Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

Tian, Feng; Zhou, Fan; Li, Xiang; Ma, Wenping; Wu, Honggui; Yang, Ming; Chapman, Alec R.; Lee, David F.; Tan, Longzhi; Xing, Dong; Yin, Gao; Semayel, Ayjan; Wang, Jing; Wang, Jia; Sun, Wenjie; He, Runsheng; Zhang, Siwei; Cao, Zhi-Jie; Lin, Wei; Shen, Lu; Yang, De-Chang; Mao, Yunuo; Gao, Yuan; Chen, Kexuan; Zhang, Yu; Liu, Xixi; Yong, Jun; Yan, Liying; Huang, Yanyi; Qiao, Jie; Tang, Fuchou; Gao, Ge; Xie, X. Sunney

doi:10.1101/2020.04.12.038000

Cited by 4 publications

(7 citation statements)

References 93 publications

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“…High-throughput single-cell analysis has proved to be a valuable strategy for understanding the functional mechanisms of cells and tissues (7, 29). However, identifying functional gene expression programs from large-scale single-cell dataset has been challenging.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, single-cell transcriptomics study has been widely used to distinguish different types of cells. Apart from cell typing, high-precision single-cell transcriptomics can also measure correlations in steady-state gene expression (6, 7). However, proteins, rather than mRNAs, are direct executers of most biological processes.…”

Section: Introductionmentioning

confidence: 99%

“…Apart from cell typing, high-precision single-cell transcriptomics can also measure correlations in steadystate gene expression (6,7). However, proteins, rather than mRNAs, are direct executers of most biological processes.…”

Section: Introductionmentioning

confidence: 99%

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Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins are Detected by Single-cell Proteomics

Zhang

Yuan

et al. 2022

Preprint

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Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than single-cell transcriptomics. However, most work to date focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlations between the translational levels of any pair of proteins in a single mammalian cell. To do so, we chose label-free instead of isobaric labeling quantification for single-cell mass spectrometry in order to minimize measurement noise. In measuring pairwise correlations among ~1,000 proteins in steady-state K562 cells, we found multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functional interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Although widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations to bulk samples, which would mask the coordination of gene expression in steady-state condition. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins are Detected by Single-cell Proteomics

Zhang

Yuan

et al. 2022

Preprint

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“…However, these ‘basal programs’ may not necessarily represent real expression programs, they can also come from technical noise. Genes having protein products that interact with each other tend to have similar functions and co-expression patterns ( 16 ). To determine the genuine co-expression pattern shared among batches (or the featured expression module characterizing a give cell/tissue type), we incorporated PPI network information into ICAnet, and used the ‘basal programs’ to score PPI.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, using PPIs as the backbone to discover gene-expression modules in scRNA-seq analyses is now feasible. Previous studies have revealed that genes with PPIs showed co-expression trends at the RNA level ( 16 , 103 ), suggesting that integrating PPI information into scRNA-seq data analyses could be beneficial. In practice, we performed multiple simulations and statistical tests to demonstrate the importance of incorporating PPI networks into cell clustering and module interpretations of scRNA-seq data (see Supplementary Notes, Sections 2 and 3 in the Supplementary Materials).…”

Section: Discussionmentioning

confidence: 99%

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

Tan

Sun

Han

et al. 2021

Nucleic Acids Research

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With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.

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Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins Are Detected by Single-Cell Proteomics

Zhang

Yuan

et al. 2023

J. Phys. Chem. B

Self Cite

View full text Add to dashboard Cite

Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than singlecell transcriptomics. However, most work to date has focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlation between the translational levels of a pair of proteins in a single mammalian cell. In measuring pairwise correlations among ∼1000 proteins in a population of homogeneous K562 cells under a steady-state condition, we observed multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functionally interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations into bulk samples. However, some correlations of gene or protein expression under the steady-state condition would be masked by perturbation. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs.

show abstract

Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

Cited by 4 publications

References 93 publications

Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins are Detected by Single-cell Proteomics

Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins are Detected by Single-cell Proteomics

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins Are Detected by Single-Cell Proteomics

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