Cells and tissues are exquisitely organized in a complex but ordered manner to form organs and bodies so that individuals can function properly. The spatial organization and tissue architecture represent a keynote property underneath all living organisms. Molecular architecture and cellular composition within intact tissues play a vital role in a variety of biological processes, such as forming the complicated tissue functionality, precise regulation of cell transition in all living activities, consolidation of central nervous system, cellular responses to immunological and pathological cues. To explore these biological events at a large scale and fine resolution, a genome-wide understanding of spatial cellular changes is essential. However, previous bulk RNA sequencing and single-cell RNA sequencing technologies could not obtain the important spatial information of tissues and cells, despite their ability to detect high content transcriptional changes. These limitations have prompted the development of numerous spatially resolved technologies which provide a new dimension to interrogate the regional gene expression, cellular microenvironment, anatomical heterogeneity and cell-cell interactions. Since the advent of spatial transcriptomics, related works that use these technologies have increased rapidly, and new methods with higher throughput and resolution have grown quickly, all of which hold great promise to accelerate new discoveries in understanding the biological complexity. In this review, we briefly discussed the historical evolution of spatially resolved transcriptome. We broadly surveyed the representative methods. Furthermore, we summarized the general computational analysis pipeline for the spatial gene expression data. Finally, we proposed perspectives for technological development of spatial multi-omics.
Single-cell sequencing has become one of the most powerful and popular techniques in dissecting molecular heterogeneity and modeling the cellular architecture of a biological system. During the past twenty years, the throughput of single-cell sequencing has increased from hundreds of cells to over tens of thousands of cells in parallel. Moreover, this technology has been developed from sequencing transcriptome to measure different omics such as DNA methylome, chromatin accessibility, and so on. Currently, multi-omics which can analyze different omics in the same cell is rapidly advancing. This work advances the study of many biosystems, including the nervous system. Here, we review current single-cell multi-omics sequencing techniques and describe how they improve our understanding of the nervous system. Finally, we discuss the open scientific questions in neural research that may be answered through further improvement of single-cell multi-omics sequencing technology.
The size and growth of a cell can be described by three related physical parameters: volume, density and mass. All the three are coupled to numerous biochemical reactions and biophysical properties of a cell. It is therefore not surprising that cell size and growth pattern are tightly regulated across all kingdoms of life. Indeed, deregulation of cell size and growth has been found to be associated with diseases. Yet, how cells regulate their size and how cell size connects to cell function remain poorly understood, partly due to the difficulties to precisely measure the size and growth of single cells. In this review, we summarize methods of measuring cell volume, density, and mass, and discuss how the new technologies may advance our understanding of cell size control.
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool to explore cells. With an increasing number of scRNA-seq data analysis tools that have been developed, it is challenging for users to choose and compare their performance. Here, we present an overview of the workflow for computational analysis of scRNA-seq data. We detail the steps of a typical scRNA-seq analysis, including experimental design, pre-processing and quality control, feature selection, dimensionality reduction, cell clustering and annotation, and downstream analysis including batch correction, trajectory inference and cell–cell communication. We provide guidelines according to our best practice. This review will be helpful for the experimentalists interested in analyzing their data, and will aid the users seeking to update their analysis pipelines.
The brain is one of the most complex organs in nature. In this organ, multiple neurons, neuron clusters, or multiple brain regions are interconnected to form a complex structural network where various brain functions are completed through interaction. In recent years, multiple tools and techniques have been developed to analyze the composition of different cell types in the brain and to construct the brain atlas on macroscopic, mesoscopic, and microscopic levels. Meanwhile, researchers have found that many neuropsychiatric diseases, such as Parkinson’s disease, Alzheimer’s disease, and Huntington’s disease, are closely related to abnormal changes of brain structure, which means the investigation in brain structure not only provides a new idea for understanding the pathological mechanism of the diseases, but also provides imaging markers for early diagnosis and potential treatment. This article pays attention to the research of human brain structure, reviews the research progress of human brain structure and the structural mechanism of neurodegenerative diseases, and discusses the problems and prospects in the field.
