Brain Banks Spur New Frontiers in Neuropsychiatric Research and Strategies for Analysis and Validation

Neuropsychiatric disorders affect hundreds of millions of patients and families worldwide. To decode the molecular framework of these diseases, many studies use human postmortem brain samples. These studies reveal brain-specific genetic and epigenetic patterns via high-throughput sequencing technologies. Identifying best practices for the collection of postmortem brain samples, analyzing such large amounts of sequencing data, and interpreting these results are critical to advance neuropsychiatry. We provide an overview of human brain banks worldwide, including progress in China, highlighting some well-known projects using human postmortem brain samples to understand molecular regulation in both normal brains and those with neuropsychiatric disorders. Finally, we discuss future research strategies, as well as state-of-the-art statistical and experimental methods that are drawn upon brain bank resources to improve our understanding of the agents of neuropsychiatric disorders.     

单细胞测序技术在传染病及微生物领域的应用研究进展

单细胞测序技术正逐渐成为生物学基础研究的“必备工具”,为我们理解各种生物学现象带来革命性的洞见。很多传染性疾病均涉及免疫细胞的差异化功能,而这些免疫细胞之间具有较大的异质性。与传统的批量高通量测序相比,近年来新兴的单细胞转录组测序使得研究者能够分析感染过程的免疫细胞异质性,充分挖掘珍贵的临床样本的分子信息,还能获取难以培养的病原微生物的遗传信息。本文着重介绍了当前单细胞测序在传染性疾病及病原微生物研究领域中的应用情况,并对其发展前景做了简要展望。     

How Big Data and High-performance Computing Drive Brain Science

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.     

popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data

The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters.Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR.     

Dissecting Human Gonadal Cell Lineage Specification and Sex Determination Using A Single-cell RNA-seq Approach

Gonadal somatic cells are the main players in gonad development and are important for sex determination and germ cell development. Here, using a time-series single-cell RNA sequencing (scRNA-seq) strategy, we analyzed fetal germ cells (FGCs) and gonadal somatic cells in human embryos and fetuses. Clustering analysis of testes and ovaries revealed several novel cell subsets, including POU5F1⁺SPARC⁺ FGCs and KRT19⁺ somatic cells. Furthermore, our data indicated that the bone morphogenetic protein (BMP) signaling pathway plays cell type-specific and developmental stage-specific roles in testis development and promotes the gonocyte-to-spermatogonium transition (GST) in late-stage testicular mitotic arrest FGCs. Intriguingly, testosterone synthesis function transitioned from fetal Sertoli cells to adult Leydig cells in a stepwise manner. In our study, potential interactions between gonadal somatic cells were systematically explored and we identified cell type-specific developmental defects in both FGCs and gonadal somatic cells in a Turner syndrome embryo (45, XO). Our work provides a blueprint of the complex yet highly ordered development of and the interactions among human FGCs and gonadal somatic cells.     

Single-cell Transcriptomic Analysis Reveals the Cellular Heterogeneity of Mesenchymal Stem Cells

Ex vivo-expanded mesenchymal stem cells (MSCs) have been demonstrated to be a heterogeneous mixture of cells exhibiting varying proliferative, multipotential, and immunomodulatory capacities. However, the exact characteristics of MSCs remain largely unknown. By single-cell RNA sequencing of 61,296 MSCs derived from bone marrow and Wharton’s jelly, we revealed five distinct subpopulations. The developmental trajectory of these five MSC subpopulations was mapped, revealing a differentiation path from stem-like active proliferative cells (APCs) to multipotent progenitor cells, followed by branching into two paths: 1) unipotent preadipocytes or 2) bipotent prechondro-osteoblasts that were subsequently differentiated into unipotent prechondrocytes. The stem-like APCs, expressing the perivascular mesodermal progenitor markers CSPG4/MCAM/NES, uniquely exhibited strong proliferation and stemness signatures. Remarkably, the prechondrocyte subpopulation specifically expressed immunomodulatory genes and was able to suppress activated CD3⁺ T cell proliferation in vitro, supporting the role of this population in immunoregulation. In summary, our analysis mapped the heterogeneous subpopulations of MSCs and identified two subpopulations with potential functions in self-renewal and immunoregulation. Our findings advance the definition of MSCs by identifying the specific functions of their heterogeneous cellular composition, allowing for more specific and effective MSC application through the purification of their functional subpopulations.     

MISC: missing imputation for single-cell RNA sequencing data

Background

Single-cell RNA sequencing (scRNA-seq) technology provides an effective way to study cell heterogeneity. However, due to the low capture efficiency and stochastic gene expression, scRNA-seq data often contains a high percentage of missing values. It has been showed that the missing rate can reach approximately 30% even after noise reduction. To accurately recover missing values in scRNA-seq data, we need to know where the missing data is; how much data is missing; and what are the values of these data.

Methods

To solve these three problems, we propose a novel model with a hybrid machine learning method, namely, missing imputation for single-cell RNA-seq (MISC). To solve the first problem, we transformed it to a binary classification problem on the RNA-seq expression matrix. Then, for the second problem, we searched for the intersection of the classification results, zero-inflated model and false negative model results. Finally, we used the regression model to recover the data in the missing elements.

Results

We compared the raw data without imputation, the mean-smooth neighbor cell trajectory, MISC on chronic myeloid leukemia data (CML), the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells. On the CML data, MISC discovered a trajectory branch from the CP-CML to the BC-CML, which provides direct evidence of evolution from CP to BC stem cells. On the mouse brain data, MISC clearly divides the pyramidal CA1 into different branches, and it is direct evidence of pyramidal CA1 in the subpopulations. In the meantime, with MISC, the oligodendrocyte cells became an independent group with an apparent boundary.

Conclusions

Our results showed that the MISC model improved the cell type classification and could be instrumental to study cellular heterogeneity. Overall, MISC is a robust missing data imputation model for single-cell RNA-seq data.

     

Integrating multiple references for single-cell assignment

Efficient single-cell assignment is essential for single-cell sequencing data analysis. With the explosive growth of single-cell sequencing data, multiple single-cell sequencing data sources are available for the same kind of tissue, which can be integrated to further improve single-cell assignment; however, an efficient integration strategy is still lacking due to the great challenges of data heterogeneity existing in multiple references. To this end, we present mtSC, a flexible single-cell assignment framework that integrates multiple references based on multitask deep metric learning designed specifically for cell type identification within tissues with multiple single-cell sequencing data as references. We evaluated mtSC on a comprehensive set of publicly available benchmark datasets and demonstrated its state-of-the-art effectiveness for integrative single-cell assignment with multiple references.     

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.     

单细胞全基因组扩增技术与应用

同一组织中的细胞往往具有类似的结构和功能,然而通过对单个细胞进行测序分析后,发现每个细胞都具有一定异质性.单细胞全基因组扩增技术是进行单细胞测序的前提,该技术可用于揭示单细胞基因组结构差异,同时在肿瘤研究、发育生物学、微生物学等研究中发挥重要作用,并成为生命科学研究技术的热点之一.单细胞全基因组扩增技术的难点在于单细胞的分离和全基因组的扩增.本文介绍了单细胞全基因组扩增技术中常用的单细胞分离技术和单细胞全基因组扩增技术,并对各技术间的优缺点进行比较,同时着重讨论该技术在肿瘤研究、发育生物学和微生物学研究中的应用.     

HeteroMeth: A Database of Cell-to-cell Heterogeneity in DNA Methylation

DNA methylation is an important epigenetic mark that plays a vital role in gene expression and cell differentiation. The average DNA methylation level among a group of cells has been extensively documented. However, the cell-to-cell heterogeneity in DNA methylation, which reflects the differentiation of epigenetic status among cells, remains less investigated. Here we established a gold standard of the cell-to-cell heterogeneity in DNA methylation based on single-cell bisulfite sequencing (BS-seq) data. With that, we optimized a computational pipeline for estimating the heterogeneity in DNA methylation from bulk BS-seq data. We further built HeteroMeth, a database for searching, browsing, visualizing, and downloading the data for heterogeneity in DNA methylation for a total of 141 samples in humans, mice, Arabidopsis, and rice. Three genes are used as examples to illustrate the power of HeteroMeth in the identification of unique features in DNA methylation. The optimization of the computational strategy and the construction of the database in this study complement the recent experimental attempts on single-cell DNA methylomes and will facilitate the understanding of epigenetic mechanisms underlying cell differentiation and embryonic development. HeteroMeth is publicly available at http://qianlab.genetics.ac.cn/HeteroMeth.     

单细胞基因组测序技术新进展及其在生物医学中的应用

随着单细胞基因组测序技术的建立与发展,对细胞基因组特征的分析进入了单细胞水平.单细胞的基因组分辨率不但使研究人员能够在单细胞尺度上分析肿瘤细胞的异质性,也使得传统上难以检测的稀有细胞的基因组研究成为可能.这些稀有细胞往往具有重要的生物学意义或临床价值,如癌症患者血液中循环肿瘤细胞(circulating tumor c...