Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors(TFs)in intricate regulatory networks in a cell-type specific manner.Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings.This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets,addressing batch effects and conserving biological variance.This integration spans a broad spectrum of tissues,including both below-and above-ground parts.Utilizing a rigorous approach for cell type annotation,we identified 47 distinct cell types or states,largely expanding our current view of plant cell compositions.We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression.Taken together,our study not only offers extensive plant cell atlas exploration that serves as a valuable resource,but also provides molecular insights into gene-regulatory programs that varies from different cell types.

SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data

Clustering is a prevalent analytical means to analyze single cell RNA sequencing (scRNA-seq) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of pressing need. Here we proposed Spearman subsampling-clustering-classification (SSCC),a new clustering framework based on random projection and feature construction,for large-scale scRNA-seq data. SSCC greatly improves clustering accuracy,robustness,and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells,SSCC achieved 20%improvement for clustering accuracy and 50-fold acceleration,but only consumed 66%memory usage,compared to the widelyused software package SC3. Compared to k-means,the accuracy improvement of SSCC can reach 3-fold. An R implementation of SSCC is available at https://github.com/Japrin/sscClust.

COVID-19 Related Research by Data Mining in Single Cell Transcriptome Profiles

The outbreak of coronavirus disease 2019(COVID-2019)has drawn public attention all over the world.As a newly emerging area,single cell sequencing also exerts its power in the battle over the epidemic.In this review,the up-to-date knowledge of COVID-19 and its receptor is summarized,followed by a collection of the mining of single cell transcriptome profiling data for the information in aspects of the vulnerable cell types in humans and the potential mechanisms of the disease.

单细胞RNA-seq数据缺失元素补全算法

基于非负矩阵分解模型,提出一种新的数据补全算法.该算法通过循环遍历确定最佳构造矩阵和rank值,解决了单细胞转录组测序(RNA-seq)数据中存在缺失值的问题,避免了由于单细胞测序深度不足对细胞分型分析的影响.在慢性粒细胞白血病单细胞测序数据上的实验结果表明,由补全算法恢复缺失值后的细胞分型更清晰,验证了该算法的有效性.

Differential gene network analysis from single cell RNA-seq

Study of gene expression has been arguably the most active research field in functional genomics.Over the last two decades,various high-throughput technologies,from gene expression microarray to RNA-seq,have been widely applied to the wholegenome profiling of gene expression.The commonality of these experiments is that they measure the gene expression levels of"bulk"sample,which pools a large number(often in the scale of millions)of cells,and thus the measurements reflect the average expression

Single-cell Transcriptome Study as Big Data

The rapid growth of single-cell RNA-seq studies （scRNA-seq） demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing （NGS）-based studies, we propose a workflow that accounts for the unique characteris- tics of scRNA-seq data and primary objectives of single-cell studies.

Initial refinement of data from video‐based single‐cell tracking

Background: Video recording of cells offers a straightforward way to gainvaluable information from their response to treatments. An indispensable stepin obtaining such information involves tracking individual cells from therecorded data. A subsequent step is reducing such data to represent essentialbiological information. This can help to compare various single‐cell trackingdata yielding a novel source of information. The vast array of potential datasources highlights the significance of methodologies prioritizing simplicity,robustness, transparency, affordability, sensor independence, and freedomfrom reliance on specific software or online services.Methods: The provided data presents single‐cell tracking of clonal (A549)cells as they grow in two‐dimensional (2D) monolayers over 94 hours,spanning several cell cycles. The cells are exposed to three differentconcentrations of yessotoxin (YTX). The data treatments showcase theparametrization of population growth curves, as well as other statisticaldescriptions. These include the temporal development of cell speed in familytrees with and without cell death, correlations between sister cells, single‐cellaverage displacements, and the study of clustering tendencies.Results: Various statistics obtained from single‐cell tracking reveal patternssuitable for data compression and parametrization. These statistics encompassessential aspects such as cell division, movements, and mutual informationbetween sister cells.Conclusion: This work presents practical examples that highlight theabundant potential information within large sets of single‐cell tracking data.Data reduction is crucial in the process of acquiring such information whichcan be relevant for phenotypic drug discovery and therapeutics, extendingbeyond standardized procedures. Conducting meaningful big data analysistypically necessitates a substantial amount of data, which can stem fromstandalone case studies as an initial foundation.

基于单细胞多组学数据解析结直肠癌动态调控扰动

目的探讨结直肠癌恶性转变过程中的分子特征及其调控网络扰动,深入剖析结直肠癌恶性转变的微环境异质性并识别潜在治疗靶点。方法首先从Gene Expression Omnibus(GEO)数据库中收集单细胞转录组测序数据,从欧洲分子生物学实验室(EMBL)收集单细胞ATAC测序数据。数据共涉及来自14例患者的70例样本,包含22例正常样本、43例息肉样本和5例肿瘤样本。首先对scRNA-Seq测序数据进行预处理,基于Seurat包筛除双细胞和低质量细胞,对scATAC-Seq测序数据使用10X Cell Ranger-atac进行原始数据处理,并使用Signac包去除低质量细胞,得到三个阶段的scRNA-Seq和scATAC-Seq细胞图谱。进一步数据整合,基于Pando R包推断T细胞和上皮细胞的转录调控网络,通过mfinder软件分析网络motif和拓扑属性,对转录因子进行分级分析。最后,基于随机森林算法进行细胞分子特征预测及预后分析。结果筛选出202465个scRNA-Seq测序细胞和136422个scATAC-Seq测序细胞。基于数据整合分析,构建不同癌变阶段的细胞图谱。结果发现,随着结直肠癌进展,肿瘤微环境组成发生显著变化,尤其是T细胞和上皮细胞在不同疾病阶段的比例有较大差异。基于单细胞多组学数据整合,利用Pando包推断结直肠癌不同阶段的转录调控网络,揭示了T细胞和上皮细胞中转录因子及其调控关系的动态变化。功能富集分析结果显示在T细胞和上皮细胞中,转录因子所调控的功能在不同的疾病阶段有明显差异。基于转录调控网络分析发现,T细胞和上皮细胞转录调控网络符合无标度网络特性。网络motif分析揭示了在不同阶段存在的特定motif模式,反映了网络拓扑结构的动态变化,且网络中大多数相互作用都具有阶段特异性。共享转录因子的层级在癌变过程中也会发生变化。最后基于转录因子调控网络构建的分类器可以成功识别T细胞和上皮细胞,表明其作为细胞分子特征的有效性。进一步识别出与患者生存显著相关的网络motif,揭示其在结直肠癌预后中的潜在作用。结论基于整合单细胞多组学数据构建转录调控网络,解析随结直肠癌的进展转录调控网络及其功能的动态变化,揭示了结直肠癌进展过程中的细胞分子特征及关键预后motif,为结直肠癌的分子机制及预后评估提供了深刻见解。

基于loess回归加权的单细胞RNA-seq数据预处理算法

单细胞RNA测序数据提供了研究细胞异质性和在生物条件下差异表达基因的机会,其中一些在细胞中表达量有显著变化的高变异基因对单细胞测序数据的下游分析有着关键的作用。本文提出一种基于LOESS回归加权的单细胞RNA-Seq数据预处理算法,处理基因在细胞中的表达量数据,使高变化基因在分析过程中作用加强,达到基因软筛选与数据降噪的目的。进一步,选取6组单细胞RNA-seq数据对算法进行测试,首先对生成的基因表达矩阵进行预处理,然后分析预处理对后续分析(可视化、聚类、差异表达分析)的影响,实验结果表明该算法有效提升了下游分析准确度,显示出良好应用价值。

单细胞DNA甲基化测序数据处理流程与分析方法

单细胞DNA甲基化测序技术近年来取得了飞速发展,在揭示细胞间异质性及表观遗传学调控机制方面发挥着重要作用。随着测序技术的进步,单细胞甲基化数据的质量与数量也在不断提高,标准化的预处理流程与合适的分析方法对确保数据的可比性与结果的可靠性尤为关键。然而,目前尚未形成一套完整的数据分析流程来指导研究人员对现有数据进行挖掘。本文系统综述了单细胞甲基化数据预处理步骤和分析方法,简要介绍了相关算法和工具,并探讨了单细胞甲基化技术在脑科学、血细胞分化及癌症研究中的应用前景,旨在为研究人员分析数据时提供指导,推动单细胞甲基化测序技术的发展和应用。

基于质谱的单细胞蛋白质组学分析

单细胞蛋白质组学分析能揭示细胞个体之间蛋白质的精细差异,在诸多重要领域具有重要的应用价值,已成为目前的研究热点;其难点在于单细胞内的蛋白质极其微量,需要解决样品处理过程中的损失问题、色谱质谱检测的灵敏度问题和低信号强度质谱数据的解析利用问题.本文从单细胞分选、样品处理、色谱质谱采集和数据分析等方面,综合评述了目前基于质谱的单细胞蛋白质组学分析方法的研究进展及其在生物医学领域的应用,并展望了其未来的发展前景.

Single cell atlas of developing mouse dental germs reveals populations of CD24^(+)and Plac8^(+)odontogenic cells

The spatiotemporal relationships in high-resolution during odontogenesis remain poorly understood.We report a cell lineage and atlas of developing mouse teeth.We performed a large-scale(92,688 cells)single cell RNA sequencing,tracing the cell trajectories during odontogenesis from embryonic days 10.5 to 16.5.Combined with an assay for transposase-accessible chromatin with high-throughput sequencing,our results suggest that mesenchymal cells show the specific transcriptome profiles to distinguish the tooth types.Subsequently,we identified key gene regulatory networks in teeth and bone formation and uncovered spatiotemporal patterns of odontogenic mesenchymal cells.CD24^(+)and Plac8^(+)cells from the mesenchyme at the bell stage were distributed in the upper half and preodontoblast layer of the dental papilla,respectively,which could individually induce nonodontogenic epithelia to form tooth-like structures.Specifically,the Plac8^(+)tissue we discovered is the smallest piece with the most homogenous cells that could induce tooth regeneration to date.Our work reveals previously unknown heterogeneity and spatiotemporal patterns of tooth germs that may lead to tooth regeneration for regenerative dentistry.

Single cell transcriptomic analysis identifies novel vascular smooth muscle subsets under high hydrostatic pressure

Although some co-risk factors and hemodynamic alterations are involved in hypertension progression,their direct biomechanical effects are unclear.Here,we constructed a high-hydrostatic-pressure cell-culture system to imitate constant hypertension and identified novel molecular classifications of human aortic smooth muscle cells(HASMCs)by single-cell transcriptome analysis.Under 100-mmHg(analogous to healthy human blood pressure)or 200-mmHg(analogous to hypertension)hydrostatic pressure for 48 h,HASMCs showed six distinct vascular SMC(VSMC)clusters according to differential gene expression and gene ontology enrichment analysis.Especially,two novel HASMC subsets were identified,named the inflammatory subset,with CXCL2,CXCL3 and CCL2 as markers,and the endothelial-function inhibitory subset,with AKR1C2,AKR1C3,SERPINF1 as markers.The inflammatory subset promoted CXCL2&3 and CCL2 chemokine expression and secretion,triggering monocyte migration;the endothelial-function inhibitory subset secreted SERPINF1 and accelerated prostaglandin F2αgeneration to inhibit angiogenesis.The expression of the two VSMC subsets was greatly increased in arterial media from patients with hypertension and experimental animal models of hypertension.Collectively,we identified high hydrostatic pressure directly driving VSMCs into two new subsets,promoting or exacerbating endothelial dysfunction,thereby contributing to the pathogenesis of cardiovascular diseases.

单细胞测序数据的生物信息学分析方法与应用

单细胞测序技术是一项新技术。文章从单细胞测序技术的基本原理和发展历程入手,突出其在生物医学领域的重要性,详细探讨生物信息学分析的关键步骤,包括数据预处理、细胞异质性分析、基因表达分析以及轨迹推断和空间基因组学等高级技术,阐述单细胞测序在癌症研究、发育生物学、免疫学和神经科学等领域的应用,揭示其在理解细胞异质性和生物过程动态中的核心作用。文章旨在为研究人员提供有关单细胞测序技术及其分析方法的全面概述,展望其在生物医学研究领域的广阔应用前景。

基于多头自编码网络的单细胞多组学数据无监督降噪

单细胞多组学测序正在广泛应用于生物医学研究中,并产生大量的多样性组学数据。然而原始的单细胞多组学数据包含多种类型的测序噪声和冗余信息,对后续生物医疗层面的分析造成困难。现有的降噪方法主要依赖于单一的数据分布假设,并针对性的处理单个组学数据,这对模型联合处理不同组学数据造成极大地限制。本研究提出一种使用单细胞多组学数据降噪的分析方法,称为scMAED(single-cell multi-omics data via a multi-head autoencoder network to denoising)。模型在多头自动编码器网络中添加了分类解码器,以无监督的方式来最大程度的去除数据噪声。首先,使用两个编码器独立学习多组学数据的内部特征,并联合输出的低维特征进行共同解码。其次,分类解码器不做任何数据分布假设,通过使用预测的细胞簇标签来反馈数据信息,以最大限度的去除复杂噪声。最后,使用主成分分析和t-SNE进行可视化。本文基于模拟数据集和真实的小鼠数据集对模型进行性能评估,结果显示sc-MAED在降噪效果上优于实验中的对比方法,并能够极大的改善单细胞多组学数据的质量。

Cross-species single-cell transcriptomic analysis of animal gastric antrum reveals intense porcine mucosal immunity

As an important part of the stomach,gastric antrum secretes gastrin which can regulate acid secretion and gastric emptying.Although most cell types in the gastric antrum are identified,the comparison of cell composition and gene expression in the gastric antrum among different species are not explored.In this study,we collected antrum epithelial tissues from human,pig,rat and mouse for scRNA-seq and compared cell types and gene expression among species.In pig antral epithelium,we identified a novel cell cluster,which is marked by high expression of AQP5,F3,CLCA1 and RRAD.We also discovered that the porcine antral epithelium has stronger immune function than the other species.Further analysis revealed that this may be due to the insufficient function of porcine immune cells.Together,our results replenish the information of multiple species of gastric antral epithelium at the single cell level and provide resources for understanding the homeostasis maintenance and regeneration of gastric antrum epithelium.

Decoding nervous system by single-cell RNA sequencing

Background： Mammalian brain are composed of a large number of specialized cell types with diverse molecular composition, functions and differentiation potentials. The application of recently developed single-cell RNA sequencing （scRNA-seq） technology in this filed has provided us new insights about this sophisticated system, deepened our understanding of the cell type diversity and led to the discovery of novel cell types. Results： Here we review recent progresses of applying this technology on studying brain cell heterogeneity, adult neurogenesis as well as brain tumors, then we discuss some current limitations and future directions of using scRNA- seq on the investagation of nervous system. Conclusions： We believe the application of single-celi RNA sequencing in neuroscience will accelerate the progress of big brain projects.

单细胞转录组学结合孟德尔分析探讨扩张性心肌病潜在治疗靶点

目的 在通过分析多组转录组和单细胞数据集,深入探究扩张性心肌病(DCM)的分子机制,并寻找潜在的治疗靶点。方法 (1)数据获取与预处理。从GEO数据库获取多个转录组和单细胞数据集,并利用SVA软件去除批次效应。(2)差异基因分析。通过PCA和差异分析,筛选出与DCM相关的关键基因。(3)功能富集分析。利用GO和KEGG富集分析,探讨这些基因在生物学过程中的功能。(4)关键基因筛选。采用共识聚类、LASSO和随机森林等算法,从差异基因中筛选出关键基因。(5)因果关系分析。进行孟德尔随机化分析,验证关键基因与DCM之间的因果关系。(6)细胞类型分析。利用单细胞数据分析确定关键基因在不同细胞类型中的表达情况。结果 (1)鉴定出788个与DCM相关的关键基因。(2)GO和KEGG分析揭示了这些基因在多个生物学通路中发挥重要作用。(3)筛选出FGFR3、RTKN2和SLC9A3R1三个关键基因。(3)孟德尔随机化分析证实SLC9A3R1与DCM存在因果关系。(4)单细胞数据分析显示SLC9A3R1在心肌细胞中高表达。结论 本研究通过多组学分析,深入解析了DCM的分子机制。SLC9A3R1作为新发现的关键基因在DCM的发病过程中可能扮演重要角色,有望成为新的治疗靶点。这些研究结果为DCM的诊疗提供了新的思路和方向。

基于单细胞数据挖掘、转录组学及网络药理学探讨暖心康防治慢性心力衰竭的机制

目的医院院内制剂暖心康在治疗慢性心力衰竭中取得良好效果,但其潜在的防治慢性心衰的具体分子机制尚不十分明确,拟通过单细胞数据挖掘、转录组学、网络药理学及动物实验等探讨暖心康防治慢性心力衰竭的机制。方法随机将40只C57/BL6雄性小鼠分成4组,假手术组(Sham)10只、模型组(TAC)10只、生理盐水组(Vehicle)10只、暖心康组(NXK)10只。动物模型制作采用主动脉弓缩窄术,先后予小动物超声心功能检测、HE染色、Masson染色、钙黄绿染色、实时荧光定量PCR(qPCR)验证暖心康防治慢性心衰。再予GEO数据库下载人源单细胞数据集(GSE95140)进行下游分析以得到慢性心衰差异基因,将暖心康药物-慢性心力衰竭疾病进行网络药理分析得到相应的药物-疾病靶点,联合转录组学所得关键靶基因进行网络互作,得到三者交集基因及相关GO条目和通路,将得到的预测基因予WB进行验证。结果在各项验证中TAC组与Vehicle组数据比较无统计学意义。而与Sham组相比,心脏体质量及质量联合超声显示TAC组、Vehicle组的HW/BW值明显升高(P<0.01),而EF值、FS值明显降低(P<0.01),HE、Masson染色、钙黄绿染色显示TAC组、Vehicle组心肌纤维化明显、心肌细胞排列紊乱及心肌细胞变大(P<0.01),qPCR显示TAC组、Vehicle组ANP、BNP、Myh7、Collagen I、Collagen III等基因的表达升高(P<0.01);与TAC组、Vehicle组比较,NXK组HW/BW值降低(P<0.05),而EF值、FS值均明显升高(P<0.01),HE、Masson染色、钙黄绿染色显示NXK组心肌纤维化、心肌细胞排列及心肌细胞大小改善明显(P<0.01),qPCR显示TAC组、Vehicle组ANP、BNP、MYH7、Collagen I、Collagen III等基因的表达降低(P<0.01);经单细胞数据挖掘所得差异基因、课题组前期心衰小鼠转录组数据所得差异基因及暖心康网络药理所得潜在治疗慢性心衰靶基因三者进行网络互作分析得到3个治疗慢性心力衰竭的核心基因,分别为SLC8A1、RYR2、NFIB;桑基图显示GO富集所得条目与钙流、跨膜转运、能量代谢、氧化磷酸化、氧化应激、凋亡等生物过程相关。WB验证实验显示,与Sham组相比,TAC组、Vehicle组RYR2、p-RYR2(ser2808)、Calpain1、Calpain2、NF-κB p65的蛋白表达量明显升高(P<0.01);与TAC、Vehicle组相比,NXK组RYR2、p-RYR2(ser2808)、Calpain1、Calpain2、NF-κB p65的蛋白表达量降低(P<0.01)。结论暖心康可改善慢性心力衰竭心室重构,其可能通过RYR2调控钙泄漏激活钙蛋白酶(Calpain1、Calpain2),进而激活NF-κB通路来发挥作用。

单细胞RNA测序数据分析方法研究进展

单细胞RNA测序(Single cell RNA sequencing,scRNA-Seq)已经广泛应用于细胞分化、肿瘤微环境及多种疾病病因学研究。目前,由于scRNA-Seq具有低捕获率、高噪声、高变异性等特点,通过优化数据分析方法提高测序结果准确性已经成为测序领域的研究热点。对近年来数据分析过程中利用的数学方法进行了总结,讨论了数据分析的优势及存在的问题,以期为新算法的开发和应用提供参考,逐步提高测序结果的可靠性。