scholarly journals Ground tissue circuitry regulates organ complexity in cereal roots

2021 ◽  
Author(s):  
Carlos Ortiz-Ramírez ◽  
Poliana Coqueiro Dias Araujo ◽  
Sanqiang Zhang ◽  
Edgar Demesa-Arevalo ◽  
Zhe Yan ◽  
...  
Keyword(s):  
Single Cell ◽  
Maize Root ◽  
Rna Seq ◽  
Ground Tissue ◽  
Cortical Tissue ◽  
Cortical Layers ◽  
Short Root ◽  
Cell Layers ◽  
A Cell ◽  
Alternative Configuration

AbstractMost plant roots have multiple cortex layers that make up the bulk of the organ and play key roles in physiology, such as flood tolerance and symbiosis. However, little is known about the formation of cortical layers outside of the highly reduced anatomy of the model Arabidopsis. Here we use single-cell RNAseq to rapidly generate a cell resolution map of the maize root, revealing an alternative configuration of the tissue formative SHORT-ROOT (SHR) signaling pathway adjacent to an expanded cortex. We show that maize SHR protein is hypermobile, moving at least eight cell layers into the cortex. Higher-order SHR mutants in both maize and Setaria have reduced numbers of cortical layers, showing that the SHR pathway controls expansion of cortical tissue in grasses that sets up anatomical complexity and a host of key traits.One sentence summarySingle-cell RNA-seq maps the maize root transcriptome uncovering a mechanism that regulates cortex layer number.

scholarly journals SCSA: a cell type annotation tool for single-cell RNA-seq data

2019 ◽  
Author(s):  
Yinghao Cao ◽  
Xiaoyue Wang ◽  
Gongxin Peng
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Confidence Levels ◽  
User Expertise ◽  
Model Combining ◽  
A Cell ◽  
Automatic Tool ◽  
Different Sources

AbstractCurrently most methods take manual strategies to annotate cell types after clustering the single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and heavily rely on user expertise, which may lead to inconsistent results. We present SCSA, an automatic tool to annotate cell types from scRNA-seq data, based on a score annotation model combining differentially expressed genes (DEGs) and confidence levels of cell markers from both known and user-defined information. Evaluation on real scRNA-seq datasets from different sources with other methods shows that SCSA is able to assign the cells into the correct types at a fully automated mode with a desirable precision.

scholarly journals SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

2017 ◽  
Author(s):  
Bo Wang ◽  
Daniele Ramazzotti ◽  
Luca De Sano ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Cell Analysis ◽  
Rna Seq ◽  
A Cell ◽  
Supplementary Material ◽  
Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals LTMG: A novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

2018 ◽  
Author(s):  
Changlin Wan ◽  
Wennan Chang ◽  
Yu Zhang ◽  
Fenil Shah ◽  
Xiaoyu Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
R Package ◽  
Data Sets ◽  
Rna Seq ◽  
Cell Functions ◽  
Transcriptional Regulatory ◽  
A Cell

ABSTRACTA key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a gene’s diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

scholarly journals SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data

2020 ◽  
Vol 11 ◽  
Author(s):  
Yinghao Cao ◽  
Xiaoyue Wang ◽  
Gongxin Peng
Keyword(s):  
Single Cell ◽  
Annotation Tool ◽  
Rna Seq ◽  
Cell Type ◽  
A Cell

scholarly journals CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

2020 ◽  
Author(s):  
Lin Li ◽  
Hao Dai ◽  
Zhaoyuan Fang ◽  
Luonan Chen
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Network Flow ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Sequencing Data ◽  
Cell Clustering ◽  
A Cell

AbstractThe rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared with bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the “conditional cell-specific network” (CCSN) method, which can measure the direct associations between genes by eliminating the indirect associations. CCSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene-gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach: (1) one direct association network for one cell; (2) most existing scRNA-seq methods designed for gene expression matrices are also applicable to CCSN-transformed degree matrices; (3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. CCSN is publicly available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/CCSN.zip.

scholarly journals Discovering Novel Cell Types across Heterogeneous Single-cell Experiments

2020 ◽  
Author(s):  
Maria Brbić ◽  
Marinka Zitnik ◽  
Sheng Wang ◽  
Angela O. Pisco ◽  
Russ B. Altman ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Learning To Learn ◽  
Multiple Datasets ◽  
Temporal Relationships ◽  
Meta Learning ◽  
A Cell ◽  
Cell Type Specific

Although tremendous effort has been put into cell type annotation and classification, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as novel cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method annotates cells by probabilistically defining a cell type based on nearest landmarks in the embedding space. MARS has a unique ability to discover cell types that have never been seen before and annotate experiments that are yet unannotated. We apply MARS to a large aging cell atlas of 23 tissues covering the life span of a mouse. MARS accurately identifies cell types, even when it has never seen them before. Further, the method automatically generates interpretable names for novel cell types. Remarkably, MARS estimates meaningful cell-type-specific signatures of aging and visualizes them as trajectories reflecting temporal relationships of cells in a tissue.

scholarly journals Single-cell entropy to quantify the cellular transcriptome from single-cell RNA-seq data

2019 ◽  
Author(s):  
Jingxin Liu ◽  
You Song ◽  
Jinzhi Lei
Keyword(s):  
Single Cell ◽  
Gaussian Mixture ◽  
Rna Seq ◽  
Cell Type ◽  
Transcriptome Profile ◽  
Biological Interpretation ◽  
A Cell ◽  
Transcriptional State ◽  
Type Classification ◽  
Cellular Transcriptome

We present the use of single-cell entropy (scEntropy) to measure the order of the cellular transcriptome profile from single-cell RNA-seq data, which leads to a method of unsupervised cell type classification through scEntropy followed by the Gaussian mixture model (scEGMM). scEntropy is straightforward in defining an intrinsic transcriptional state of a cell. scEGMM is a coherent method of cell type classification that includes no parameters and no clustering; however, it is comparable to existing machine learning-based methods in benchmarking studies and facilitates biological interpretation.

scholarly journals Single-Cell RNA Sequencing of Batch Chlamydomonas Cultures Reveals Heterogeneity in their Diurnal Cycle Phase

2021 ◽  
Author(s):  
Feiyang Ma ◽  
Patrice A Salomé ◽  
Sabeeha S Merchant ◽  
Matteo Pellegrini
Keyword(s):  
Cell Wall ◽  
Single Cell ◽  
Rna Sequencing ◽  
Environmental Changes ◽  
Single Cells ◽  
Nitrogen Deficiency ◽  
Cycle Phase ◽  
Rna Seq ◽  
Single Cell Rna Sequencing ◽  
A Cell

Abstract The photosynthetic unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) is a versatile reference for algal biology because of its ease of culture in the laboratory. Genomic and systems biology approaches have previously described transcriptome responses to environmental changes using bulk data, thus representing the average behavior from pools of cells. Here, we apply single-cell RNA sequencing (scRNA-seq) to probe the heterogeneity of Chlamydomonas cell populations under three environments and in two genotypes differing by the presence of a cell wall. First, we determined that RNA can be extracted from single algal cells with or without a cell wall, offering the possibility to sample natural algal communities. Second, scRNA-seq successfully separated single cells into non-overlapping cell clusters according to their growth conditions. Cells exposed to iron or nitrogen deficiency were easily distinguished despite a shared tendency to arrest photosynthesis and cell division to economize resources. Notably, these groups of cells recapitulated known patterns observed with bulk RNA-seq, but also revealed their inherent heterogeneity. A substantial source of variation between cells originated from their endogenous diurnal phase, although cultures were grown in constant light. We exploited this result to show that circadian iron responses may be conserved from algae to land plants. We document experimentally that bulk RNA-seq data represent an average of typically hidden heterogeneity in the population.

scholarly journals Imputing single-cell RNA-seq data by considering cell heterogeneity and prior expression of dropouts

2020 ◽  
Author(s):  
Lihua Zhang ◽  
Shihua Zhang
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Dropout Rate ◽  
Population Based ◽  
Low Rank ◽  
Cell Heterogeneity ◽  
Rna Seq ◽  
A Cell ◽  
Low Dimensional ◽  
The Relationship

Abstract Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to determine expression patterns of thousands of individual cells. However, the analysis of scRNA-seq data remains a computational challenge due to the high technical noise such as the presence of dropout events that lead to a large proportion of zeros for expressed genes. Taking into account the cell heterogeneity and the relationship between dropout rate and expected expression level, we present a cell sub-population based bounded low-rank (PBLR) method to impute the dropouts of scRNA-seq data. Through application to both simulated and real scRNA-seq datasets, PBLR is shown to be effective in recovering dropout events, and it can dramatically improve the low-dimensional representation and the recovery of gene‒gene relationships masked by dropout events compared to several state-of-the-art methods. Moreover, PBLR also detects accurate and robust cell sub-populations automatically, shedding light on its flexibility and generality for scRNA-seq data analysis.

scholarly journals CBA: Cluster-Guided Batch Alignment for Single Cell RNA-seq

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenbo Yu ◽  
Ahmed Mahfouz ◽  
Marcel J. T. Reinders
Keyword(s):  
Single Cell ◽  
Developmental Process ◽  
Mouse Cell ◽  
Cell Alignment ◽  
Rna Seq ◽  
Cell Type ◽  
Expression Of Genes ◽  
A Cell ◽  
Differential Expression Of Genes ◽  
Cell Cell

The power of single-cell RNA sequencing (scRNA-seq) in detecting cell heterogeneity or developmental process is becoming more and more evident every day. The granularity of this knowledge is further propelled when combining two batches of scRNA-seq into a single large dataset. This strategy is however hampered by technical differences between these batches. Typically, these batch effects are resolved by matching similar cells across the different batches. Current approaches, however, do not take into account that we can constrain this matching further as cells can also be matched on their cell type identity. We use an auto-encoder to embed two batches in the same space such that cells are matched. To accomplish this, we use a loss function that preserves: (1) cell-cell distances within each of the two batches, as well as (2) cell-cell distances between two batches when the cells are of the same cell-type. The cell-type guidance is unsupervised, i.e., a cell-type is defined as a cluster in the original batch. We evaluated the performance of our cluster-guided batch alignment (CBA) using pancreas and mouse cell atlas datasets, against six state-of-the-art single cell alignment methods: Seurat v3, BBKNN, Scanorama, Harmony, LIGER, and BERMUDA. Compared to other approaches, CBA preserves the cluster separation in the original datasets while still being able to align the two datasets. We confirm that this separation is biologically meaningful by identifying relevant differential expression of genes for these preserved clusters.

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