Cell population-based framework of genetic epidemiology in the single-cell omics era

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology.     

Plant synthetic biology innovations for biofuels and bioproducts

Plant-based biosynthesis of fuels, chemicals, and materials promotes environmental sustainability, which includes decreases in greenhouse gas emissions, water pollution, and loss of biodiversity. Advances in plant synthetic biology (synbio) should improve precision and efficacy of genetic engineering for sustainability. Applicable synbio innovations include genome editing, gene circuit design, synthetic promoter development, gene stacking technologies, and the design of environmental sensors. Moreover, recent advancements in developing spatially resolved and single-cell omics contribute to the discovery and characterization of cell-type-specific mechanisms and spatiotemporal gene regulations in distinct plant tissues for the expression of cell- and tissue-specific genes, resulting in improved bioproduction. This review highlights recent plant synbio progress and new single-cell molecular profiling towards sustainable biofuel and biomaterial production.     

单细胞技术进展及其在植物研究中的应用

多细胞生物体的生存依赖于不同类型细胞特异性的功能分工,不同类型的细胞尽管基因组相同,但有其独特的发育过程和应对环境变化的能力。生物学的一大挑战就是揭示基因如何在正确的位置、正确的时间表达到正确的水平,最近出现了很多通过细胞类型特异性方法研究单细胞组学的工具,这些新技术使我们能通过空前分辨率,理解多细胞生物体内不同类型的单个细胞基因表达特点及其适应环境变化的机制。单细胞样品的获取一直是单细胞研究的一大技术瓶颈,因此本文将以如何获得起始材料为重点,探讨单细胞研究的样品标记、单细胞分离及获取、组学数据分析和结果验证等技术方法及其在植物研究中的应用。     

Emergence of neuron types

Neuron types are the building blocks of the nervous system, and therefore, of functional circuits. Understanding the origin of neuronal diversity has always been an essential question in neuroscience and developmental biology. While knowledge on the molecular control of their diversification has largely increased during the last decades, it is now possible to reveal the dynamic mechanisms and the actual stepwise molecular changes occurring at single-cell level with the advent of single-cell omics technologies and analysis with high temporal resolution. Here, we focus on recent advances in the field and in technical and analytical tools that enable detailed insights into the emergence of neuron types in the central and peripheral nervous systems.     

Perspective: functional genomics towards new biotechnology in medicinal plants

The secret of chemical diversity and function of specialized metabolites in medicinal plants will be unveiled by study of functional genomics at an unprecedentedly rapid rate in the coming years. This is mostly ascribed to the remarkable advancement in the high-throughput DNA sequencing together with other omics technologies such as metabolomics, in particular, due to drastic reduction in the cost of acquiring, storing and analyzing massive omics datasets. Once the genes involved in a biosynthetic pathway of specialized compounds in plants are elucidated, synthetic biology or genome editing can be applied to produce the target compounds in an engineered organism or to manipulate the pathway in planta. Coupled with these advancements in pathway elucidation approaches, modern plant biotechnology strategies are bound to significantly contribute to the sustainable development goals set by United Nations.     

Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution

Phenotypic profiling of natural, engineered or synthetic cells has increasingly become a bottleneck in the mining and engineering of cell factories. Single-cell phenotyping technologies are highly promising for tackling this hurdle, yet ideally they should allow non-invasive live-cell probing, be label-free, provide landscape-like phenotyping capability, distinguish complex functions, operate with high speed, sufficient throughput and low cost, and finally, couple with cell sorting so as to enable downstream omics analysis. This review focuses on recent progress in Ramanome Technology Platform (RTP), which consists of Raman spectroscopy based phenotyping, sorting and sequencing of single cells, and discuss the key challenges and emerging trends. In addition, we propose ramanome, a collection of single-cell Raman spectra (SCRS) acquired from individual cells within a cellular population or consortium, as a new type of biological phenome datatype at the single-cell resolution. By establishing the phenome-genome links in a label-free, single-cell manner, RTP should find wide applications in functional screening and strain development of live microbial, plant and animal cell factories.     

Unveiling network-based functional features through integration of gene expression into protein networks

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype–phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.     

组学技术在产油微生物中的应用

微生物油脂是未来燃料和食品用油的重要潜在资源.近年来,随着系统生物学技术的快速发展,从全局角度理解产油微生物生理代谢及脂质积累的特征成为研究热点.组学技术作为系统生物学研究的重要工具被广泛用于揭示产油微生物脂质高效生产的机制研究中,这为产油微生物理性遗传改造和发酵过程控制提供了基础.文中对组学技术在产油微生物中的应用概...     

Plant systems biology: insights,advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this  review, we discuss the basics of systems biology including the various ‘omics’ approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.     

Revealing allele-specific gene expression by single-cell transcriptomics

Single-cell sequencing has emerged as a revolutionary method that reveals biological processes with unprecedented resolution and scale, and has already greatly impacted biology and medicine. To investigate processes such as alternative splicing, novel exon detection and allele-specific expression (ASE), full-length based single-cell RNA-seq methods are required for broad sequence coverage and single nucleotide polymorphism (SNP) identification. In this review, we revisit recent achievements from studies that used single-cell RNA-seq to advance our understanding of ASE in the context of both autosomal and X-chromosome genes. We also recapitulate useful bioinformatic tools developed to identify haplotype phase.     

Cell death goes LIVE: Technological advances in real-time tracking of cell death

Cell population can be viewed as a quantum system, which like Schrödinger’s cat exists as a combination of survival- and death-allowing states. Tracking and understanding cell-to-cell variability in processes of high spatio-temporal complexity such as cell death is at the core of current systems biology approaches. As probabilistic modeling tools attempt to impute information inaccessible by current experimental approaches, advances in technologies for single-cell imaging and omics (proteomics, genomics, metabolomics) should go hand in hand with the computational efforts. Over the last few years we have made exciting technological advances that allow studies of cell death dynamically in real-time and with the unprecedented accuracy. These approaches are based on innovative fluorescent assays and recombinant proteins, bioelectrical properties of cells, and more recently also on state-of-the-art optical spectroscopy. Here, we review current status of the most innovative analytical technologies for dynamic tracking of cell death, and address the interdisciplinary promises and future challenges of these methods.     

植物合成生物学领域发展态势的文献计量分析

合成生物学作为一种颠覆性技术可应用于农业领域的创新发展,解决当前农业学科中的瓶颈问题。利用文献计量学方法从领域发表论文的时序数量分布、主题分布等探测当前合成生物学的基本态势。基于领域的主题分布可知,其中植物合成生物学这一主题是稳定存在的且主题规模处于稳定增长趋势。聚焦植物合成生物学这一主题方向,在构建引文网络的基础上利用主路径分析方法从知识流动角度探测植物合成生物学领域重要知识节点,内容涵盖介子油苷生物合成途径,重要催化酶功能解析、转录因子的调控作用,组学方法的应用,利用微生物酵母进行生物物质合成,这些内容表征了合成生物的核心理论技术。