdisciplinary field that develops methods, software tools for understanding biological data and aims to investigate questions about biological composition, structure, function, and evolution of molecules, cells, tissues, and organisms using mathematics, informatics, statistics, and computer science. As we are moving towards the era of cutting-edge technologies there will be a lot of data to store, process and analyze. It offers analysis software for data studies and comparisons and provides tools for modeling, visualizing, exploring and interpreting data. It includes analysis, structural and functional characterization of biomolecules leading to the development of Genomics, Proteomics, Transcriptomics, and Metabolomics, etc. Drug discovery and development tools, supported by recent advancements in machine learning and cloud computing should shorten the time to find and produce an efficient drug compound with fewer side effects and more results emerge as a branch called Chemo-informatics. Personalized medicine where bioinformatics can help a lot to make drug molecules based on the genetic makeup of individuals for better outcomes is a prime area of research and need of the society at present. The major futures challenge of the scientific community is to create an in-vitro model of whole-cell or organism and further simulating a whole cell or an organism by applying in-silico approaches. To achieve that, reliable tools that utilize those technologies need to be developed and tested. Bioinformatics reduces the search space/size of the problem by thousand times. The main goal is to convert a multitude of complex data into useful information and knowledge. As a consequence of understanding such data, one can basically engineer longer life for society.

Foldy: An open-source web application for interactive protein structure analysis.

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy's interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy's interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.

Unlocking the potential of big data and AI in medicine: insights from biobanking.

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

Polarization-encoded 3D structured light and multifocal spot arrays generation based on metasurface

Fluorescence microscopy possesses the advantages of high resolution, high sensitivity, molecular specificity and noninvasiveness, providing an important tool in life science research. The multifocal array and 3D structured light are two kinds of important light fields that are often used in scanning fluorescence microscopy systems and wide-field fluorescence microscopy systems. However, traditional methods for generating multifocal arrays and 3D structured light illumination rely on various bulk optical components, making it challenging to achieve compact optical systems. Besides, generating these two types of illumination typically requires two separate and independent optical systems, hindering the integration of different types of fluorescence microscopy systems. Here, a dielectric metasurface is proposed that can achieve the switching between multifocal arrays and 3D structured light through polarization state modulation, greatly simplifying the illumination optics of fluorescence microscopy systems and facilitating the integration of different types of fluorescence microscopy systems.

Hi-Tag: a simple and efficient method for identifying protein-mediated long-range chromatin interactions with low cell numbers.

Protein-mediated chromatin interactions can be revealed by coupling proximity-based ligation with chromatin immunoprecipitation. However, these techniques require complex experimental procedures and millions of cells per experiment, which limits their widespread application in life science research. Here, we develop a novel method, Hi-Tag, that identifies high-resolution, long-range chromatin interactions through transposase tagmentation and chromatin proximity ligation (with a phosphorothioate-modified linker). Hi-Tag can be implemented using as few as 100,000 cells, involving simple experimental procedures that can be completed within 1.5 days. Meanwhile, Hi-Tag is capable of using its own data to identify the binding sites of specific proteins, based on which, it can acquire accurate interaction information. Our results suggest that Hi-Tag has great potential for advancing chromatin interaction studies, particularly in the context of limited cell availability.

Coordinating virus research: The Virus Infectious Disease Ontology.

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

Theoretical Design and Synthesis of Caged Compounds Using X-Ray-Triggered Azo Bond Cleavage.

Caged compounds are frequently used in life science research. However, the light used to activate them is commonly absorbed and scattered by biological materials, limiting their use to basic research in cells or small animals. In contrast, hard X-rays exhibit high bio-permeability due to the difficulty of interacting with biological molecules. With the main goal of developing X-ray activatable caged compounds, azo compounds are designed and synthesized with a positive charge and long π-conjugated system to increase the reaction efficiency with hydrated electrons. The azo bonds in the designed compounds are selectively cleaved by X-ray, and the fluorescent substance Diethyl Rhodamine is released. Based on the results of experiments and quantum chemical calculations, azo bond cleavage is assumed to occur via a two-step process: a two-electron reduction of the azo bond followed by N─N bond cleavage. Cellular experiments also demonstrate that the azo bonds can be cleaved intracellularly. Thus, caged compounds that can be activated by an azo bond cleavage reaction promoted by X-ray are successfully generated.

Bibliometric and visual analysis of single-cell sequencing from 2010 to 2022.

Background: Single-cell sequencing (SCS) is a technique used to analyze the genome, transcriptome, epigenome, and other genetic data at the level of a single cell. The procedure is commonly utilized in multiple fields, including neurobiology, immunology, and microbiology, and has emerged as a key focus of life science research. However, a thorough and impartial analysis of the existing state and trends of SCS-related research is lacking. The current study aimed to map the development trends of studies on SCS during the years 2010-2022 through bibliometric software. Methods: Pertinent papers on SCS from 2010 to 2022 were obtained using the Web of Science Core Collection. Research categories, nations/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords were analyzed using VOSviewer, the R package "bibliometric", and CiteSpace. Results: The bibliometric analysis included 9,929 papers published between 2010 and 2022, and showed a consistent increase in the quantity of papers each year. The United States was the source of the highest quantity of articles and citations in this field. The majority of articles were published in the periodical Nature Communications. Butler A was the most frequently quoted author on this topic, and his article "Integrating single-cell transcriptome data across diverse conditions, technologies, and species" has received numerous citations to date. The literature and keyword analysis showed that studies involving single-cell RNA sequencing (scRNA-seq) were prominent in this discipline during the study period. Conclusion: This study utilized bibliometric techniques to visualize research in SCS-related domains, which facilitated the identification of emerging patterns and future directions in the field. Current hot topics in SCS research include COVID-19, tumor microenvironment, scRNA-seq, and neuroscience. Our results are significant for scholars seeking to identify key issues and generate new research ideas.

Synchrotron radiation data-driven artificial intelligence approaches in materials discovery

Synchrotron radiation technology provides high-resolution and high-sensitivity information for many fields such as material science, life science, and energy research. Synchrotron radiation data-driven methods have significantly accelerated the development of materials discovery and analysis. However, synchrotron radiation data is complex and large, requiring artificial intelligence for analysis. Artificial intelligence can efficiently process complex high-dimensional data, automate the analysis process, discover hidden patterns and associations, and build predictive models. This review provides an overview of the application and development of combining synchrotron radiation data-driven methods with artificial intelligence in the field of materials discovery. The application of the method in science is still limited by the problems of large and complex synchrotron radiation data, valuable experimental machine time, and uninterpretable artificial intelligence models. To address these problems, this review correspondingly proposes solutions for synchrotron radiation artificial intelligence data banks, standardized experiment records systems, and interpretable artificial intelligence predictive models.

Unlocking integration opportunities: How Dahlgren Memorial Library implemented the circulation of room keys for the Georgetown University School of Medicine rooms

Dahlgren Memorial Library, the Graduate Health and Life Sciences Research Library at Georgetown University implemented the circulation of room keys in 2007. The rooms were outside of the library and managed by Georgetown University School of Medicine’s Office of Financial Affairs who were integral in the implementation of the keys and the management of the rooms. This article will discuss the policies and procedures for circulating the room keys, downfalls and benefits, and future plans.

Biobanco orientado nas doenças veterinárias para a análise biomolecular com base em tecidos congelados, cultura celular e tecido forense

ABSTRACT: Collecting and analyzing biological specimens leads to unprecedented opportunities for assessing and understanding the complex networks of interactions between biomolecules, and the functional and clinical consequences of their alterations. In this context, the Tissue Bank plays a key role in life science research and development, making its implementation in veterinary medicine essential for developing new research, especially in the forensic and tumor fields. In total, 52 tissues were collected, 15 forensic samples, 25 neoplasms, and 12 cell cultures. The storage, freezing, data management protocols, and the quality of these samples were analyzed. In the same way, the quality of the RNA and DNA in the short and long term was evaluated. The quality of the genetic material was confirmed by polymerase chain reaction. For data comparisons, non-parametric tests were used, with a significance level of p˂0.05. The measures adopted in the Tissue Bank for obtaining and handling the samples and for controlling and guaranteeing the quality were considered adequate, as they enabled optimal preservation of the integrity of the genetic material, a relevant factor for the use of tumor samples and forensic samples stored in the Biobank, thus contributing to future molecular research in the area of pathology and investigations of crimes against fauna.

A programmable and automated optical electrowetting-on-dielectric (oEWOD) driven platform for massively parallel and sequential processing of single cell assay operations.

Recently, there has been an increasing emphasis on single cell profiling for high-throughput screening workflows in drug discovery and life sciences research. However, the biology underpinning these screens is often complex and is insufficiently addressed by singleplex assay screens. Traditional single cell screening technologies have created powerful sets of 'omic data that allow users to bioinformatically infer biological function, but have as of yet not empowered direct functional analysis at the level of each individual cell. Consequently, screening campaigns often require multiple secondary screens leading to laborious, time-consuming and expensive workflows in which attrition points may not be queried until late in the process. We describe a platform that harnesses droplet microfluidics and optical electrowetting-on-dielectric (oEWOD) to perform highly-controlled sequential and multiplexed single cell assays in massively parallelised workflows to enable complex cell profiling during screening. Soluble reagents or objects, such as cells or assay beads, are encapsulated into droplets of media in fluorous oil and are actively filtered based on size and optical features ensuring only desirable droplets (e.g. single cell droplets) are retained for analysis, thereby overcoming the Poisson probability distribution. Droplets are stored in an array on a temperature-controlled chip and the history of individual droplets is logged from the point of filter until completion of the workflow. On chip, droplets are subject to an automated and flexible suite of operations including the merging of sample droplets and the fluorescent acquisition of assay readouts to enable complex sequential assay workflows. To demonstrate the broad utility of the platform, we present examples of single-cell functional workflows for various applications such as antibody discovery, infectious disease, and cell and gene therapy.

Understanding the molecular mechanisms of human diseases: the benefits of fission yeasts.

The role of model organisms such as yeasts in life science research is crucial. Although the baker's yeast (Saccharomyces cerevisiae) is the most popular model among yeasts, the contribution of the fission yeasts (Schizosaccharomyces) to life science is also indisputable. Since both types of yeasts share several thousands of common orthologous genes with humans, they provide a simple research platform to investigate many fundamental molecular mechanisms and functions, thereby contributing to the understanding of the background of human diseases. In this review, we would like to highlight the many advantages of fission yeasts over budding yeasts. The usefulness of fission yeasts in virus research is shown as an example, presenting the most important research results related to the Human Immunodeficiency Virus Type 1 (HIV-1) Vpr protein. Besides, the potential role of fission yeasts in the study of prion biology is also discussed. Furthermore, we are keen to promote the uprising model yeast Schizosaccharomyces japonicus, which is a dimorphic species in the fission yeast genus. We propose the hyphal growth of S. japonicus as an unusual opportunity as a model to study the invadopodia of human cancer cells since the two seemingly different cell types can be compared along fundamental features. Here we also collect the latest laboratory protocols and bioinformatics tools for the fission yeasts to highlight the many possibilities available to the research community. In addition, we present several limiting factors that everyone should be aware of when working with yeast models.