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Peng H, Xu J, Liu K, Liu F, Zhang A, Zhang X. EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors. Brief Funct Genomics 2024; 23:373-383. [PMID: 37642217 DOI: 10.1093/bfgp/elad040] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/07/2023] [Accepted: 08/14/2023] [Indexed: 08/31/2023] Open
Abstract
Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.
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Affiliation(s)
- Huixiang Peng
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Kangchen Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Fang Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan 430074, China
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2
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Pal S, Dhar R. Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making. FEBS Lett 2024; 598:1673-1691. [PMID: 38724715 DOI: 10.1002/1873-3468.14898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 07/23/2024]
Abstract
The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.
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Affiliation(s)
- Sampriti Pal
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
| | - Riddhiman Dhar
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
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3
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Chang X, Zheng Y, Xu K. Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research. Mol Biotechnol 2024; 66:1497-1519. [PMID: 37322261 PMCID: PMC11217094 DOI: 10.1007/s12033-023-00777-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/23/2023] [Indexed: 06/17/2023]
Abstract
Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.
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Affiliation(s)
- Xu Chang
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Yunxi Zheng
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Kai Xu
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China.
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4
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Kim GD, Shin SI, Jung SW, An H, Choi SY, Eun M, Jun CD, Lee S, Park J. Cell Type- and Age-Specific Expression of lncRNAs across Kidney Cell Types. J Am Soc Nephrol 2024; 35:870-885. [PMID: 38621182 PMCID: PMC11230714 DOI: 10.1681/asn.0000000000000354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/08/2024] [Indexed: 04/17/2024] Open
Abstract
Key Points
We constructed a single-cell long noncoding RNA atlas of various tissues, including normal and aged kidneys.We identified age- and cell type–specific expression changes of long noncoding RNAs in kidney cells.
Background
Accumulated evidence demonstrates that long noncoding RNAs (lncRNAs) regulate cell differentiation and homeostasis, influencing kidney aging and disease. Despite their versatility, the function of lncRNA remains poorly understood because of the lack of a reference map of lncRNA transcriptome in various cell types.
Methods
In this study, we used a targeted single-cell RNA sequencing method to enrich and characterize lncRNAs in individual cells. We applied this method to various mouse tissues, including normal and aged kidneys.
Results
Through tissue-specific clustering analysis, we identified cell type–specific lncRNAs that showed a high correlation with known cell-type marker genes. Furthermore, we constructed gene regulatory networks to explore the functional roles of differentially expressed lncRNAs in each cell type. In the kidney, we observed dynamic expression changes of lncRNAs during aging, with specific changes in glomerular cells. These cell type– and age-specific expression patterns of lncRNAs suggest that lncRNAs may have a potential role in regulating cellular processes, such as immune response and energy metabolism, during kidney aging.
Conclusions
Our study sheds light on the comprehensive landscape of lncRNA expression and function and provides a valuable resource for future analysis of lncRNAs (https://gist-fgl.github.io/sc-lncrna-atlas/).
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Affiliation(s)
- Gyeong Dae Kim
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - So-I Shin
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Su Woong Jung
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
- Division of Nephrology, Department of Internal Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Hyunsu An
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Sin Young Choi
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Minho Eun
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Chang-Duk Jun
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Sangho Lee
- Division of Nephrology, Department of Internal Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
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5
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Nouri N, Gaglia G, Mattoo H, de Rinaldis E, Savova V. GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions. CELL REPORTS METHODS 2024; 4:100794. [PMID: 38861988 PMCID: PMC11228368 DOI: 10.1016/j.crmeth.2024.100794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/28/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.
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Affiliation(s)
- Nima Nouri
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA.
| | - Giorgio Gaglia
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Hamid Mattoo
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Emanuele de Rinaldis
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Virginia Savova
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA.
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6
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Gan Y, Yu J, Xu G, Yan C, Zou G. Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding. Bioinformatics 2024; 40:btae291. [PMID: 38810116 PMCID: PMC11142726 DOI: 10.1093/bioinformatics/btae291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/06/2024] [Accepted: 05/28/2024] [Indexed: 05/31/2024] Open
Abstract
MOTIVATION Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships. RESULTS In this study, we develop a supervised deep learning framework, IGEGRNS, to infer GRNs from scRNA-seq data based on graph embedding. In the framework, contextual information of genes is captured by GraphSAGE, which aggregates gene features and neighborhood structures to generate low-dimensional embedding for genes. Then, the k most influential nodes in the whole graph are filtered through Top-k pooling. Finally, potential regulatory relationships between genes are predicted by stacking CNNs. Compared with nine competing supervised and unsupervised methods, our method achieves better performance on six time-series scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION Our method IGEGRNS is implemented in Python using the Pytorch machine learning library, and it is freely available at https://github.com/DHUDBlab/IGEGRNS.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai 201620, China
| | - Jiacheng Yu
- School of Computer Science and Technology, Donghua University, Shanghai 201620, China
| | - Guangwei Xu
- School of Computer Science and Technology, Donghua University, Shanghai 201620, China
| | - Cairong Yan
- School of Computer Science and Technology, Donghua University, Shanghai 201620, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
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7
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Grath A, Dai G. SOX17/ETV2 improves the direct reprogramming of adult fibroblasts to endothelial cells. CELL REPORTS METHODS 2024; 4:100732. [PMID: 38503291 PMCID: PMC10985233 DOI: 10.1016/j.crmeth.2024.100732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 11/07/2023] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
An autologous source of vascular endothelial cells (ECs) is valuable for vascular regeneration and tissue engineering without the concern of immune rejection. The transcription factor ETS variant 2 (ETV2) has been shown to directly convert patient fibroblasts into vascular EC-like cells. However, reprogramming efficiency is low and there are limitations in EC functions, such as eNOS expression. In this study, we directly reprogram adult human dermal fibroblasts into reprogrammed ECs (rECs) by overexpressing SOX17 in conjunction with ETV2. We find several advantages to rEC generation using this approach, including improved reprogramming efficiency, increased enrichment of EC genes, formation of large blood vessels carrying blood from the host, and, most importantly, expression of eNOS in vivo. From these results, we present an improved method to reprogram adult fibroblasts into functional ECs and posit ideas for the future that could potentially further improve the reprogramming process.
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Affiliation(s)
- Alexander Grath
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Guohao Dai
- Department of Bioengineering, Northeastern University, Boston, MA, USA.
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8
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Alghamdi S, Turki T. A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks. Sci Rep 2024; 14:4491. [PMID: 38396138 PMCID: PMC10891129 DOI: 10.1038/s41598-024-54923-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/18/2024] [Indexed: 02/25/2024] Open
Abstract
Accurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learning useful feature representation. However, existing DL tools are far from perfect and do not provide appropriate interpretation as a guideline to explain and promote superior performance in the target task. Therefore, we provide an interpretable approach for our presented deep transfer learning (DTL) models to overcome such drawbacks, working as follows. We utilize several pre-trained models including SEResNet152, and SEResNeXT101. Then, we transfer knowledge from pre-trained models via keeping the weights in the convolutional base (i.e., feature extraction part) while modifying the classification part with the use of Adam optimizer to deal with classifying healthy controls and T2D based on single-cell gene regulatory network (SCGRN) images. Another DTL models work in a similar manner but just with keeping weights of the bottom layers in the feature extraction unaltered while updating weights of consecutive layers through training from scratch. Experimental results on the whole 224 SCGRN images using five-fold cross-validation show that our model (TFeSEResNeXT101) achieving the highest average balanced accuracy (BAC) of 0.97 and thereby significantly outperforming the baseline that resulted in an average BAC of 0.86. Moreover, the simulation study demonstrated that the superiority is attributed to the distributional conformance of model weight parameters obtained with Adam optimizer when coupled with weights from a pre-trained model.
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Affiliation(s)
- Sumaya Alghamdi
- Department of Computer Science, King Abdulaziz University, 21589, Jeddah, Saudi Arabia
- Department of Computer Science, Albaha University, 65799, Albaha, Saudi Arabia
| | - Turki Turki
- Department of Computer Science, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
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9
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Li G, Zhao H, Cheng Z, Liu J, Li G, Guo Y. Single-cell transcriptomic profiling of heart reveals ANGPTL4 linking fibroblasts and angiogenesis in heart failure with preserved ejection fraction. J Adv Res 2024:S2090-1232(24)00068-7. [PMID: 38346487 DOI: 10.1016/j.jare.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 02/19/2024] Open
Abstract
INTRODUCTION Despite the high morbidity and mortality, the effective therapies for heart failure with preserved fraction (HFpEF) are limited as the poor understand of its pathophysiological basis. OBJECTIVE This study was aimed to characterize the cellular heterogeneity and potential mechanisms of HFpEF at single-cell resolution. METHODS An HFpEF mouse model was induced by a high-fat diet with N-nitro-L-arginine methyl ester. Cells from the hearts were subjected to single-cell sequencing. The key protein expression was measured with Immunohistochemistry and immunofluorescence staining. RESULTS In HFpEF hearts, myocardial fibroblasts exhibited higher levels of fibrosis. Furthermore, an increased number of fibroblasts differentiated into high-metabolism and high-fibrosis phenotypes. The expression levels of genes encoding certain pro-angiogenic secreted proteins were decreased in the HFpEF group, as confirmed by bulk RNA sequencing. Additionally, the proportion of the endothelial cell (EC) lineages in the HFpEF group was significantly downregulated, with low angiogenesis and high apoptosis phenotypes observed in these EC lineages. Interestingly, the fibroblasts in the HFpEF heart might cross-link with the EC lineages via over-secretion of ANGPTL4, thus displaying an anti-angiogenic function. Immunohistochemistry and immunofluorescence staining then revealed the downregulation of vascular density and upregulation of ANGPTL4 expression in HFpEF hearts. Finally, we predicted ANGPTL4as a potential druggable target using DrugnomeAI. CONCLUSION In conclusion, this study comprehensively characterized the angiogenesis impairment in HFpEF hearts at single-cell resolution and proposed that ANGPTL4 secretion by fibroblasts may be a potential mechanism underlying this angiogenic abnormality.
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Affiliation(s)
- Guoxing Li
- Institute of Life Sciences, Chongqing Medical University, 400016, China
| | - Huilin Zhao
- Institute of Life Sciences, Chongqing Medical University, 400016, China
| | - Zhe Cheng
- Department of Cardiology, Chongqing University Three Gorges Hospital, Chongqing 404199, China
| | - Junjin Liu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Gang Li
- Institute of Life Sciences, Chongqing Medical University, 400016, China; Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, 400016, China.
| | - Yongzheng Guo
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
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10
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Bhuvaneshwar K, Gusev Y. Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review. Brief Bioinform 2024; 25:bbae098. [PMID: 38493340 PMCID: PMC10944574 DOI: 10.1093/bib/bbae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/23/2024] [Accepted: 02/18/2024] [Indexed: 03/18/2024] Open
Abstract
Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.
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Affiliation(s)
- Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington DC, 20007, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington DC, 20007, USA
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11
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Alanis-Lobato G, Bartlett TE, Huang Q, Simon CS, McCarthy A, Elder K, Snell P, Christie L, Niakan KK. MICA: a multi-omics method to predict gene regulatory networks in early human embryos. Life Sci Alliance 2024; 7:e202302415. [PMID: 37879938 PMCID: PMC10599980 DOI: 10.26508/lsa.202302415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 10/27/2023] Open
Abstract
Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.
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Affiliation(s)
| | | | - Qiulin Huang
- Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK
- https://ror.org/013meh722 Department of Physiology, Development and Neuroscience, The Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
| | - Claire S Simon
- Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK
| | - Afshan McCarthy
- Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK
| | | | | | | | - Kathy K Niakan
- Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK
- https://ror.org/013meh722 Department of Physiology, Development and Neuroscience, The Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
- https://ror.org/013meh722 Wellcome - Medical Research Council Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Epigenetics Programme, Babraham Institute, Cambridge, UK
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12
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Pechmann S. Single-cell expression predicts neuron-specific protein homeostasis networks. Open Biol 2024; 14:230386. [PMID: 38262604 PMCID: PMC10805596 DOI: 10.1098/rsob.230386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 11/17/2023] [Indexed: 01/25/2024] Open
Abstract
The protein homeostasis network keeps proteins in their correct shapes and avoids unwanted aggregation. In turn, the accumulation of aberrantly misfolded proteins has been directly associated with the onset of ageing-associated neurodegenerative diseases such as Alzheimer's and Parkinson's. However, a detailed and rational understanding of how protein homeostasis is achieved in health, and how it can be targeted for therapeutic intervention in diseases remains missing. Here, large-scale single-cell expression data from the Allen Brain Map are analysed to investigate the transcription regulation of the core protein homeostasis network across the human brain. Remarkably, distinct expression profiles suggest specialized protein homeostasis networks with systematic adaptations in excitatory neurons, inhibitory neurons and non-neuronal cells. Moreover, several chaperones and Ubiquitin ligases are found transcriptionally coregulated with genes important for synapse formation and maintenance, thus linking protein homeostasis to the regulation of neuronal function. Finally, evolutionary analyses highlight the conservation of an elevated interaction density in the chaperone network, suggesting that one of the most exciting aspects of chaperone action may yet be discovered in their collective action at the systems level. More generally, our work highlights the power of computational analyses for breaking down complexity and gaining complementary insights into fundamental biological problems.
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13
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Ishikawa M, Sugino S, Masuda Y, Tarumoto Y, Seto Y, Taniyama N, Wagai F, Yamauchi Y, Kojima Y, Kiryu H, Yusa K, Eiraku M, Mochizuki A. RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations. Commun Biol 2023; 6:1290. [PMID: 38155269 PMCID: PMC10754834 DOI: 10.1038/s42003-023-05594-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 11/15/2023] [Indexed: 12/30/2023] Open
Abstract
Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems.
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Affiliation(s)
- Masato Ishikawa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan.
| | - Seiichi Sugino
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yoshie Masuda
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yusuke Tarumoto
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yusuke Seto
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Nobuko Taniyama
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Fumi Wagai
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yuhei Yamauchi
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Yasuhiro Kojima
- Laboratory of Computational Life Science, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Hisanori Kiryu
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Kosuke Yusa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
| | - Mototsugu Eiraku
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, 606-8507, Japan
| | - Atsushi Mochizuki
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan
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14
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Ripley DM, Garner T, Hook SA, Veríssimo A, Grunow B, Moritz T, Clayton P, Shiels HA, Stevens A. Warming during embryogenesis induces a lasting transcriptomic signature in fishes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:165954. [PMID: 37536606 DOI: 10.1016/j.scitotenv.2023.165954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/24/2023] [Accepted: 07/30/2023] [Indexed: 08/05/2023]
Abstract
Exposure to elevated temperatures during embryogenesis can influence the plasticity of tissues in later life. Despite these long-term changes in plasticity, few differentially expressed genes are ever identified, suggesting that the developmental programming of later life plasticity may occur through the modulation of other aspects of transcriptomic architecture, such as gene network organisation. Here, we use network modelling approaches to demonstrate that warm temperatures during embryonic development (developmental warming) have consistent effects in later life on the organisation of transcriptomic networks across four diverse species of fishes: Scyliorhinus canicula, Danio rerio, Dicentrarchus labrax, and Gasterosteus aculeatus. The transcriptomes of developmentally warmed fishes are characterised by an increased entropy of their pairwise gene interaction networks, implying a less structured, more 'random' set of gene interactions. We also show that, in zebrafish subject to developmental warming, the entropy of an individual gene within a network is associated with that gene's probability of expression change during temperature acclimation in later life. However, this association is absent in animals reared under 'control' conditions. Thus, the thermal environment experienced during embryogenesis can alter transcriptomic organisation in later life, and these changes may influence an individual's responsiveness to future temperature challenges.
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Affiliation(s)
- Daniel M Ripley
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK.
| | - Terence Garner
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Samantha A Hook
- Department of Earth and Environmental Sciences, The University of Manchester, Manchester, UK
| | - Ana Veríssimo
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, 4485-661 Vairão, Portugal; BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, 4485-661 Vairão, Portugal
| | - Bianka Grunow
- Fish Growth Physiology, Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Timo Moritz
- Deutsches Meeresmuseum, Katharinenberg 14-20, 18439 Stralsund, Germany; Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059 Rostock, Germany
| | - Peter Clayton
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Holly A Shiels
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Adam Stevens
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK.
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15
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Merelli I, Beretta S, Cesana D, Gennari A, Benedicenti F, Spinozzi G, Cesini D, Montini E, D’Agostino D, Calabria A. InCliniGene enables high-throughput and comprehensive in vivo clonal tracking toward clinical genomics data integration. Database (Oxford) 2023; 2023:baad069. [PMID: 37935583 PMCID: PMC10630073 DOI: 10.1093/database/baad069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 08/15/2023] [Accepted: 10/04/2023] [Indexed: 11/09/2023]
Abstract
High-throughput clonal tracking in patients under hematopoietic stem cell gene therapy with integrating vector is instrumental in assessing bio-safety and efficacy. Monitoring the fate of millions of transplanted clones and their progeny across differentiation and proliferation over time leverages the identification of the vector integration sites, used as surrogates of clonal identity. Although γ-tracking retroviral insertion sites (γ-TRIS) is the state-of-the-art algorithm for clonal identification, the computational drawbacks in the tracking algorithm, based on a combinatorial all-versus-all strategy, limit its use in clinical studies with several thousands of samples per patient. We developed the first clonal tracking graph database, InCliniGene (https://github.com/calabrialab/InCliniGene), that imports the output files of γ-TRIS and generates the graph of clones (nodes) connected by arches if two nodes share common genomic features as defined by the γ-TRIS rules. Embedding both clonal data and their connections in the graph, InCliniGene can track all clones longitudinally over samples through data queries that fully explore the graph. This approach resulted in being highly accurate and scalable. We validated InCliniGene using an in vitro dataset, specifically designed to mimic clinical cases, and tested the accuracy and precision. InCliniGene allows extensive use of γ-TRIS in large gene therapy clinical applications and naturally realizes the full data integration of molecular and genomics data, clinical and treatment measurements and genomic annotations. Further extensions of InCliniGene with data federation and with application programming interface will support data mining toward precision, personalized and predictive medicine in gene therapy. Database URL: https://github.com/calabrialab/InCliniGene.
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Affiliation(s)
| | - Stefano Beretta
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Daniela Cesana
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Alessandro Gennari
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Fabrizio Benedicenti
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Giulio Spinozzi
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Daniele Cesini
- Centro Nazionale Analisi Fotogrammi (CNAF), Istituto Nazionale di Fisica Nucleare, Viale Carlo Berti Pichat 6/2, Bologna 40127, Italy
| | - Eugenio Montini
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Daniele D’Agostino
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), Università degli Studi di Genova, Viale Causa 13, Genoa 16145, Italy
- Institute of Biomedical Technologies, Italian National Research Council, Via Fratelli Cervi 93, Segrate (MI) 20054, Italy
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Andrea Calabria
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
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16
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Li Z, Liu X, Wang L, Zhao H, Wang S, Yu G, Wu D, Chu J, Han J. Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals RNA N6-methyladenosine modification associated with prognosis and drug resistance in acute myeloid leukemia. Front Immunol 2023; 14:1281687. [PMID: 38022588 PMCID: PMC10644381 DOI: 10.3389/fimmu.2023.1281687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Acute myeloid leukemia (AML) is a type of blood cancer that is identified by the unrestricted growth of immature myeloid cells within the bone marrow. Despite therapeutic advances, AML prognosis remains highly variable, and there is a lack of biomarkers for customizing treatment. RNA N6-methyladenosine (m6A) modification is a reversible and dynamic process that plays a critical role in cancer progression and drug resistance. Methods To investigate the m6A modification patterns in AML and their potential clinical significance, we used the AUCell method to describe the m6A modification activity of cells in AML patients based on 23 m6A modification enzymes and further integrated with bulk RNA-seq data. Results We found that m6A modification was more effective in leukemic cells than in immune cells and induced significant changes in gene expression in leukemic cells rather than immune cells. Furthermore, network analysis revealed a correlation between transcription factor activation and the m6A modification status in leukemia cells, while active m6A-modified immune cells exhibited a higher interaction density in their gene regulatory networks. Hierarchical clustering based on m6A-related genes identified three distinct AML subtypes. The immune dysregulation subtype, characterized by RUNX1 mutation and KMT2A copy number variation, was associated with a worse prognosis and exhibited a specific gene expression pattern with high expression level of IGF2BP3 and FMR1, and low expression level of ELAVL1 and YTHDF2. Notably, patients with the immune dysregulation subtype were sensitive to immunotherapy and chemotherapy. Discussion Collectively, our findings suggest that m6A modification could be a potential therapeutic target for AML, and the identified subtypes could guide personalized therapy.
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Affiliation(s)
- Zhongzheng Li
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Institute of Biomedical Science, Henan Normal University, Xinxiang, China
| | - Xin Liu
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Lan Wang
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Institute of Biomedical Science, Henan Normal University, Xinxiang, China
| | - Huabin Zhao
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Institute of Biomedical Science, Henan Normal University, Xinxiang, China
| | - Shenghui Wang
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Institute of Biomedical Science, Henan Normal University, Xinxiang, China
| | - Guoying Yu
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Institute of Biomedical Science, Henan Normal University, Xinxiang, China
| | - Depei Wu
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Jianhong Chu
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Jingjing Han
- The First Affiliated Hospital of Soochow University, National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
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17
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Li Y, Lin B, Hao D, Du Z, Wang Q, Song Z, Li X, Li K, Wang J, Zhang Q, Wu J, Xi Z, Chen H. Short-term PM 2.5 exposure induces transient lung injury and repair. JOURNAL OF HAZARDOUS MATERIALS 2023; 459:132227. [PMID: 37586238 DOI: 10.1016/j.jhazmat.2023.132227] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/01/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
Exposure to fine atmospheric particulate matter (PM) is known to induce lung inflammation and injury; however, the way in which sophisticated endogenous lung repair and regenerative programs respond to this exposure remains unknown. In this study, we established a whole-body mouse exposure model to mimic real scenarios. Exposure to fine PM (PM with an aerodynamic diameter ≤ 2.5 µm [PM2.5]; mean 1.05 mg/m3) for 1-month elicited inflammatory infiltration and epithelial alterations in the lung, which were resolved 6 months after cessation of exposure. Immune cells that responded to PM2.5 exposure mainly included macrophages and neutrophils. During PM2.5 exposure, alveolar epithelial type 2 cells initiated rapid repair of alveolar epithelial mucosa through proliferation. However, the reparative capacity of airway progenitor cells (club cells) was impaired, which may have been related to the oxidative production of neutrophils or macrophages, as suggested in organoid co-cultures. These data suggested that the pulmonary toxic effects of short-term exposure to fine atmospheric PM at a certain dosage could be overcome through tissue reparative mechanisms.
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Affiliation(s)
- Yu Li
- Department of Basic Medicine, Haihe Hospital, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Lung Regenerative Medicine, Tianjin, China
| | - Bencheng Lin
- Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
| | - De Hao
- Department of Basic Medicine, Haihe Hospital, Tianjin University, Tianjin, China
| | - Zhongchao Du
- Department of Basic Medicine, Haihe Clinical School, Tianjin Medical University, Tianjin, China
| | - Qi Wang
- Department of Basic Medicine, Haihe Hospital, Tianjin University, Tianjin, China
| | - Zhaoyu Song
- Department of Basic Medicine, Haihe Clinical School, Tianjin Medical University, Tianjin, China
| | - Xue Li
- Department of Basic Medicine, Haihe Hospital, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Lung Regenerative Medicine, Tianjin, China
| | - Kuan Li
- Department of Basic Medicine, Haihe Hospital, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Lung Regenerative Medicine, Tianjin, China
| | - Jianhai Wang
- Department of Basic Medicine, Haihe Hospital, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Lung Regenerative Medicine, Tianjin, China
| | - Qiuyang Zhang
- Department of Basic Medicine, Haihe Hospital, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Lung Regenerative Medicine, Tianjin, China
| | - Junping Wu
- Tianjin Institute of Respiratory Diseases, Tianjin, China; Department of Tuberculosis, Haihe Hospital, Tianjin University, Tianjin, China
| | - Zhuge Xi
- Tianjin Institute of Environmental and Operational Medicine, Tianjin, China.
| | - Huaiyong Chen
- Department of Basic Medicine, Haihe Hospital, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Lung Regenerative Medicine, Tianjin, China; College of Pulmonary and Critical Care Medicine, 8th Medical Center, Chinese PLA General Hospital, Beijing, China.
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18
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Şapcı AOB, Lu S, Yan S, Ay F, Tastan O, Keleş S. MuDCoD: multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing. Bioinformatics 2023; 39:btad592. [PMID: 37740957 PMCID: PMC10564618 DOI: 10.1093/bioinformatics/btad592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/24/2023] [Accepted: 09/21/2023] [Indexed: 09/25/2023] Open
Abstract
MOTIVATION With the wide availability of single-cell RNA-seq (scRNA-seq) technology, population-scale scRNA-seq datasets across multiple individuals and time points are emerging. While the initial investigations of these datasets tend to focus on standard analysis of clustering and differential expression, leveraging the power of scRNA-seq data at the personalized dynamic gene co-expression network level has the potential to unlock subject and/or time-specific network-level variation, which is critical for understanding phenotypic differences. Community detection from co-expression networks of multiple time points or conditions has been well-studied; however, none of the existing settings included networks from multiple subjects and multiple time points simultaneously. To address this, we develop Multi-subject Dynamic Community Detection (MuDCoD) for multi-subject community detection in personalized dynamic gene networks from scRNA-seq. MuDCoD builds on the spectral clustering framework and promotes information sharing among the networks of the subjects as well as networks at different time points. It clusters genes in the personalized dynamic gene networks and reveals gene communities that are variable or shared not only across time but also among subjects. RESULTS Evaluation and benchmarking of MuDCoD against existing approaches reveal that MuDCoD effectively leverages apparent shared signals among networks of the subjects at individual time points, and performs robustly when there is no or little information sharing among the networks. Applications to population-scale scRNA-seq datasets of human-induced pluripotent stem cells during dopaminergic neuron differentiation and CD4+ T cell activation indicate that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid in the exploration of subject-specific biological processes that vary across time. AVAILABILITY AND IMPLEMENTATION MuDCoD is publicly available at https://github.com/bo1929/MuDCoD as a Python package. Implementation includes simulation and real-data experiments together with extensive documentation.
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Affiliation(s)
- Ali Osman Berk Şapcı
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, United States
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
| | - Shan Lu
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Shuchen Yan
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Ferhat Ay
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, United States
- Centers for Autoimmunity, Inflammation and Cancer Immunotherapy, La Jolla Institute for Immunology, La Jolla, CA 92037, United States
| | - Oznur Tastan
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
| | - Sündüz Keleş
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, United States
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19
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Wang L, Li Z, Wan R, Pan X, Li B, Zhao H, Yang J, Zhao W, Wang S, Wang Q, Yan P, Ma C, Yuan H, Zhao M, Rosas I, Ding C, Sun B, Yu G. Single-Cell RNA Sequencing Provides New Insights into Therapeutic Roles of Thyroid Hormone in Idiopathic Pulmonary Fibrosis. Am J Respir Cell Mol Biol 2023; 69:456-469. [PMID: 37402274 PMCID: PMC10557923 DOI: 10.1165/rcmb.2023-0080oc] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/29/2023] [Indexed: 07/06/2023] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive fatal interstitial lung disease without an effective cure. Herein, we explore the role of 3,5,3'-triiodothyronine (T3) administration on lung alveolar regeneration and fibrosis at the single-cell level. T3 supplementation significantly altered the gene expression in fibrotic lung tissues. Immune cells were rapidly recruited into the lung after the injury; there were much more M2 macrophages than M1 macrophages in the lungs of bleomycin-treated mice; and M1 macrophages increased slightly, whereas M2 macrophages were significantly reduced after T3 treatment. T3 enhanced the resolution of pulmonary fibrosis by promoting the differentiation of Krt8+ transitional alveolar type II epithelial cells into alveolar type I epithelial cells and inhibiting fibroblast activation and extracellular matrix production potentially by regulation of Nr2f2. In addition, T3 regulated the crosstalk of macrophages with fibroblasts, and the Pros1-Axl signaling axis significantly facilitated the attenuation of fibrosis. The findings demonstrate that administration of a thyroid hormone promotes alveolar regeneration and resolves fibrosis mainly by regulation of the cellular state and cell-cell communication of alveolar epithelial cells, macrophages, and fibroblasts in mouse lungs in comprehensive ways.
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Affiliation(s)
- Lan Wang
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Zhongzheng Li
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Ruyan Wan
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Xin Pan
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Bin Li
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Huabin Zhao
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Juntang Yang
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Weiming Zhao
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Shenghui Wang
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Qiwen Wang
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Peishuo Yan
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Chi Ma
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
- School of Life Sciences, Fudan University, Shanghai, China; and
| | - Hongmei Yuan
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Mengxia Zhao
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Ivan Rosas
- Division of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, Texas
| | - Chen Ding
- School of Life Sciences, Fudan University, Shanghai, China; and
| | - Baofa Sun
- College of Life Science, Nankai University, Tianjin, China
| | - Guoying Yu
- State Key Laboratory of Cell Differentiation and Regulation, and
- Henan International Joint Laboratory of Pulmonary Fibrosis, College of Life Science, Henan Normal University, Xinxiang, Henan, China
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20
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Beppu AK, Zhao J, Yao C, Carraro G, Israely E, Coelho AL, Drake K, Hogaboam CM, Parks WC, Kolls JK, Stripp BR. Epithelial plasticity and innate immune activation promote lung tissue remodeling following respiratory viral infection. Nat Commun 2023; 14:5814. [PMID: 37726288 PMCID: PMC10509177 DOI: 10.1038/s41467-023-41387-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 09/02/2023] [Indexed: 09/21/2023] Open
Abstract
Epithelial plasticity has been suggested in lungs of mice following genetic depletion of stem cells but is of unknown physiological relevance. Viral infection and chronic lung disease share similar pathological features of stem cell loss in alveoli, basal cell (BC) hyperplasia in small airways, and innate immune activation, that contribute to epithelial remodeling and loss of lung function. We show that a subset of distal airway secretory cells, intralobar serous (IS) cells, are activated to assume BC fates following influenza virus infection. Injury-induced hyperplastic BC (hBC) differ from pre-existing BC by high expression of IL-22Ra1 and undergo IL-22-dependent expansion for colonization of injured alveoli. Resolution of virus-elicited inflammation results in BC to IS re-differentiation in repopulated alveoli, and increased local expression of protective antimicrobial factors, but fails to restore normal alveolar epithelium responsible for gas exchange.
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Affiliation(s)
- Andrew K Beppu
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Department of Medicine, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Juanjuan Zhao
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Department of Medicine, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Changfu Yao
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Department of Medicine, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Gianni Carraro
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Department of Medicine, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Edo Israely
- Department of Medicine, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Anna Lucia Coelho
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Katherine Drake
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Department of Medicine, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Cory M Hogaboam
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - William C Parks
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Jay K Kolls
- Tulane Center for Translational Research in Infection and Inflammation, School of Medicine, New Orleans, LA, 70112, USA
| | - Barry R Stripp
- Department of Medicine, Women's Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
- Department of Medicine, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
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21
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Xue L, Wu Y, Lin Y. Dissecting and improving gene regulatory network inference using single-cell transcriptome data. Genome Res 2023; 33:1609-1621. [PMID: 37580132 PMCID: PMC10620053 DOI: 10.1101/gr.277488.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 08/07/2023] [Indexed: 08/16/2023]
Abstract
Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. Although a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell data sets, we showed that target genes' mature mRNA levels often fail to accurately report upstream regulatory activities because of gene-level and network-level factors, which can be improved by using pre-mRNA levels. We tested this finding on public single-cell RNA-seq data sets using intronic reads as proxies of pre-mRNA levels and can indeed achieve a higher inference accuracy compared to using exonic reads (corresponding to mature mRNAs). Using experimental data sets, we further validated findings from the simulated data sets and identified factors such as transcription factor activity dynamics influencing the accuracy of pre-mRNA-based inference. This work delineates the fundamental limitations of gene regulatory inference and helps improve GRN inference using single-cell RNA-seq data.
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Affiliation(s)
- Lingfeng Xue
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 100871
| | - Yan Wu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 100871
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China, 100871
| | - Yihan Lin
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 100871;
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China, 100871
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 100871
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22
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Tan F, Xuan Y, Long L, Yu Y, Zhang C, Liang P, Wang Y, Chen M, Wen J, Chen G. Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications. BMC Genomics 2023; 24:514. [PMID: 37658288 PMCID: PMC10474653 DOI: 10.1186/s12864-023-09615-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/22/2023] [Indexed: 09/03/2023] Open
Abstract
BACKGROUND The cellular and molecular dynamics of human prepuce are crucial for understanding its biological and physiological functions, as well as the prevention of related genital diseases. However, the cellular compositions and heterogeneity of human prepuce at single-cell resolution are still largely unknown. Here we systematically dissected the prepuce of children and adults based on the single-cell RNA-seq data of 90,770 qualified cells. RESULTS We identified 15 prepuce cell subtypes, including fibroblast, smooth muscle cells, T/natural killer cells, macrophages, vascular endothelial cells, and dendritic cells. The proportions of these cell types varied among different individuals as well as between children and adults. Moreover, we detected cell-type-specific gene regulatory networks (GRNs), which could contribute to the unique functions of related cell types. The GRNs were also highly dynamic between the prepuce cells of children and adults. Our cell-cell communication network analysis among different cell types revealed a set of child-specific (e.g., CD96, EPO, IFN-1, and WNT signaling pathways) and adult-specific (e.g., BMP10, NEGR, ncWNT, and NPR1 signaling pathways) signaling pathways. The variations of GRNs and cellular communications could be closely associated with prepuce development in children and prepuce maintenance in adults. CONCLUSIONS Collectively, we systematically analyzed the cellular variations and molecular changes of the human prepuce at single-cell resolution. Our results gained insights into the heterogeneity of prepuce cells and shed light on the underlying molecular mechanisms of prepuce development and maintenance.
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Affiliation(s)
- Fei Tan
- School of Medicine, Shanghai Skin Disease Hospital, Tongji University, Shanghai, 200443, China.
- Shanghai Skin Disease Clinical College, The Fifth Clinical Medical College, Anhui Medical University, Shanghai Skin Disease Hospital, Shanghai, 200443, China.
| | - Yuan Xuan
- Shanghai Skin Disease Clinical College, The Fifth Clinical Medical College, Anhui Medical University, Shanghai Skin Disease Hospital, Shanghai, 200443, China
| | - Lan Long
- Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, 518172, China
| | - Yang Yu
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China
| | - Chunhua Zhang
- Department of Dermatology, Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, 201999, China
| | - Pengchen Liang
- School of Microelectronics, Shanghai University, Shanghai, 201800, China
| | - Yaoqun Wang
- Shanghai Skin Disease Clinical College, The Fifth Clinical Medical College, Anhui Medical University, Shanghai Skin Disease Hospital, Shanghai, 200443, China
| | - Meiyu Chen
- Shanghai Skin Disease Clinical College, The Fifth Clinical Medical College, Anhui Medical University, Shanghai Skin Disease Hospital, Shanghai, 200443, China
| | - Jiling Wen
- Department of Urology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China.
| | - Geng Chen
- School of Medicine, Shanghai Skin Disease Hospital, Tongji University, Shanghai, 200443, China.
- Center for Bioinformatics and Computational Biology, School of Life Sciences, East China Normal University, Shanghai, 200241, China.
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23
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Karri K, Waxman DJ. TCDD dysregulation of lncRNA expression, liver zonation and intercellular communication across the liver lobule. Toxicol Appl Pharmacol 2023; 471:116550. [PMID: 37172768 PMCID: PMC10330769 DOI: 10.1016/j.taap.2023.116550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/21/2023] [Accepted: 05/09/2023] [Indexed: 05/15/2023]
Abstract
The persistent environmental aryl hydrocarbon receptor agonist and hepatotoxin TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin) induces hepatic lipid accumulation (steatosis), inflammation (steatohepatitis) and fibrosis. Thousands of liver-expressed, nuclear-localized lncRNAs with regulatory potential have been identified; however, their roles in TCDD-induced hepatoxicity and liver disease are unknown. We analyzed single nucleus (sn)RNA-seq data from control and subchronic (4 wk) TCDD-exposed mouse liver to determine liver cell-type specificity, zonation and differential expression profiles for thousands of lncRNAs. TCDD dysregulated >4000 of these lncRNAs in one or more liver cell types, including 684 lncRNAs specifically dysregulated in liver non-parenchymal cells. Trajectory inference analysis revealed major disruption by TCDD of hepatocyte zonation, affecting >800 genes, including 121 lncRNAs, with strong enrichment for lipid metabolism genes. TCDD also dysregulated expression of >200 transcription factors, including 19 Nuclear Receptors, most notably in hepatocytes and Kupffer cells. TCDD-induced changes in cell-cell communication patterns included marked decreases in EGF signaling from hepatocytes to non-parenchymal cells and increases in extracellular matrix-receptor interactions central to liver fibrosis. Gene regulatory networks constructed from the snRNA-seq data identified TCDD-exposed liver network-essential lncRNA regulators linked to functions such as fatty acid metabolic process, peroxisome and xenobiotic metabolism. Networks were validated by the striking enrichments that predicted regulatory lncRNAs showed for specific biological pathways. These findings highlight the power of snRNA-seq to discover functional roles for many xenobiotic-responsive lncRNAs in both hepatocytes and liver non-parenchymal cells and to elucidate novel aspects of foreign chemical-induced hepatotoxicity and liver disease, including dysregulation of intercellular communication within the liver lobule.
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Affiliation(s)
- Kritika Karri
- Department of Biology and Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - David J Waxman
- Department of Biology and Bioinformatics Program, Boston University, Boston, MA 02215, USA.
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24
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Androvic P, Schifferer M, Perez Anderson K, Cantuti-Castelvetri L, Jiang H, Ji H, Liu L, Gouna G, Berghoff SA, Besson-Girard S, Knoferle J, Simons M, Gokce O. Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury. Nat Commun 2023; 14:4115. [PMID: 37433806 DOI: 10.1038/s41467-023-39447-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 06/14/2023] [Indexed: 07/13/2023] Open
Abstract
Understanding the complexity of cellular function within a tissue necessitates the combination of multiple phenotypic readouts. Here, we developed a method that links spatially-resolved gene expression of single cells with their ultrastructural morphology by integrating multiplexed error-robust fluorescence in situ hybridization (MERFISH) and large area volume electron microscopy (EM) on adjacent tissue sections. Using this method, we characterized in situ ultrastructural and transcriptional responses of glial cells and infiltrating T-cells after demyelinating brain injury in male mice. We identified a population of lipid-loaded "foamy" microglia located in the center of remyelinating lesion, as well as rare interferon-responsive microglia, oligodendrocytes, and astrocytes that co-localized with T-cells. We validated our findings using immunocytochemistry and lipid staining-coupled single-cell RNA sequencing. Finally, by integrating these datasets, we detected correlations between full-transcriptome gene expression and ultrastructural features of microglia. Our results offer an integrative view of the spatial, ultrastructural, and transcriptional reorganization of single cells after demyelinating brain injury.
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Affiliation(s)
- Peter Androvic
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Martina Schifferer
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Katrin Perez Anderson
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Ludovico Cantuti-Castelvetri
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Hanyi Jiang
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Hao Ji
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Lu Liu
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Garyfallia Gouna
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Stefan A Berghoff
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Simon Besson-Girard
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Johanna Knoferle
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Mikael Simons
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Ozgun Gokce
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany.
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany.
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25
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Karri K, Waxman DJ. Dysregulation of murine long noncoding single-cell transcriptome in nonalcoholic steatohepatitis and liver fibrosis. RNA (NEW YORK, N.Y.) 2023; 29:977-1006. [PMID: 37015806 PMCID: PMC10275269 DOI: 10.1261/rna.079580.123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 03/09/2023] [Indexed: 06/18/2023]
Abstract
LncRNAs comprise a heterogeneous class of RNA-encoding genes typified by low expression, nuclear enrichment, high tissue-specificity, and functional diversity, but the vast majority remain uncharacterized. Here, we assembled the mouse liver noncoding transcriptome from >2000 bulk RNA-seq samples and discovered 48,261 liver-expressed lncRNAs, a majority novel. Using these lncRNAs as a single-cell transcriptomic reference set, we elucidated lncRNA dysregulation in mouse models of high fat diet-induced nonalcoholic steatohepatitis and carbon tetrachloride-induced liver fibrosis. Trajectory inference analysis revealed lncRNA zonation patterns across the liver lobule in each major liver cell population. Perturbations in lncRNA expression and zonation were common in several disease-associated liver cell types, including nonalcoholic steatohepatitis-associated macrophages, a hallmark of fatty liver disease progression, and collagen-producing myofibroblasts, a central feature of liver fibrosis. Single-cell-based gene regulatory network analysis using bigSCale2 linked individual lncRNAs to specific biological pathways, and network-essential regulatory lncRNAs with disease-associated functions were identified by their high network centrality metrics. For a subset of these lncRNAs, promoter sequences of the network-defined lncRNA target genes were significantly enriched for lncRNA triplex formation, providing independent mechanistic support for the lncRNA-target gene linkages predicted by the gene regulatory networks. These findings elucidate liver lncRNA cell-type specificities, spatial zonation patterns, associated regulatory networks, and temporal patterns of dysregulation during hepatic disease progression. A subset of the liver disease-associated regulatory lncRNAs identified have human orthologs and are promising candidates for biomarkers and therapeutic targets.
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Affiliation(s)
- Kritika Karri
- Department of Biology, Boston University, Boston, Massachusetts 02215, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA
| | - David J Waxman
- Department of Biology, Boston University, Boston, Massachusetts 02215, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA
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26
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Cho H, Liu C, Preisser JS, Wu D. A bivariate zero-inflated negative binomial model and its applications to biomedical settings. Stat Methods Med Res 2023; 32:1300-1317. [PMID: 37167422 PMCID: PMC10500952 DOI: 10.1177/09622802231172028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The zero-inflated negative binomial distribution has been widely used for count data analyses in various biomedical settings due to its capacity of modeling excess zeros and overdispersion. When there are correlated count variables, a bivariate model is essential for understanding their full distributional features. Examples include measuring correlation of two genes in sparse single-cell RNA sequencing data and modeling dental caries count indices on two different tooth surface types. For these purposes, we develop a richly parametrized bivariate zero-inflated negative binomial model that has a simple latent variable framework and eight free parameters with intuitive interpretations. In the scRNA-seq data example, the correlation is estimated after adjusting for the effects of dropout events represented by excess zeros. In the dental caries data, we analyze how the treatment with Xylitol lozenges affects the marginal mean and other patterns of response manifested in the two dental caries traits. An R package "bzinb" is available on Comprehensive R Archive Network.
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Affiliation(s)
- Hunyong Cho
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
| | - Chuwen Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
| | - John S Preisser
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
| | - Di Wu
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, NC, USA
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27
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Sun Y, Shim WJ, Shen S, Sinniah E, Pham D, Su Z, Mizikovsky D, White MD, Ho JK, Nguyen Q, Bodén M, Palpant N. Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity. Nucleic Acids Res 2023; 51:e62. [PMID: 37125641 PMCID: PMC10287941 DOI: 10.1093/nar/gkad307] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 04/28/2023] [Indexed: 05/02/2023] Open
Abstract
Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.
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Affiliation(s)
- Yuliangzi Sun
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Woo Jun Shim
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Sophie Shen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Enakshi Sinniah
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Duy Pham
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Dalia Mizikovsky
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Melanie D White
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Mikael Bodén
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
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28
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Brunson T, Sanati N, Matthews L, Haw R, Beavers D, Shorser S, Sevilla C, Viteri G, Conley P, Rothfels K, Hermjakob H, Stein L, D’Eustachio P, Wu G. Illuminating Dark Proteins using Reactome Pathways. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543335. [PMID: 37333417 PMCID: PMC10274615 DOI: 10.1101/2023.06.05.543335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Limited knowledge about a substantial portion of protein coding genes, known as "dark" proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach. Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at https://idg.reactome.org, a web application featuring tissue-specific protein and gene expression overlay, as well as drug interactions. Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.
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Affiliation(s)
| | - Nasim Sanati
- Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Deidre Beavers
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Solomon Shorser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Guilherme Viteri
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Patrick Conley
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A1, Canada
| | | | - Guanming Wu
- Oregon Health & Science University, Portland, OR 97239, USA
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29
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Lu S, Keleş S. Debiased personalized gene coexpression networks for population-scale scRNA-seq data. Genome Res 2023; 33:932-947. [PMID: 37295843 PMCID: PMC10519377 DOI: 10.1101/gr.277363.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 06/07/2023] [Indexed: 06/12/2023]
Abstract
Population-scale single-cell RNA-seq (scRNA-seq) data sets create unique opportunities for quantifying expression variation across individuals at the gene coexpression network level. Estimation of coexpression networks is well established for bulk RNA-seq; however, single-cell measurements pose novel challenges owing to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased toward zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq data sets and accurately quantify network-level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the data sets. Compared with alternatives, Dozer results in fewer false-positive edges in the coexpression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the data sets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Coexpression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer's disease and controls uniquely reveals coexpression modules of innate immune response with distinct coexpression levels between the diagnoses. Dozer represents an important advance in estimating personalized coexpression networks from scRNA-seq data.
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Affiliation(s)
- Shan Lu
- Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Sündüz Keleş
- Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA;
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53706, USA
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30
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Tani S, Okada H, Onodera S, Chijimatsu R, Seki M, Suzuki Y, Xin X, Rowe DW, Saito T, Tanaka S, Chung UI, Ohba S, Hojo H. Stem cell-based modeling and single-cell multiomics reveal gene-regulatory mechanisms underlying human skeletal development. Cell Rep 2023; 42:112276. [PMID: 36965484 DOI: 10.1016/j.celrep.2023.112276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 01/19/2023] [Accepted: 03/02/2023] [Indexed: 03/27/2023] Open
Abstract
Although the skeleton is essential for locomotion, endocrine functions, and hematopoiesis, the molecular mechanisms of human skeletal development remain to be elucidated. Here, we introduce an integrative method to model human skeletal development by combining in vitro sclerotome induction from human pluripotent stem cells and in vivo endochondral bone formation by implanting the sclerotome beneath the renal capsules of immunodeficient mice. Histological and scRNA-seq analyses reveal that the induced bones recapitulate endochondral ossification and are composed of human skeletal cells and mouse circulatory cells. The skeletal cell types and their trajectories are similar to those of human embryos. Single-cell multiome analysis reveals dynamic changes in chromatin accessibility associated with multiple transcription factors constituting cell-type-specific gene-regulatory networks (GRNs). We further identify ZEB2, which may regulate the GRNs in human osteogenesis. Collectively, these results identify components of GRNs in human skeletal development and provide a valuable model for its investigation.
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Affiliation(s)
- Shoichiro Tani
- Laboratory of Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan; Sensory and Motor System Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan.
| | - Hiroyuki Okada
- Laboratory of Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan; Sensory and Motor System Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Shoko Onodera
- Department of Biochemistry, Tokyo Dental College, Tokyo 101-0061, Japan
| | - Ryota Chijimatsu
- Sensory and Motor System Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan; Center for Comprehensive Genomic Medicine, Okayama University Hospital, Okayama 700-8558, Japan
| | - Masahide Seki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan
| | - Xiaonan Xin
- Department of Reconstructive Sciences, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - David W Rowe
- Department of Reconstructive Sciences, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Taku Saito
- Sensory and Motor System Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Sakae Tanaka
- Sensory and Motor System Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Ung-Il Chung
- Laboratory of Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan; Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8655, Japan
| | - Shinsuke Ohba
- Laboratory of Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan; Department of Cell Biology, Institute of Biomedical Sciences, Nagasaki University, Nagasaki 852-8588, Japan; Department of Oral Anatomy and Developmental Biology, Graduate School of Dentistry, Osaka University, Osaka 565-0871, Japan.
| | - Hironori Hojo
- Laboratory of Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan; Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8655, Japan.
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31
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Advances in Mass Spectrometry-Based Single Cell Analysis. BIOLOGY 2023; 12:biology12030395. [PMID: 36979087 PMCID: PMC10045136 DOI: 10.3390/biology12030395] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023]
Abstract
Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.
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32
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Hoeft K, Schaefer GJL, Kim H, Schumacher D, Bleckwehl T, Long Q, Klinkhammer BM, Peisker F, Koch L, Nagai J, Halder M, Ziegler S, Liehn E, Kuppe C, Kranz J, Menzel S, Costa I, Wahida A, Boor P, Schneider RK, Hayat S, Kramann R. Platelet-instructed SPP1 + macrophages drive myofibroblast activation in fibrosis in a CXCL4-dependent manner. Cell Rep 2023; 42:112131. [PMID: 36807143 PMCID: PMC9992450 DOI: 10.1016/j.celrep.2023.112131] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/11/2022] [Accepted: 01/31/2023] [Indexed: 02/19/2023] Open
Abstract
Fibrosis represents the common end stage of chronic organ injury independent of the initial insult, destroying tissue architecture and driving organ failure. Here we discover a population of profibrotic macrophages marked by expression of Spp1, Fn1, and Arg1 (termed Spp1 macrophages), which expands after organ injury. Using an unbiased approach, we identify the chemokine (C-X-C motif) ligand 4 (CXCL4) to be among the top upregulated genes during profibrotic Spp1 macrophage differentiation. In vitro and in vivo studies show that loss of Cxcl4 abrogates profibrotic Spp1 macrophage differentiation and ameliorates fibrosis after both heart and kidney injury. Moreover, we find that platelets, the most abundant source of CXCL4 in vivo, drive profibrotic Spp1 macrophage differentiation. Single nuclear RNA sequencing with ligand-receptor interaction analysis reveals that macrophages orchestrate fibroblast activation via Spp1, Fn1, and Sema3 crosstalk. Finally, we confirm that Spp1 macrophages expand in both human chronic kidney disease and heart failure.
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Affiliation(s)
- Konrad Hoeft
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Gideon J L Schaefer
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Hyojin Kim
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - David Schumacher
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany; Department of Anesthesiology, RWTH Aachen University, Aachen, Germany
| | - Tore Bleckwehl
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Qingqing Long
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | | | - Fabian Peisker
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Lars Koch
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - James Nagai
- Institute for Computational Genomics, RWTH Aachen University Hospital, Aachen, Germany; Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Maurice Halder
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Susanne Ziegler
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Elisa Liehn
- Institute for Molecular Medicine, University of South Denmark, Odense, Denmark
| | - Christoph Kuppe
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Jennifer Kranz
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany; Department of Urology, RWTH Aachen University, Aachen, Germany; Department of Urology and Kidney Transplantation, Martin-Luther-University, Halle (Saale), Germany
| | - Sylvia Menzel
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Ivan Costa
- Institute for Computational Genomics, RWTH Aachen University Hospital, Aachen, Germany; Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Adam Wahida
- Institute of Metabolism and Cell Death, Helmholtz Zentrum München, Neuherberg, Germany; Division of Gynecological Oncology, National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Peter Boor
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany; Department of Pathology, RWTH Aachen University, Aachen, Germany
| | - Rebekka K Schneider
- Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands; Department of Cell Biology, Institute for Biomedical Technologies, RWTH Aachen University, Aachen, Germany
| | - Sikander Hayat
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Rafael Kramann
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany; Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, the Netherlands.
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Ma A, Wang X, Li J, Wang C, Xiao T, Liu Y, Cheng H, Wang J, Li Y, Chang Y, Li J, Wang D, Jiang Y, Su L, Xin G, Gu S, Li Z, Liu B, Xu D, Ma Q. Single-cell biological network inference using a heterogeneous graph transformer. Nat Commun 2023; 14:964. [PMID: 36810839 PMCID: PMC9944243 DOI: 10.1038/s41467-023-36559-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 02/06/2023] [Indexed: 02/23/2023] Open
Abstract
Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.
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Affiliation(s)
- Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Xiaoying Wang
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Jingxian Li
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Cankun Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Tong Xiao
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Yuntao Liu
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Hao Cheng
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Juexin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Yang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Jinpu Li
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Duolin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Yuexu Jiang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Li Su
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Gang Xin
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Shaopeng Gu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Zihai Li
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, China.
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA.
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
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34
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Kamimoto K, Stringa B, Hoffmann CM, Jindal K, Solnica-Krezel L, Morris SA. Dissecting cell identity via network inference and in silico gene perturbation. Nature 2023; 614:742-751. [PMID: 36755098 PMCID: PMC9946838 DOI: 10.1038/s41586-022-05688-9] [Citation(s) in RCA: 114] [Impact Index Per Article: 114.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 12/28/2022] [Indexed: 02/10/2023]
Abstract
Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.
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Affiliation(s)
- Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Blerta Stringa
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Christy M Hoffmann
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Kunal Jindal
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Lilianna Solnica-Krezel
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA.
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA.
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35
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Qiu J, Ma L, Wang T, Chen J, Wang D, Guo Y, Li Y, Ma X, Chen G, Luo Y, Cheng X, Xu L. Bioinformatic analysis of single-cell RNA sequencing dataset dissects cellular heterogeneity of triple-negative breast cancer in transcriptional profile, splicing event and crosstalk network. CLINICAL & TRANSLATIONAL ONCOLOGY : OFFICIAL PUBLICATION OF THE FEDERATION OF SPANISH ONCOLOGY SOCIETIES AND OF THE NATIONAL CANCER INSTITUTE OF MEXICO 2023; 25:1856-1868. [PMID: 36692641 DOI: 10.1007/s12094-023-03083-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/09/2023] [Indexed: 01/25/2023]
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high tumoral heterogeneity, while the detailed regulatory network is not well known. METHODS Via single-cell RNA-sequencing (scRNA-seq) data analysis, we comprehensively investigated the transcriptional profile of different subtypes of TNBC epithelial cells with gene regulatory network (GRN) and alternative splicing (AS) event analysis, as well as the crosstalk between epithelial and non-epithelial cells. RESULTS Of note, we found that luminal progenitor subtype exhibited the most complex GRN and splicing events. Besides, hnRNPs negatively regulates AS events in luminal progenitor subtype. In addition, we explored the cellular crosstalk among endothelial cells, stromal cells and immune cells in TNBC and discovered that NOTCH4 was a key receptor and prognostic marker in endothelial cells, which provide potential biomarker and target for TNBC intervention. CONCLUSIONS In summary, our study elaborates on the cellular heterogeneity of TNBC, revealing that NOTCH4 in endothelial cells was critical for TNBC intervention. This in-depth understanding of epithelial cell and non-epithelial cell network would provide theoretical basis for the development of new drugs targeting this sophisticated network in TNBC.
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Affiliation(s)
- Jin Qiu
- Department of Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Lu Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Tingting Wang
- Department of Anaesthesia, Changning Maternity and Infant Health Hospital, East China Normal University, Shanghai, 200050, China
| | - Juntong Chen
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Dongmei Wang
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yuhan Guo
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yin Li
- Department of Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
- Department of Anaesthesia, Changning Maternity and Infant Health Hospital, East China Normal University, Shanghai, 200050, China
- Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China
| | - Geng Chen
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Ying Luo
- Prenatal Diagnosis Center, Department of Clinical Laboratory, Changning Maternity and Infant Health Hospital, East China Normal University, Shanghai, 200050, China.
| | - Xinghua Cheng
- Department of Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China.
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Department of Anaesthesia, Changning Maternity and Infant Health Hospital, East China Normal University, Shanghai, 200050, China.
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36
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Lineage-specific regulatory changes in hypertrophic cardiomyopathy unraveled by single-nucleus RNA-seq and spatial transcriptomics. Cell Discov 2023; 9:6. [PMID: 36646705 PMCID: PMC9842679 DOI: 10.1038/s41421-022-00490-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 10/29/2022] [Indexed: 01/18/2023] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is the most common cardiac genetic disorder characterized by cardiomyocyte hypertrophy and cardiac fibrosis. Pathological cardiac remodeling in the myocardium of HCM patients may progress to heart failure. An in-depth elucidation of the lineage-specific changes in pathological cardiac remodeling of HCM is pivotal for the development of therapies to mitigate the progression. Here, we performed single-nucleus RNA-seq of the cardiac tissues from HCM patients or healthy donors and conducted spatial transcriptomic assays on tissue sections from patients. Unbiased clustering of 55,122 nuclei from HCM and healthy conditions revealed 9 cell lineages and 28 clusters. Lineage-specific changes in gene expression, subpopulation composition, and intercellular communication in HCM were discovered through comparative analyses. According to the results of pseudotime ordering, differential expression analysis, and differential regulatory network analysis, potential key genes during the transition towards a failing state of cardiomyocytes such as FGF12, IL31RA, and CREB5 were identified. Transcriptomic dynamics underlying cardiac fibroblast activation were also uncovered, and potential key genes involved in cardiac fibrosis were obtained such as AEBP1, RUNX1, MEOX1, LEF1, and NRXN3. Using the spatial transcriptomic data, spatial activity patterns of the candidate genes, pathways, and subpopulations were confirmed on patient tissue sections. Moreover, we showed experimental evidence that in vitro knockdown of AEBP1 could promote the activation of human cardiac fibroblasts, and overexpression of AEBP1 could attenuate the TGFβ-induced activation. Our study provided a comprehensive analysis of the lineage-specific regulatory changes in HCM, which laid the foundation for targeted drug development in HCM.
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Karri K, Waxman DJ. TCDD dysregulation of lncRNA expression, liver zonation and intercellular communication across the liver lobule. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.07.523119. [PMID: 36711947 PMCID: PMC9881922 DOI: 10.1101/2023.01.07.523119] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The persistent environmental aryl hydrocarbon receptor agonist and hepatotoxin TCDD (2,3,7,8-tetrachlorodibenzo- p -dioxin) induces hepatic lipid accumulation (steatosis), inflammation (steatohepatitis) and fibrosis. Thousands of liver-expressed, nuclear-localized lncRNAs with regulatory potential have been identified; however, their roles in TCDD-induced hepatoxicity and liver disease are unknown. We analyzed single nucleus (sn)RNA-seq data from control and chronic TCDD-exposed mouse liver to determine liver cell-type specificity, zonation and differential expression profiles for thousands of IncRNAs. TCDD dysregulated >4,000 of these lncRNAs in one or more liver cell types, including 684 lncRNAs specifically dysregulated in liver non-parenchymal cells. Trajectory inference analysis revealed major disruption by TCDD of hepatocyte zonation, affecting >800 genes, including 121 IncRNAs, with strong enrichment for lipid metabolism genes. TCDD also dysregulated expression of >200 transcription factors, including 19 Nuclear Receptors, most notably in hepatocytes and Kupffer cells. TCDD-induced changes in cellâ€"cell communication patterns included marked decreases in EGF signaling from hepatocytes to non-parenchymal cells and increases in extracellular matrix-receptor interactions central to liver fibrosis. Gene regulatory networks constructed from the snRNA-seq data identified TCDD-exposed liver network-essential lncRNA regulators linked to functions such as fatty acid metabolic process, peroxisome and xenobiotic metabolic. Networks were validated by the striking enrichments that predicted regulatory IncRNAs showed for specific biological pathways. These findings highlight the power of snRNA-seq to discover functional roles for many xenobiotic-responsive lncRNAs in both hepatocytes and liver non-parenchymal cells and to elucidate novel aspects of foreign chemical-induced hepatotoxicity and liver disease, including dysregulation of intercellular communication within the liver lobule.
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38
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Cha J, Lavi M, Kim J, Shomron N, Lee I. Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis. Comput Struct Biotechnol J 2023; 21:2296-2304. [PMID: 37035549 PMCID: PMC10073994 DOI: 10.1016/j.csbj.2023.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/21/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.
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Affiliation(s)
- Junha Cha
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Michael Lavi
- Faculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - Junhan Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Noam Shomron
- Faculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
- Corresponding author.
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
- POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
- Corresponding author at: Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.
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39
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Becker J, Sun B, Alammari F, Haerty W, Vance KW, Szele FG. What has single-cell transcriptomics taught us about long non-coding RNAs in the ventricular-subventricular zone? Stem Cell Reports 2022; 18:354-376. [PMID: 36525965 PMCID: PMC9860170 DOI: 10.1016/j.stemcr.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 12/16/2022] Open
Abstract
Long non-coding RNA (lncRNA) function is mediated by the process of transcription or through transcript-dependent associations with proteins or nucleic acids to control gene regulatory networks. Many lncRNAs are transcribed in the ventricular-subventricular zone (V-SVZ), a postnatal neural stem cell niche. lncRNAs in the V-SVZ are implicated in neurodevelopmental disorders, cancer, and brain disease, but their functions are poorly understood. V-SVZ neurogenesis capacity declines with age due to stem cell depletion and resistance to neural stem cell activation. Here we analyzed V-SVZ transcriptomics by pooling current single-cell RNA-seq data. They showed consistent lncRNA expression during stem cell activation, lineage progression, and aging. In conjunction with epigenetic and genetic data, we predicted V-SVZ lncRNAs that regulate stem cell activation and differentiation. Some of the lncRNAs validate known epigenetic mechanisms, but most remain uninvestigated. Our analysis points to several lncRNAs that likely participate in key aspects of V-SVZ stem cell activation and neurogenesis in health and disease.
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Affiliation(s)
- Jemima Becker
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Bin Sun
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Farah Alammari
- Department of Blood and Cancer Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia,Clinical Laboratory Sciences Department, College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | | | - Keith W. Vance
- Department of Life Sciences, University of Bath, Bath, UK
| | - Francis George Szele
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
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40
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Ye F, Zhang G, E. W, Chen H, Yu C, Yang L, Fu Y, Li J, Fu S, Sun Z, Fei L, Guo Q, Wang J, Xiao Y, Wang X, Zhang P, Ma L, Ge D, Xu S, Caballero-Pérez J, Cruz-Ramírez A, Zhou Y, Chen M, Fei JF, Han X, Guo G. Construction of the axolotl cell landscape using combinatorial hybridization sequencing at single-cell resolution. Nat Commun 2022; 13:4228. [PMID: 35869072 PMCID: PMC9307617 DOI: 10.1038/s41467-022-31879-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 07/08/2022] [Indexed: 01/01/2023] Open
Abstract
The Mexican axolotl (Ambystoma mexicanum) is a well-established tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, a process that triggers many dramatic changes in diverse organs, accompanied by gradually decline of their regeneration capacity and lifespan. However, the molecular regulation and cellular changes in neotenic and metamorphosed axolotls are still poorly investigated. Here, we develop a single-cell sequencing method based on combinatorial hybridization to generate a tissue-based transcriptomic landscape of the neotenic and metamorphosed axolotls. We perform gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell landscape. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls reveal the heterogeneity of non-immune parenchymal cells in different tissues and established their regulatory network. Furthermore, we describe dynamic gene expression patterns during limb development in neotenic axolotls. This system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls, serves as a resource to explore the molecular identity of the axolotl and facilitates better understanding of metamorphosis. The Mexican axolotl is a well-established tetrapod model for regeneration and development. Here the authors report a scRNA-seq method to profile neotenic, metamorphic and limb development stages, highlighting unique perturbation patterns of cell type-related gene expression throughout metamorphosis.
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Cervantes-Pérez SA, Thibivillliers S, Tennant S, Libault M. Review: Challenges and perspectives in applying single nuclei RNA-seq technology in plant biology. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 325:111486. [PMID: 36202294 DOI: 10.1016/j.plantsci.2022.111486] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/12/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Plant single-cell RNA-seq technology quantifies the abundance of plant transcripts at a single-cell resolution. Deciphering the transcriptomes of each plant cell, their regulation during plant cell development, and their response to environmental stresses will support the functional study of genes, the establishment of precise transcriptional programs, the prediction of more accurate gene regulatory networks, and, in the long term, the design of de novo gene pathways to enhance selected crop traits. In this review, we will discuss the opportunities, challenges, and problems, and share tentative solutions associated with the generation and analysis of plant single-cell transcriptomes. We will discuss the benefit and limitations of using plant protoplasts vs. nuclei to conduct single-cell RNA-seq experiments on various plant species and organs, the functional annotation of plant cell types based on their transcriptomic profile, the characterization of the dynamic regulation of the plant genes during cell development or in response to environmental stress, the need to characterize and integrate additional layers of -omics datasets to capture new molecular modalities at the single-cell level and reveal their causalities, the deposition and access to single-cell datasets, and the accessibility of this technology to plant scientists.
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Affiliation(s)
- Sergio Alan Cervantes-Pérez
- Department of Agronomy and Horticulture, Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68503, USA
| | - Sandra Thibivillliers
- Department of Agronomy and Horticulture, Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68503, USA; Center for Biotechnology, University of Nebraska, Lincoln, NE 68588, USA; Single Cell Genomics Core Facility, University of Nebraska-Lincoln, NE 68588, USA
| | - Sutton Tennant
- Department of Agronomy and Horticulture, Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68503, USA
| | - Marc Libault
- Department of Agronomy and Horticulture, Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68503, USA; Center for Biotechnology, University of Nebraska, Lincoln, NE 68588, USA; Single Cell Genomics Core Facility, University of Nebraska-Lincoln, NE 68588, USA.
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Wang S, Ding P, Yuan J, Wang H, Zhang X, Chen D, Ma D, Zhang X, Wang F. Integrative cross-species analysis of GABAergic neuron cell types and their functions in Alzheimer's disease. Sci Rep 2022; 12:19358. [PMID: 36369318 PMCID: PMC9652313 DOI: 10.1038/s41598-022-21496-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding the phenotypic and functional diversity of cerebral cortical GABAergic neurons requires a comprehensive analysis of key transcriptional signatures and neuronal subtype identity. However, the diversity and conservation of GABAergic neurons across multiple mammals remain unclear. Here, we collected the single-nucleus RNA sequencing (snRNA-seq) datasets of cerebral cortex from human, macaque, mouse, and pig to identify the conserved neuronal cell types across species. After systematic analysis of the heterogeneity of GABAergic neurons, we defined four major conserved GABAergic neuron subclasses (Inc SST, Inc LAMP5, Inc PVALB, and Inc VIP) across species. We characterized the species-enriched subclasses of GABAergic neurons from four mammals, such as Inc Meis2 in mouse. Then, we depicted the genetic regulatory network (GRNs) of GABAergic neuron subclasses, which showed the conserved and species-specific GRNs for GABAergic neuron cell types. Finally, we investigated the GABAergic neuron subclass-specific expression modules of Alzheimer's disease (AD)-related genes in GABAergic neuron cell types. Overall, our study reveals the conserved and divergent GABAergic neuron subclasses and GRNs across multiple species and unravels the gene expression modules of AD-risk genes in GABAergic neuron subclasses, facilitating the GABAergic neurons research and clinical treatment.
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Affiliation(s)
- Shiyou Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Peiwen Ding
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Jingnan Yuan
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Haoyu Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Xiuqing Zhang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Dongli Ma
- Department of Respiratory Diseases, Institute of Pediatrics, Shenzhen Children's Hospital, Shenzhen, 518038, China
| | - Xingliang Zhang
- Department of Respiratory Diseases, Institute of Pediatrics, Shenzhen Children's Hospital, Shenzhen, 518038, China.
- Department of Pediatrics, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524001, China.
| | - Fei Wang
- BGI-Shenzhen, Shenzhen, 518083, China.
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
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Cha J, Yu J, Cho JW, Hemberg M, Lee I. scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes. Nucleic Acids Res 2022; 51:e8. [PMID: 36350625 PMCID: PMC9881140 DOI: 10.1093/nar/gkac1042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/19/2022] [Accepted: 10/25/2022] [Indexed: 11/10/2022] Open
Abstract
A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.
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Affiliation(s)
- Junha Cha
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Jiwon Yu
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Jae-Won Cho
- Evergrande Center for Immunologic Disease, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Martin Hemberg
- Correspondence may also be addressed to Martin Hemberg. Tel: +1 857 307 1422;
| | - Insuk Lee
- To whom correspondence should be addressed. Tel: +82 2 2123 5559; Fax: +82 2 362 7265;
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Savelieff MG, Chen KS, Elzinga SE, Feldman EL. Diabetes and dementia: Clinical perspective, innovation, knowledge gaps. J Diabetes Complications 2022; 36:108333. [PMID: 36240668 PMCID: PMC10076101 DOI: 10.1016/j.jdiacomp.2022.108333] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/30/2022] [Indexed: 10/31/2022]
Abstract
The world faces a pandemic-level prevalence of type 2 diabetes. In parallel with this massive burden of metabolic disease is the growing prevalence of dementia as the population ages. The two health issues are intertwined. The Lancet Commission on dementia prevention, intervention, and care was convened to tackle the growing global concern of dementia by identifying risk factors. It concluded, along with other studies, that diabetes as well as obesity and the metabolic syndrome more broadly, which are frequently comorbid, raise the risk of developing dementia. Type 2 diabetes is a modifiable risk factor; however, it is uncertain whether anti-diabetic drugs mitigate risk of developing dementia. Reasons are manifold but constitute a critical knowledge gap in the field. This review outlines studies of type 2 diabetes on risk of dementia, illustrating key concepts. Moreover, it identifies knowledge gaps, reviews strategies to help fill these gaps, and concludes with a series of recommendations to mitigate risk and advance understanding of type 2 diabetes and dementia.
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Affiliation(s)
- Masha G Savelieff
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kevin S Chen
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Sarah E Elzinga
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Eva L Feldman
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA.
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Yang M, Harrison BR, Promislow DEL. In search of a Drosophila core cellular network with single-cell transcriptome data. G3 GENES|GENOMES|GENETICS 2022; 12:6670625. [PMID: 35976114 PMCID: PMC9526075 DOI: 10.1093/g3journal/jkac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 08/03/2022] [Indexed: 11/29/2022]
Abstract
Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, or a subset of genes specific to certain cells, remains a central question in biology. Here, we focus on gene coexpression to search for a core cellular network across a whole organism. Single-cell RNA-sequencing measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current efforts to study cellular functions focus primarily on identifying differentially expressed genes across cells. However, patterns of coexpression between genes are probably more indicative of biological processes than are the expression of individual genes. We constructed cell-type-specific gene coexpression networks using single-cell transcriptome datasets covering diverse cell types from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types and present this as the best estimate of a core cellular network. This core is very small compared with cell-type-specific gene coexpression networks and shows dense connectivity. Gene members of this core tend to be ancient genes and are enriched for those encoding ribosomal proteins. Overall, we find evidence for a core cellular network in diverse cell types of the fruit fly. The topological, structural, functional, and evolutionary properties of this core indicate that it accounts for only a minority of essential functions.
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Affiliation(s)
- Ming Yang
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
| | - Benjamin R Harrison
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
| | - Daniel E L Promislow
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
- Department of Biology, University of Washington , Seattle, WA 98195, USA
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Saul D, Kosinsky RL, Atkinson EJ, Doolittle ML, Zhang X, LeBrasseur NK, Pignolo RJ, Robbins PD, Niedernhofer LJ, Ikeno Y, Jurk D, Passos JF, Hickson LJ, Xue A, Monroe DG, Tchkonia T, Kirkland JL, Farr JN, Khosla S. A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues. Nat Commun 2022; 13:4827. [PMID: 35974106 PMCID: PMC9381717 DOI: 10.1038/s41467-022-32552-1] [Citation(s) in RCA: 198] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 08/05/2022] [Indexed: 02/01/2023] Open
Abstract
Although cellular senescence drives multiple age-related co-morbidities through the senescence-associated secretory phenotype, in vivo senescent cell identification remains challenging. Here, we generate a gene set (SenMayo) and validate its enrichment in bone biopsies from two aged human cohorts. We further demonstrate reductions in SenMayo in bone following genetic clearance of senescent cells in mice and in adipose tissue from humans following pharmacological senescent cell clearance. We next use SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from human and murine bone marrow/bone scRNA-seq data. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Using this senescence panel, we are able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways. SenMayo also represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.
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Affiliation(s)
- Dominik Saul
- Division of Endocrinology, Mayo Clinic, Rochester, MN, 55905, USA.
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA.
- Department of Trauma, Orthopedics and Reconstructive Surgery, Georg-August-University of Goettingen, Goettingen, Germany.
| | - Robyn Laura Kosinsky
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | - Madison L Doolittle
- Division of Endocrinology, Mayo Clinic, Rochester, MN, 55905, USA
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Nathan K LeBrasseur
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Robert J Pignolo
- Division of Endocrinology, Mayo Clinic, Rochester, MN, 55905, USA
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Paul D Robbins
- Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Laura J Niedernhofer
- Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Yuji Ikeno
- Department of Pathology, University of Texas Health, San Antonio, TX, USA
| | - Diana Jurk
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - João F Passos
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - LaTonya J Hickson
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL, USA
| | - Ailing Xue
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
| | - David G Monroe
- Division of Endocrinology, Mayo Clinic, Rochester, MN, 55905, USA
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
| | - Tamara Tchkonia
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - James L Kirkland
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Joshua N Farr
- Division of Endocrinology, Mayo Clinic, Rochester, MN, 55905, USA.
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA.
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.
| | - Sundeep Khosla
- Division of Endocrinology, Mayo Clinic, Rochester, MN, 55905, USA.
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA.
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.
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Recent Progress in the Diagnosis and Management of Type 2 Diabetes Mellitus in the Era of COVID-19 and Single Cell Multi-Omics Technologies. LIFE (BASEL, SWITZERLAND) 2022; 12:life12081205. [PMID: 36013384 PMCID: PMC9409806 DOI: 10.3390/life12081205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 11/17/2022]
Abstract
Type 2 diabetes mellitus (T2DM) is one of the world’s leading causes of death and life-threatening conditions. Therefore, we review the complex vicious circle of causes responsible for T2DM and risk factors such as the western diet, obesity, genetic predisposition, environmental factors, and SARS-CoV-2 infection. The prevalence and economic burden of T2DM on societal and healthcare systems are dissected. Recent progress on the diagnosis and clinical management of T2DM, including both non-pharmacological and latest pharmacological treatment regimens, are summarized. The treatment of T2DM is becoming more complex as new medications are approved. This review is focused on the non-insulin treatments of T2DM to reach optimal therapy beyond glycemic management. We review experimental and clinical findings of SARS-CoV-2 risks that are attributable to T2DM patients. Finally, we shed light on the recent single-cell-based technologies and multi-omics approaches that have reached breakthroughs in the understanding of the pathomechanism of T2DM.
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48
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Uthamacumaran A. Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics. BIOLOGICAL CYBERNETICS 2022; 116:407-445. [PMID: 35678918 DOI: 10.1007/s00422-022-00935-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 05/04/2022] [Indexed: 06/15/2023]
Abstract
Cancers are complex dynamic ecosystems. Reductionist approaches to science are inadequate in characterizing their self-organized patterns and collective emergent behaviors. Since current approaches to single-cell analysis in cancer systems rely primarily on single time-point multiomics, many of the temporal features and causal adaptive behaviors in cancer dynamics are vastly ignored. As such, tools and concepts from the interdisciplinary paradigm of complex systems theory are introduced herein to decode the cellular cybernetics of cancer differentiation dynamics and behavioral patterns. An intuition for the attractors and complex networks underlying cancer processes such as cell fate decision-making, multiscale pattern formation systems, and epigenetic state-transitions is developed. The applications of complex systems physics in paving targeted therapies and causal pattern discovery in precision oncology are discussed. Pediatric high-grade gliomas are discussed as a model-system to demonstrate that cancers are complex adaptive systems, in which the emergence and selection of heterogeneous cellular states and phenotypic plasticity are driven by complex multiscale network dynamics. In specific, pediatric glioblastoma (GBM) is used as a proof-of-concept model to illustrate the applications of the complex systems framework in understanding GBM cell fate decisions and decoding their adaptive cellular dynamics. The scope of these tools in forecasting cancer cell fate dynamics in the emerging field of computational oncology and patient-centered systems medicine is highlighted.
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Otero S, Gildea I, Roszak P, Lu Y, Di Vittori V, Bourdon M, Kalmbach L, Blob B, Heo JO, Peruzzo F, Laux T, Fernie AR, Tavares H, Helariutta Y. A root phloem pole cell atlas reveals common transcriptional states in protophloem-adjacent cells. NATURE PLANTS 2022; 8:954-970. [PMID: 35927456 DOI: 10.1038/s41477-022-01178-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
Single-cell sequencing has recently allowed the generation of exhaustive root cell atlases. However, some cell types are elusive and remain underrepresented. Here we use a second-generation single-cell approach, where we zoom in on the root transcriptome sorting with specific markers to profile the phloem poles at an unprecedented resolution. Our data highlight the similarities among the developmental trajectories and gene regulatory networks common to protophloem sieve element (PSE)-adjacent lineages in relation to PSE enucleation, a key event in phloem biology. As a signature for early PSE-adjacent lineages, we have identified a set of DNA-binding with one finger (DOF) transcription factors, the PINEAPPLEs (PAPL), that act downstream of PHLOEM EARLY DOF (PEAR) genes and are important to guarantee a proper root nutrition in the transition to autotrophy. Our data provide a holistic view of the phloem poles that act as a functional unit in root development.
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Affiliation(s)
- Sofia Otero
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Iris Gildea
- Institute of Biotechnology, HiLIFE/Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland
- Department of Plant Biology and Genome Center, University of California, Davis, CA, USA
| | - Pawel Roszak
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Yipeng Lu
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Valerio Di Vittori
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Dipartimento di Scienze Agrarie, Alimentari ed Ambientali, Università Politecnica delle Marche, Ancona, Italy
| | - Matthieu Bourdon
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Lothar Kalmbach
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Bernhard Blob
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Jung-Ok Heo
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | | | - Thomas Laux
- Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Hugo Tavares
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK.
- Department of Genetics, University of Cambridge, Cambridge, UK.
| | - Yka Helariutta
- The Sainsbury Laboratory, University of Cambridge, Cambridge, UK.
- Institute of Biotechnology, HiLIFE/Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland.
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50
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Algabri YA, Li L, Liu ZP. scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms. Bioengineering (Basel) 2022; 9:bioengineering9080353. [PMID: 36004879 PMCID: PMC9405199 DOI: 10.3390/bioengineering9080353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/27/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene–gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.
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