System-level analyses of keystone genes required for mammalian tooth development

AbstractWhen a null mutation of a gene causes a complete developmental arrest, the gene is typically considered essential for life. Yet, in most cases null mutations have more subtle effects on the phenotype. Here we used the phenotypic severity of mutations as a tool to examine system-level dynamics of gene expression. We classify genes required for the normal development of the mouse molar into different categories that range from essential to subtle modification of the phenotype. Collectively, we call these the developmental keystone genes. Transcriptome profiling using microarray and RNAseq analyses of patterning stage mouse molars show highly elevated expression levels for genes essential for the progression of tooth development, a result reminiscent of essential genes in single cell organisms. Elevated expression levels of progression genes were also detected in developing rat molars, suggesting evolutionary conservation of this system-level dynamics. Single-cell RNAseq analyses of developing mouse molars reveal that even though the size of the expression domain, measured in number of cells, is the main driver of organ-level expression, progression genes show high cell-level transcript abundances. Progression genes are also upregulated within their pathways, which themselves are highly expressed. In contrast, a high proportion of the genes required for normal tooth patterning are secreted ligands that are expressed in fewer cells than their receptors and intracellular components. Overall, even though expression patterns of individual genes can be highly different, conserved system-level principles of gene expression can be detected using phenotypically defined gene categories.

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The single-cell transcriptional landscape of the axolotl

10.21203/rs.3.rs-960203/v1 ◽

2021 ◽

Author(s):

Fang Ye ◽

Guodong Zhang ◽

Weigao E ◽

Haide Chen ◽

Chengxuan Yu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Limb Development ◽

Single Cell Analysis ◽

Single Cells ◽

Expression Patterns ◽

Ambystoma Mexicanum ◽

System Level ◽

Molecular Characteristics ◽

Developmental Studies

Abstract The Mexican axolotl (Ambystoma mexicanum) is a promising tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, during which their regeneration capacity and lifespan gradually decline. However, a system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls is still lacking. Here, we developed a single-cell RNA-seq method based on combinatorial hybridization to generate a tissue-based transcriptomic atlas of the adult axolotl. We performed gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell atlas. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls revealed the heterogeneity of structural cells in different tissues and established their regulatory network. Furthermore, we described dynamic gene expression patterns during limb development in neotenic axolotls. These data serve as a resource to explore the molecular identity of the axolotl as well as its metamorphosis.

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scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

Bioinformatics ◽

10.1093/bioinformatics/btaa108 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3131-3138

Author(s):

Ke Jin ◽

Le Ou-Yang ◽

Xing-Ming Zhao ◽

Hong Yan ◽

Xiao-Fei Zhang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Supplementary Information ◽

Expression Levels ◽

Single Cell Rna Sequencing ◽

Downstream Analysis ◽

Gene Expression Levels

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽

10.1016/j.isci.2021.102357 ◽

2021 ◽

Vol 24 (4) ◽

pp. 102357

Author(s):

Brenda Morsey ◽

Meng Niu ◽

Shetty Ravi Dyavar ◽

Courtney V. Fletcher ◽

Benjamin G. Lamberty ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Related Gene ◽

Single Cell Rna Sequencing ◽

Disease Related Gene

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Combined Metabolome and Transcriptome Profiling Reveal Optimal Harvest Strategy Model Based on Different Production Purposes in Olive

Foods ◽

10.3390/foods10020360 ◽

2021 ◽

Vol 10 (2) ◽

pp. 360

Author(s):

Guodong Rao ◽

Jianguo Zhang ◽

Xiaoxia Liu ◽

Xue Li ◽

Chenhe Wang

Keyword(s):

Gene Expression ◽

Olive Oil ◽

Expression Patterns ◽

Transcriptome Profiling ◽

Minor Components ◽

Harvest Time ◽

Rna Seq ◽

Optimal Harvest ◽

Harvest Strategy ◽

Different Color

Olive oil has been favored as high-quality edible oil because it contains balanced fatty acids (FAs) and high levels of minor components. The contents of FAs and minor components are variable in olive fruits of different color at harvest time, which render it difficult to determine the optimal harvest strategy for olive oil producing. Here, we combined metabolome, Pacbio Iso-seq, and Illumina RNA-seq transcriptome to investigate the association between metabolites and gene expression of olive fruits at harvest time. A total of 34 FAs, 12 minor components, and 181 other metabolites (including organic acids, polyols, amino acids, and sugars) were identified in this study. Moreover, we proposed optimal olive harvesting strategy models based on different production purposes. In addition, we used the combined Pacbio Iso-seq and Illumina RNA-seq gene expression data to identify genes related to the biosynthetic pathways of hydroxytyrosol and oleuropein. These data lay the foundation for future investigations of olive fruit metabolism and gene expression patterns, and provide a method to obtain olive harvesting strategies for different production purposes.

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Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab021 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Hitoshi Iuchi ◽

Michiaki Hamada

Keyword(s):

Gene Expression ◽

Time Series ◽

Time Course ◽

Time Series Data ◽

Expression Patterns ◽

Detection Methods ◽

Series Data ◽

Expression Levels ◽

Over Time ◽

Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

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Joint profiling of gene expression and chromatin accessibility of amphioxus development at single cell resolution

10.21203/rs.3.rs-504113/v1 ◽

2021 ◽

Author(s):

Pengcheng Ma ◽

Xingyan Liu ◽

Huimin Liu ◽

Zaoxu Xu ◽

Xiangning Ding ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Functional Divergence ◽

Expression Patterns ◽

Genetic Regulatory Networks ◽

Public Access ◽

Origin And Evolution ◽

Key Species

Abstract Vertebrate evolution was accompanied with two rounds of whole genome duplication followed by functional divergence in terms of regulatory circuits and gene expression patterns. As a basal and slow-evolving chordate species, amphioxus is an ideal paradigm for exploring the origin and evolution of vertebrates. Single cell sequencing has been widely employed to construct the developmental cell atlas of several key species of vertebrates (human, mouse, zebrafish and frog) and tunicate (sea squirts). Here, we performed single-nucleus RNA sequencing (snRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) for different stages of amphioxus (covering embryogenesis and adult tissues). With the datasets generated we constructed the developmental tree for amphioxus cell fate commitment and lineage specification, and revealed the underlying key regulators and genetic regulatory networks. The generated data were integrated into an online platform, AmphioxusAtlas, for public access at http://120.79.46.200:81/AmphioxusAtlas.

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Batch effects and the effective design of single-cell gene expression studies

10.1101/062919 ◽

2016 ◽

Cited By ~ 11

Author(s):

Po-Yuan Tung ◽

John D. Blischak ◽

Chiaowen Joyce Hsiao ◽

David A. Knowles ◽

Jonathan E. Burnett ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Induced Pluripotent Stem Cell ◽

Expression Levels ◽

Amplification Bias ◽

Expression Studies ◽

Cell Gene Expression ◽

Gene Expression Studies ◽

Induced Pluripotent ◽

Gene Expression Levels

AbstractSingle cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.

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Inferring time-lagged causality using the derivative of single-cell expression

10.1101/2021.02.03.429525 ◽

2021 ◽

Author(s):

Huan-Huan Wei ◽

Hui Lu ◽

Hongyu Zhao

Keyword(s):

Gene Expression ◽

Causal Inference ◽

Single Cell ◽

Causal Relationship ◽

Gene Expression Data ◽

Expression Data ◽

Causal Relationships ◽

Expression Levels ◽

Gene Pairs ◽

Time Lagged

AbstractMany computational methods have been developed for inferring causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, we propose a method, called causal inference with time-lagged information (CITL), to infer time-lagged causal relationships from scRNA-seq data by assessing conditional independence between the changing and current expression levels of genes. CITL estimates the changing expression levels of genes by “RNA velocity”. We demonstrate the accuracy and stability of CITL for inferring time-lagged causality on simulation data against other leading approaches. We have applied CITL to real scRNA data and inferred 878 pairs of time-lagged causal relationships, with many of these inferred results supported by the literature.Author summaryComputational causal inference is a promising way to survey causal relationships between genes efficiently. Though many causal inference methods have been applied to gene expression data, none considers the time-lagged causal relationship, which means that some genes may take some time to affect their target genes with several reactions. If relationships between genes are time-lagged, the existing methods’ assumptions will be violated. The relationships will be challenging to recognize. We demonstrate that this is indeed the case through simulation. Therefore, we develop a method for inferring time-lagged causal relationships of single-cell gene expression data. We assume that a time-lagged causal relationship should present a strong association between the cause and the effect’s changing. To calculate such correlation, we first estimate the derivative of gene expression using the information from unspliced transcripts. Then, we use conditional independent tests to search gene pairs satisfying our assumption. Our results suggest that we could accurately infer time-lagged causal gene pairs validated by published literature. This method may complement gene regulatory analysis and provide candidate gene pairs for further controlled experiments.

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ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes

10.1101/2020.02.04.932988 ◽

2020 ◽

Cited By ~ 2

Author(s):

Kenneth H. Hu ◽

John P. Eichorst ◽

Chris S. McGinnis ◽

David M. Patterson ◽

Eric D. Chow ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Real Time ◽

Dimensional Space ◽

Single Cells ◽

Expression Patterns ◽

Live Cells ◽

Glass Substrates ◽

Complete Mapping

ABSTRACTSpatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

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