2019年 · 第02卷第04期

Schizophrenia is a life-long, complex mental illness that still lacks satisfactory treatments. In recent years, increasing numbers of candidate biomarkers of schizophrenia occurrences and drug responses to schizophrenia therapies have been successfully identified by many omics studies. This review discusses the latest discoveries regarding effective drug targets and relevant drug classifications in schizophrenia. It also assesses our understanding of biomarkers for drug efficacy and adverse drug reactions in current schizophrenia treatments using omics technologies. Future applications in clinical practice have been proposed based on these new findings, and are now considered highly promising strategies to better treat schizophrenia. Finally, we explore several novel approaches that aim to reveal additional genetic signatures of schizophrenia using multi-omics data, which are hoped to improve the diagnosis and treatment of this illness in the future.

Many common diseases are characterized by polygenic architectures in which a single variant has only a small effect on phenotype. Genome-wide association studies and next generation sequencing have identified thousands of genetic variants of disease susceptibility. Recently, non-coding variants identified by genome-wide association studies have been systematically reviewed. Here, we review disease-causing coding variants and their relevance to clinical medicine.

Phototherapies offer promising alternatives to traditional cancer therapies. Phototherapies mainly rely on manipulation of target tissue through photothermal, photochemical, or photomechanical interactions. Combining phototherapy with immunotherapy has the benefit of eliciting a systemic immune response. Specifically, photothermal therapy (PTT) has been shown to induce apoptosis and necrosis in cancer cells, releasing tumor associated antigenic peptides while sparing healthy host cells, through temperature increase in targeted tissue. However, the tissue temperature must be monitored and controlled to minimize adverse thermal effects on normal tissue and to avoid the destruction of tumor-specific antigens, in order to achieve the desired therapeutic effects of PTT. Techniques for monitoring PTT have evolved from post-treatment quantification methods like enzyme linked immunosorbent assay, western blot analysis, and flow cytometry to modern methods capable of real-time monitoring, such as magnetic resonance thermometry, computed tomography, and photoacoustic imaging. Monitoring methods are largely chosen based on the type of light delivery to the target tissue. Interstitial methods of thermometry, such as thermocouples and fiber-optic sensors, are able to monitor temperature of the local tumor environment. However, these methods can be challenging if the phototherapy itself is interstitially administered. Increasingly, non-invasive therapies call for non-invasive monitoring, which can be achieved through magnetic resonance thermometry, computed tomography, and photoacoustic imaging techniques. The purpose of this review is to introduce the feasible methods used to monitor tissue temperature during PTT. The descriptions of different techniques and the measurement examples can help the researchers and practitioners when using therapeutic PTT.

Single-cell RNA sequencing (scRNA-seq) allows researchers to examine the transcriptome at the single-cell level and has been increasingly employed as technologies continue to advance. Due to technical and biological reasons unique to scRNA-seq data, denoising and batch effect correction are almost indispensable to ensure valid and powerful data analysis. However, various aspects of scRNA-seq data pose grand challenges for such essential tasks pertaining to data pre-processing, normalization or harmonization. In this review, we first discuss properties of scRNA-seq data that contribute to the challenges for denoising and batch effect correction from a computational perspective. We then focus on reviewing several state-of-the-art methods for dropout imputation and batch effect correction, comparing their strengths and weaknesses. Finally, we benchmarked three widely used correction tools using two hematopoietic scRNA-seq datasets to show their performance in a real data application.

Since the boom of biomedical big data studies, various big data processing technologies have been developed rapidly. As an important form of knowledge representation, ontology has become an important means for the utilization and integration of biomedical big data. The emergence of new technologies for ontology development has resulted in the generation of many biomedical ontologies by many ontology development communities. The Open Biological and Biomedical Ontology Foundry, an academic organization for bio-ontology developers, has provided a set of principles to guide community-based open ontology construction. The Open Biological and Biomedical Ontology Foundry have also built many widely used ontologies, such as Gene Ontology, Human Phenotype Ontology, and Chemical Entities of Biological Interest. Other various ontology repositories have also been created and used to support ontology reuse. Many efficient tools for ontology applications, such as data annotation and terms mapping, have also been developed. High quality ontologies are also being used to develop new methods and tools for biomedical data analysis. The applications of Gene Ontology and Human Phenotype Ontology for data analysis and integration in recent years are reviewed here. To promote the development and applications of biomedical ontologies in China, a research community, OntoChina, was founded recently. OntoChina aims to support the development of reference ontologies, especially bilingual and Chinese translated ontologies. OntoChina also encourages ontology developers to follow the Open Biological and Biomedical Ontology Foundry principles.

Navigation and recognition of disease lesions remain challenging during colorectal cancer diagnosis and treatment, given that the precision and capacity of using specific surface antigens as recognition sites are relatively lacking. Moreover, the low penetration rate of noninvasive detection methods has delayed disease diagnosis in developing countries. We developed a bacterial device targeting the Thomsen–Friedenreich antigen, which is abundant on lesion tissue, and produced vesicles that serve as markers for ultrasonic detection. The device uses a bacterial cell surface display system and acoustic reporter gene to function as a screening device for colorectal cancer detection. The diagnostic efficiency of this device was determined by flow cytometry, immunohistochemistry, microfluidic chip-based assay, and ultrasonic examinations on both cellular and tissue scales. In all scales and experiments, our device showed great feasibility upon differentiating disease lesion and normal tissue, while foundation of the usage of vesicle reporter system as noninvasive method has also been laid. The application of this device provides insight into the practicability and prospect of bacterial detecting agents in the field of diagnostics. All animal studies were approved by the Institutional Animal Care and Use Committee of Shanghai Jiao Tong University, China (approval No. 201801015) on February 23, 2018.