scholarly journals Enhancing single-cell cellular state inference by incorporating molecular network features

2019 ◽  
Author(s):  
Ji Dong ◽  
Peijie Zhou ◽  
Yichong Wu ◽  
Wendong Wang ◽  
Yidong Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Goblet Cells ◽  
Summary Score ◽  
Molecular Network ◽  
Molecular Networks ◽  
Gene Interactions ◽  
Rna Seq ◽  
Dynamic Changes ◽  
State Inference ◽  
Microfold Cells

AbstractIn biological systems, genes function in conjunction rather than in isolation. However, traditional single-cell RNA-seq (scRNA-seq) analyses heavily rely on the transcriptional similarity of individual genes, ignoring the inherent gene-gene interactions. Here, we present SCORE, a network-based method, which incorporates the validated molecular network features to infer cellular states. Using real scRNA-seq datasets, SCORE outperforms existing methods in accuracy, robustness, scalability, data integration and removal of batch effect. When applying SCORE to a newly generated human ileal scRNA-seq dataset, we identified several novel stem/progenitor clusters, including a Cripto-1+ cluster. Moreover, two distinct groups of goblet cells were identified and only one of them tended to secrete mucus. Besides, we found that the recently identified BEST4+OTOP2+ microfold cells also highly expressed CFTR, which is different from their colonic counterparts. In summary, SCORE enhances cellular state inference by simulating the dynamic changes of molecular networks, providing more biological insights beyond statistical interpretations.

Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features

2021 ◽  
Author(s):  
Ji Dong ◽  
Peijie Zhou ◽  
Yichong Wu ◽  
Yidong Chen ◽  
Haoling Xie ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Developmental Stages ◽  
Rapid Development ◽  
Molecular Network ◽  
Rna Seq ◽  
Single Cell Sequencing ◽  
The World ◽  
Information Score ◽  
Simple Network

Abstract With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration.

scholarly journals Dynamic changes in the regulatory T-cell heterogeneity and function by murine IL-2 mutein

2020 ◽  
Vol 3 (5) ◽  
pp. e201900520 ◽  
Author(s):  
Daniel R Lu ◽  
Hao Wu ◽  
Ian Driver ◽  
Sarah Ingersoll ◽  
Sue Sohn ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Control Cell ◽  
Mutant Form ◽  
Rna Seq ◽  
Dynamic Changes ◽  
Cell Clustering ◽  
Transcriptional Changes ◽  
And Function

The therapeutic expansion of Foxp3+ regulatory T cells (Tregs) shows promise for treating autoimmune and inflammatory disorders. Yet, how this treatment affects the heterogeneity and function of Tregs is not clear. Using single-cell RNA-seq analysis, we characterized 31,908 Tregs from the mice treated with a half-life extended mutant form of murine IL-2 (IL-2 mutein, IL-2M) that preferentially expanded Tregs, or mouse IgG Fc as a control. Cell clustering analysis revealed that IL-2M specifically expands multiple sub-states of Tregs with distinct expression profiles. TCR profiling with single-cell analysis uncovered Treg migration across tissues and transcriptional changes between clonally related Tregs after IL-2M treatment. Finally, we identified IL-2M–expanded Tnfrsf9+Il1rl1+ Tregs with superior suppressive function, highlighting the potential of IL-2M to expand highly suppressive Foxp3+ Tregs.

scholarly journals Smoking modulates different secretory subpopulations expressing SARS-CoV-2 entry genes in the nasal and bronchial airways

2021 ◽  
Author(s):  
Ke Xu ◽  
Xingyi Shi ◽  
Chris Husted ◽  
Rui Hong ◽  
Yichen Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Factors ◽  
Epithelial Cells ◽  
Viral Infection ◽  
Single Cell ◽  
Goblet Cells ◽  
Secretory Cells ◽  
Gene Module ◽  
Rna Seq ◽  
Ve Gene

AbstractCoronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 SARS-CoV-2), which infects host cells with help from the Viral Entry (VE) proteins ACE2, TMPRSS2, and CTSL1–4. Proposed risk factors for viral infection, as well as the rate of disease progression, include age5,6, sex7, chronic obstructive pulmonary disease7,8, cancer9, and cigarette smoking10–13. To investigate whether the proposed risk factors increase viral infection by modulation of the VE genes, we examined gene expression profiles of 796 nasal and 1,673 bronchial samples across four lung cancer screening cohorts containing individuals without COVID-19. Smoking was the only clinical factor reproducibly associated with the expression of any VE gene across cohorts. ACE2 expression was significantly up-regulated with smoking in the bronchus but significantly down-regulated with smoking in the nose. Furthermore, expression of individual VE genes were not correlated between paired nasal and bronchial samples from the same patients. Single-cell RNA-seq of nasal brushings revealed that an ACE2 gene module was detected in a variety of nasal secretory cells with the highest expression in the C15orf48+ secretory cells, while a TMPRSS2 gene module was most highly expressed in nasal keratinizing epithelial cells. In contrast, single-cell RNA-seq of bronchial brushings revealed that ACE2 andTMPRSS2 gene modules were most enriched in MUC5AC+ bronchial goblet cells. The CTSL gene module was highly expressed in immune populations of both nasal and bronchial brushings. Deconvolution of bulk RNA-seq showed that the proportion of MUC5AC+ goblet cells was increased in current smokers in both the nose and bronchus but proportions of nasal keratinizing epithelial cells, C15orf48+ secretory cells, and immune cells were not associated with smoking status. The complex association between VE gene expression and smoking in the nasal and bronchial epithelium revealed by our results may partially explain conflicting reports on the association between smoking and SARS-CoV-2 infection.

scholarly journals Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks

2020 ◽  
Author(s):  
Jiahua Rao ◽  
Xiang Zhou ◽  
Yutong Lu ◽  
Huiying Zhao ◽  
Yuedong Yang
Keyword(s):  
Single Cell ◽  
Clustering Analysis ◽  
State Of The Art ◽  
Differential Expression Analysis ◽  
Gene Interactions ◽  
Rna Seq ◽  
Sequencing Technology ◽  
Imputation Methods ◽  
Downstream Analysis ◽  
Low Dimensional

AbstractSingle-cell RNA sequencing technology promotes the profiling of single-cell transcriptomes at an unprecedented throughput and resolution. However, in scRNA-seq studies, only a low amount of sequenced mRNA in each cell leads to missing detection for a portion of mRNA molecules, i.e. the dropout problem. The dropout event hinders various downstream analysis, such as clustering analysis, differential expression analysis, and inference of gene-to-gene relationships. Therefore, it is necessary to develop robust and effective imputation methods for the increasing scRNA-seq data. In this study, we have developed an imputation method (GraphSCI) to impute the dropout events in scRNA-seq data based on the graph convolution networks. The method takes advantage of low-dimensional representations of similar cells and gene-gene interactions to impute the dropouts. Extensive experiments demonstrated that GraphSCI outperforms other state-of-the-art methods for imputation on both simulated and real scRNA-seq data. Meanwhile, GraphSCI is able to accurately infer gene-to-gene relationships by utilizing the imputed matrix that are concealed by dropout events in raw data.

scholarly journals Integrative Single-cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Haematopoiesis

2020 ◽  
Author(s):  
Anna Maria Ranzoni ◽  
Andrea Tangherloni ◽  
Ivan Berest ◽  
Simone Giovanni Riva ◽  
Brynelle Myers ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Blood Cells ◽  
Chromatin Accessibility ◽  
Haematopoietic Stem Cell ◽  
Rna Seq ◽  
Dynamic Changes ◽  
Multipotent Progenitors ◽  
Foetal Liver ◽  
Distinct Lineage

AbstractRegulation of haematopoiesis during human development remains poorly defined. Here, we applied single-cell (sc)RNA-Seq and scATAC-Seq analysis to over 8,000 human immunophenotypic blood cells from foetal liver and bone marrow. We inferred their differentiation trajectory and identified three highly proliferative oligopotent progenitor populations downstream from haematopoietic stem cell/multipotent progenitors (HSC/MPPs). Along this trajectory, we observed opposing patterns of chromatin accessibility and differentiation that coincided with dynamic changes in the activity of distinct lineage-specific transcription factors. Integrative analysis of chromatin accessibility and gene expression revealed extensive epigenetic but not transcriptional priming of HSC/MPPs prior to their lineage commitment. Finally, we refined and functionally validated the sorting strategy for the HSC/MPPs and achieved around 90% enrichment. Our study provides a useful framework for future investigation of human developmental haematopoiesis in the context of blood pathologies and regenerative medicine.

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