1
|
Okada H, Chung UI, Hojo H. Practical Compass of Single-Cell RNA-Seq Analysis. Curr Osteoporos Rep 2024; 22:433-440. [PMID: 38019344 PMCID: PMC11420265 DOI: 10.1007/s11914-023-00840-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
PURPOSE OF REVIEW This review paper provides step-by-step instructions on the fundamental process, from handling fastq datasets to illustrating plots and drawing trajectories. RECENT FINDINGS The number of studies using single-cell RNA-seq (scRNA-seq) is increasing. scRNA-seq revealed the heterogeneity or diversity of the cellular populations. scRNA-seq also provides insight into the interactions between different cell types. User-friendly scRNA-seq packages for ligand-receptor interactions and trajectory analyses are available. In skeletal biology, osteoclast differentiation, fracture healing, ectopic ossification, human bone development, and the bone marrow niche have been examined using scRNA-seq. scRNA-seq data analysis tools are still being developed, even at the fundamental step of dataset integration. However, updating the latest information is difficult for many researchers. Investigators and reviewers must share their knowledge of in silico scRNA-seq for better biological interpretation. This review article aims to provide a useful guide for complex analytical processes in single-cell RNA-seq data analysis.
Collapse
Affiliation(s)
- Hiroyuki Okada
- Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
- Department of Orthopaedic Surgery, The University of Tokyo, Tokyo, Japan.
- Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, MA, 02115, USA.
| | - Ung-Il Chung
- Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Bunkyo-Ku, Tokyo, 113-8655, Japan
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Hironori Hojo
- Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Bunkyo-Ku, Tokyo, 113-8655, Japan
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
2
|
K Lodi M, Chernikov A, Ghosh P. COFFEE: consensus single cell-type specific inference for gene regulatory networks. Brief Bioinform 2024; 25:bbae457. [PMID: 39311699 PMCID: PMC11418232 DOI: 10.1093/bib/bbae457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/22/2024] [Accepted: 09/02/2024] [Indexed: 09/26/2024] Open
Abstract
The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.
Collapse
Affiliation(s)
- Musaddiq K Lodi
- Integrative Life Sciences, Virginia Commonwealth University, 1000 W Cary St, Richmond, VA 23284, United States
| | - Anna Chernikov
- Center for Biological Data Science, Virginia Commonwealth University, 1015 Floyd Ave, Richmond, VA 23284, United States
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, 401 W Main St, Richmond, VA 23284, United States
| |
Collapse
|
3
|
Wang Y, Zheng P, Cheng YC, Wang Z, Aravkin A. Gene regulatory network inference with covariance dynamics. Math Biosci 2024:109284. [PMID: 39168402 DOI: 10.1016/j.mbs.2024.109284] [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: 03/25/2024] [Revised: 06/25/2024] [Accepted: 08/16/2024] [Indexed: 08/23/2024]
Abstract
Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.
Collapse
Affiliation(s)
- Yue Wang
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, NewYork, 10027, NY, USA.
| | - Peng Zheng
- Institute for Health Metrics and Evaluation, Seattle, 98195, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, 98195, WA, USA
| | - Yu-Chen Cheng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, 02215, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, 02215, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, 02138, MA, USA
| | - Zikun Wang
- Laboratory of Genetics, The Rockefeller University, NewYork, 10065, NY, USA
| | - Aleksandr Aravkin
- Department of Applied Mathematics, University of Washington, Seattle, 98195, WA, USA
| |
Collapse
|
4
|
Liu B, Hu S, Wang X. Applications of single-cell technologies in drug discovery for tumor treatment. iScience 2024; 27:110486. [PMID: 39171294 PMCID: PMC11338156 DOI: 10.1016/j.isci.2024.110486] [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] [Indexed: 08/23/2024] Open
Abstract
Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.
Collapse
Affiliation(s)
- Bingyu Liu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| | - Shunfeng Hu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong 250021, China
| |
Collapse
|
5
|
Song Y, Parada G, Lee JTH, Hemberg M. Mining alternative splicing patterns in scRNA-seq data using scASfind. Genome Biol 2024; 25:197. [PMID: 39075577 PMCID: PMC11285346 DOI: 10.1186/s13059-024-03323-6] [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: 08/17/2023] [Accepted: 06/26/2024] [Indexed: 07/31/2024] Open
Abstract
Single-cell RNA-seq (scRNA-seq) is widely used for transcriptome profiling, but most analyses focus on gene-level events, with less attention devoted to alternative splicing. Here, we present scASfind, a novel computational method to allow for quantitative analysis of cell type-specific splicing events using full-length scRNA-seq data. ScASfind utilizes an efficient data structure to store the percent spliced-in value for each splicing event. This makes it possible to exhaustively search for patterns among all differential splicing events, allowing us to identify marker events, mutually exclusive events, and events involving large blocks of exons that are specific to one or more cell types.
Collapse
Affiliation(s)
- Yuyao Song
- Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
- European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, CB10 1SD, UK
| | - Guillermo Parada
- Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | | | - Martin Hemberg
- Wellcome Sanger Institute, Hinxton, CB10 1SA, UK.
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital, and Harvard Medical School, Boston, MA, 02115, USA.
| |
Collapse
|
6
|
Kubota S, Sun Y, Morii M, Bai J, Ideue T, Hirayama M, Sorin S, Eerdunduleng, Yokomizo-Nakano T, Osato M, Hamashima A, Iimori M, Araki K, Umemoto T, Sashida G. Chromatin modifier Hmga2 promotes adult hematopoietic stem cell function and blood regeneration in stress conditions. EMBO J 2024; 43:2661-2684. [PMID: 38811851 PMCID: PMC11217491 DOI: 10.1038/s44318-024-00122-4] [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: 07/14/2023] [Revised: 04/22/2024] [Accepted: 04/25/2024] [Indexed: 05/31/2024] Open
Abstract
The molecular mechanisms governing the response of hematopoietic stem cells (HSCs) to stress insults remain poorly defined. Here, we investigated effects of conditional knock-out or overexpression of Hmga2 (High mobility group AT-hook 2), a transcriptional activator of stem cell genes in fetal HSCs. While Hmga2 overexpression did not affect adult hematopoiesis under homeostasis, it accelerated HSC expansion in response to injection with 5-fluorouracil (5-FU) or in vitro treatment with TNF-α. In contrast, HSC and megakaryocyte progenitor cell numbers were decreased in Hmga2 KO animals. Transcription of inflammatory genes was repressed in Hmga2-overexpressing mice injected with 5-FU, and Hmga2 bound to distinct regions and chromatin accessibility was decreased in HSCs upon stress. Mechanistically, we found that casein kinase 2 (CK2) phosphorylates the Hmga2 acidic domain, promoting its access and binding to chromatin, transcription of anti-inflammatory target genes, and the expansion of HSCs under stress conditions. Notably, the identified stress-regulated Hmga2 gene signature is activated in hematopoietic stem progenitor cells of human myelodysplastic syndrome patients. In sum, these results reveal a TNF-α/CK2/phospho-Hmga2 axis controlling adult stress hematopoiesis.
Collapse
Affiliation(s)
- Sho Kubota
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
- Department of Medicinal Pharmacology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Yuqi Sun
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
- Department of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Mariko Morii
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Jie Bai
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Takako Ideue
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Mayumi Hirayama
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Supannika Sorin
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Eerdunduleng
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Takako Yokomizo-Nakano
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Motomi Osato
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
- Department of General Internal Medicine, Kumamoto Kenhoku Hospital, Kumamoto, Japan
| | - Ai Hamashima
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Mihoko Iimori
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Kimi Araki
- Institute of Resource Development and Analysis, Kumamoto University, Kumamoto, Japan
- Center for Metabolic Regulation of Healthy Aging, Kumamoto University, Kumamoto, Japan
| | - Terumasa Umemoto
- Laboratory of Hematopoietic Stem Cell Engineering, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Goro Sashida
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan.
| |
Collapse
|
7
|
Xie J, Ruan S, Tu M, Yuan Z, Hu J, Li H, Li S. Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding. Oncogene 2024; 43:2279-2292. [PMID: 38834657 DOI: 10.1038/s41388-024-03074-5] [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: 01/21/2024] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024]
Abstract
Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.
Collapse
Affiliation(s)
- Jinxin Xie
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Shanshan Ruan
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Mingyan Tu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Zhen Yuan
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Jianguo Hu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, Shanghai, 200062, China.
- Lingang Laboratory, Shanghai, 200031, China.
| | - Shiliang Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, Shanghai, 200062, China.
| |
Collapse
|
8
|
Wang Y, Zhou F, Guan J. SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network. Bioinformatics 2024; 40:btae433. [PMID: 38950180 PMCID: PMC11236097 DOI: 10.1093/bioinformatics/btae433] [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: 02/06/2024] [Revised: 06/18/2024] [Accepted: 06/28/2024] [Indexed: 07/03/2024] Open
Abstract
MOTIVATION The rise of single-cell RNA sequencing (scRNA-seq) technology presents new opportunities for constructing detailed cell type-specific gene regulatory networks (GRNs) to study cell heterogeneity. However, challenges caused by noises, technical errors, and dropout phenomena in scRNA-seq data pose significant obstacles to GRN inference, making the design of accurate GRN inference algorithms still essential. The recent growth of both single-cell and spatial transcriptomic sequencing data enables the development of supervised deep learning methods to infer GRNs on these diverse single-cell datasets. RESULTS In this study, we introduce a novel deep learning framework based on shared factor neighborhood and integrated neural network (SFINN) for inferring potential interactions and causalities between transcription factors and target genes from single-cell and spatial transcriptomic data. SFINN utilizes shared factor neighborhood to construct cellular neighborhood network based on gene expression data and additionally integrates cellular network generated from spatial location information. Subsequently, the cell adjacency matrix and gene pair expression are fed into an integrated neural network framework consisting of a graph convolutional neural network and a fully-connected neural network to determine whether the genes interact. Performance evaluation in the tasks of gene interaction and causality prediction against the existing GRN reconstruction algorithms demonstrates the usability and competitiveness of SFINN across different kinds of data. SFINN can be applied to infer GRNs from conventional single-cell sequencing data and spatial transcriptomic data. AVAILABILITY AND IMPLEMENTATION SFINN can be accessed at GitHub: https://github.com/JGuan-lab/SFINN.
Collapse
Affiliation(s)
- Yongjie Wang
- Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
| | - Fengfan Zhou
- Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
| | - Jinting Guan
- Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| |
Collapse
|
9
|
Moriel N, Memet E, Nitzan M. Optimal sequencing budget allocation for trajectory reconstruction of single cells. Bioinformatics 2024; 40:i446-i452. [PMID: 38940162 PMCID: PMC11211845 DOI: 10.1093/bioinformatics/btae258] [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] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations. RESULTS Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.
Collapse
Affiliation(s)
- Noa Moriel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Edvin Memet
- Department of Physics, Harvard University, Cambridge, MA 02138, United States
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| |
Collapse
|
10
|
Zhou X, Pan J, Chen L, Zhang S, Chen Y. DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data. Biomolecules 2024; 14:766. [PMID: 39062480 PMCID: PMC11274664 DOI: 10.3390/biom14070766] [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: 05/11/2024] [Revised: 06/22/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types.
Collapse
Affiliation(s)
- Xiguo Zhou
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China; (X.Z.); (J.P.); (L.C.)
| | - Jingyi Pan
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China; (X.Z.); (J.P.); (L.C.)
| | - Liang Chen
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China; (X.Z.); (J.P.); (L.C.)
| | - Shaoqiang Zhang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China; (X.Z.); (J.P.); (L.C.)
| | - Yong Chen
- Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
| |
Collapse
|
11
|
Ura H, Niida Y. Comparison of RNA-Sequencing Methods for Degraded RNA. Int J Mol Sci 2024; 25:6143. [PMID: 38892331 PMCID: PMC11172666 DOI: 10.3390/ijms25116143] [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: 05/02/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024] Open
Abstract
RNA sequencing (RNA-Seq) is a powerful technique and is increasingly being used in clinical research and drug development. Currently, several RNA-Seq methods have been developed. However, the relative advantage of each method for degraded RNA and low-input RNA, such as RNA samples collected in the field of clinical setting, has remained unknown. The Standard method of RNA-Seq captures mRNA by poly(A) capturing using Oligo dT beads, which is not suitable for degraded RNA. Here, we used three commercially available RNA-Seq library preparation kits (SMART-Seq, xGen Broad-range, and RamDA-Seq) using random primer instead of Oligo dT beads. To evaluate the performance of these methods, we compared the correlation, the number of detected expressing genes, and the expression levels with the Standard RNA-Seq method. Although the performance of RamDA-Seq was similar to that of Standard RNA-Seq, the performance for low-input RNA and degraded RNA has decreased. The performance of SMART-Seq was better than xGen and RamDA-Seq in low-input RNA and degraded RNA. Furthermore, the depletion of ribosomal RNA (rRNA) improved the performance of SMART-Seq and xGen due to increased expression levels. SMART-Seq with rRNA depletion has relative advantages for RNA-Seq using low-input and degraded RNA.
Collapse
Affiliation(s)
- Hiroki Ura
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Japan;
- Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Japan
| | - Yo Niida
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Japan;
- Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku 920-0923, Japan
| |
Collapse
|
12
|
Morii M, Kubota S, Iimori M, Yokomizo-Nakano T, Hamashima A, Bai J, Nishimura A, Tasaki M, Ando Y, Araki K, Sashida G. TIF1β activates leukemic transcriptional program in HSCs and promotes BCR::ABL1-induced myeloid leukemia. Leukemia 2024; 38:1275-1286. [PMID: 38734786 DOI: 10.1038/s41375-024-02276-w] [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: 05/04/2023] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 05/13/2024]
Abstract
TIF1β/KAP1/TRIM28, a chromatin modulator, both represses and activates the transcription of genes in normal and malignant cells. Analyses of datasets on leukemia patients revealed that the expression level of TIF1β was increased in patients with chronic myeloid leukemia at the blast crisis and acute myeloid leukemia. We generated a BCR::ABL1 conditional knock-in (KI) mouse model, which developed aggressive myeloid leukemia, and demonstrated that the deletion of the Tif1β gene inhibited the progression of myeloid leukemia and showed longer survival than that in BCR::ABL1 KI mice, suggesting that Tif1β drove the progression of BCR::ABL1-induced leukemia. In addition, the deletion of Tif1β sensitized BCR::ABL1 KI leukemic cells to dasatinib. The deletion of Tif1β decreased the expression levels of TIF1β-target genes and chromatin accessibility peaks enriched with the Fosl1-binding motif in BCR::ABL1 KI stem cells. TIF1β directly bound to the promoters of proliferation genes, such as FOSL1, in human BCR::ABL1 cells, in which TIF1β and FOSL1 bound to adjacent regions of chromatin. Since the expression of Fosl1 was critical for the enhanced growth of BCR::ABL1 KI cells, Tif1β and Fosl1 interacted to activate the leukemic transcriptional program in and cellular function of BCR::ABL1 KI stem cells and drove the progression of myeloid leukemia.
Collapse
MESH Headings
- Animals
- Mice
- Humans
- Fusion Proteins, bcr-abl/genetics
- Fusion Proteins, bcr-abl/metabolism
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/pathology
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/metabolism
- Gene Expression Regulation, Leukemic
- Tripartite Motif-Containing Protein 28/metabolism
- Tripartite Motif-Containing Protein 28/genetics
- Transcription, Genetic
Collapse
Affiliation(s)
- Mariko Morii
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Sho Kubota
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Mihoko Iimori
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Takako Yokomizo-Nakano
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Ai Hamashima
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Jie Bai
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Akiho Nishimura
- Gastrointestinal Cancer Biology, International Research Center of Medical Sciences, Kumamoto University, Kumamoto, 860-0811, Japan
| | - Masayoshi Tasaki
- Department of Biomedical Laboratory Sciences, Graduate School of Health Sciences, Kumamoto University, Kumamoto, Japan
| | - Yukio Ando
- Department of Amyloidosis Research, Nagasaki International University, Sasebo, Japan
| | - Kimi Araki
- Institute of Resource Development and Analysis, Kumamoto University, Kumamoto, Japan
- Center for Metabolic Regulation of Healthy Aging, Kumamoto University, Kumamoto, Japan
| | - Goro Sashida
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan.
| |
Collapse
|
13
|
Wan R, Zhang Y, Peng Y, Tian F, Gao G, Tang F, Jia J, Ge H. Unveiling gene regulatory networks during cellular state transitions without linkage across time points. Sci Rep 2024; 14:12355. [PMID: 38811747 PMCID: PMC11137113 DOI: 10.1038/s41598-024-62850-1] [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: 01/24/2024] [Accepted: 05/22/2024] [Indexed: 05/31/2024] Open
Abstract
Time-stamped cross-sectional data, which lack linkage across time points, are commonly generated in single-cell transcriptional profiling. Many previous methods for inferring gene regulatory networks (GRNs) driving cell-state transitions relied on constructing single-cell temporal ordering. Introducing COSLIR (COvariance restricted Sparse LInear Regression), we presented a direct approach to reconstructing GRNs that govern cell-state transitions, utilizing only the first and second moments of samples between two consecutive time points. Simulations validated COSLIR's perfect accuracy in the oracle case and demonstrated its robust performance in real-world scenarios. When applied to single-cell RT-PCR and RNAseq datasets in developmental biology, COSLIR competed favorably with existing methods. Notably, its running time remained nearly independent of the number of cells. Therefore, COSLIR emerges as a promising addition to GRN reconstruction methods under cell-state transitions, bypassing the single-cell temporal ordering to enhance accuracy and efficiency in single-cell transcriptional profiling.
Collapse
Affiliation(s)
- Ruosi Wan
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Yuhao Zhang
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Yongli Peng
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Feng Tian
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Ge Gao
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Fuchou Tang
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Jinzhu Jia
- School of Public Health and Center for Statistical Science, Peking University, Beijing, China.
| | - Hao Ge
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China.
| |
Collapse
|
14
|
Nakamura M, Matsumoto M, Ito T, Hidaka I, Tatsuta H, Katsumoto Y. Microfluidic device for the high-throughput and selective encapsulation of single target cells. LAB ON A CHIP 2024; 24:2958-2967. [PMID: 38722067 DOI: 10.1039/d4lc00037d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Droplet-based microfluidic technologies for encapsulating single cells have rapidly evolved into powerful tools for single-cell analysis. In conventional passive single-cell encapsulation techniques, because cells arrive randomly at the droplet generation section, to encapsulate only a single cell with high precision, the average number of cells per droplet has to be decreased by reducing the average frequency at which cells arrive relative to the droplet generation rate. Therefore, the encapsulation efficiency for a given droplet generation rate is very low. Additionally, cell sorting operations are required prior to the encapsulation of target cells for specific cell type analysis. To address these challenges, we developed a cell encapsulation technology with a cell sorting function using a microfluidic chip. The microfluidic chip is equipped with an optical detection section to detect the optical information of cells and a sorting section to encapsulate cells into droplets by controlling a piezo element, enabling active encapsulation of only the single target cells. For a particle population including both targeted and non-targeted particles arriving at an average frequency of up to 6000 particles per s, with an average number of particles per droplet of 0.45, our device maintained a high purity above 97.9% for the single-target-particle droplets and achieved an outstanding throughput, encapsulating up to 2900 single target particles per s. The proposed encapsulation technology surpasses the encapsulation efficiency of conventional techniques, provides high efficiency and flexibility for single-cell research, and shows excellent potential for various applications in single-cell analysis.
Collapse
Affiliation(s)
- Masahiko Nakamura
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Masahiro Matsumoto
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Tatsumi Ito
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Isao Hidaka
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Hirokazu Tatsuta
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Yoichi Katsumoto
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| |
Collapse
|
15
|
Liu W, Teng Z, Li Z, Chen J. CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data. Interdiscip Sci 2024:10.1007/s12539-024-00633-y. [PMID: 38778003 DOI: 10.1007/s12539-024-00633-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 05/25/2024]
Abstract
Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.
Collapse
Affiliation(s)
- Wei Liu
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.
| | - Zhijie Teng
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang, 412002, China
| | - Jing Chen
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
| |
Collapse
|
16
|
Jia R, Ren YZ, Li PN, Gao R, Zhang YS. SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition. Brief Bioinform 2024; 25:bbae273. [PMID: 38855914 PMCID: PMC11163303 DOI: 10.1093/bib/bbae273] [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: 01/20/2024] [Revised: 04/25/2024] [Accepted: 05/30/2024] [Indexed: 06/11/2024] Open
Abstract
Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.
Collapse
Affiliation(s)
- Ran Jia
- School of Mathematics and Statistics, Shandong University, Weihai 264209, Shandong, China
| | - Ying-Zan Ren
- School of Mathematics and Statistics, Shandong University, Weihai 264209, Shandong, China
| | - Po-Nian Li
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong, China
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan 250100, Shandong, China
| | - Yu-Sen Zhang
- School of Mathematics and Statistics, Shandong University, Weihai 264209, Shandong, China
| |
Collapse
|
17
|
Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [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: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
Collapse
Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| |
Collapse
|
18
|
Yuan CU, Quah FX, Hemberg M. Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing. Mol Aspects Med 2024; 96:101255. [PMID: 38368637 DOI: 10.1016/j.mam.2024.101255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/20/2024]
Abstract
Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.
Collapse
Affiliation(s)
- Chengwei Ulrika Yuan
- Department of Biochemistry, University of Cambridge, Cambridge, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Fu Xiang Quah
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Martin Hemberg
- Gene Lay Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
19
|
Ye F, Zhang S, Fu Y, Yang L, Zhang G, Wu Y, Pan J, Chen H, Wang X, Ma L, Niu H, Jiang M, Zhang T, Jia D, Wang J, Wang Y, Han X, Guo G. Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3. Cell Discov 2024; 10:33. [PMID: 38531851 DOI: 10.1038/s41421-023-00642-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 12/21/2023] [Indexed: 03/28/2024] Open
Abstract
Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.
Collapse
Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuang Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guodong Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yijun Wu
- Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jun Pan
- Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haide Chen
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xinru Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haofu Niu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mengmeng Jiang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingyue Zhang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Danmei Jia
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yongcheng Wang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang, China.
- Institute of Hematology, Zhejiang University, Hangzhou, Zhejiang, China.
| |
Collapse
|
20
|
Chen Y, Wang X, Na X, Zhang Y, Li Z, Chen X, Cai L, Song J, Xu R, Yang C. Highly Multiplexed, Efficient, and Automated Single-Cell MicroRNA Sequencing with Digital Microfluidics. SMALL METHODS 2024; 8:e2301250. [PMID: 38016072 DOI: 10.1002/smtd.202301250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/14/2023] [Indexed: 11/30/2023]
Abstract
Single-cell microRNA (miRNA) sequencing has allowed for comprehensively studying the abundance and complex networks of miRNAs, which provides insights beyond single-cell heterogeneity into the dynamic regulation of cellular events. Current benchtop-based technologies for single-cell miRNA sequencing are low throughput, limited reaction efficiency, tedious manual operations, and high reagent costs. Here, a highly multiplexed, efficient, integrated, and automated sample preparation platform is introduced for single-cell miRNA sequencing based on digital microfluidics (DMF), named Hiper-seq. The platform integrates major steps and automates the iterative operations of miRNA sequencing library construction by digital control of addressable droplets on the DMF chip. Based on the design of hydrophilic micro-structures and the capability of handling droplets of DMF, multiple single cells can be selectively isolated and subject to sample processing in a highly parallel way, thus increasing the throughput and efficiency for single-cell miRNA measurement. The nanoliter reaction volume of this platform enables a much higher miRNA detection ability and lower reagent cost compared to benchtop methods. It is further applied Hiper-seq to explore miRNAs involved in the ossification of mouse skeletal stem cells after bone fracture and discovered unreported miRNAs that regulate bone repairing.
Collapse
Affiliation(s)
- Yingwen Chen
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xuanqun Wang
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xing Na
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Yingkun Zhang
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Zan Li
- Department of Sports Medicine, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Xiaohui Chen
- State Key Laboratory of Cellular Stress Biology, The First Affiliated Hospital of Xiamen University-ICMRS Collaborating Center for Skeletal Stem Cell, School of Medicine, Xiamen University, Xiamen, 361100, China
- Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen, 361100, China
| | - Linfeng Cai
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Ren Xu
- State Key Laboratory of Cellular Stress Biology, The First Affiliated Hospital of Xiamen University-ICMRS Collaborating Center for Skeletal Stem Cell, School of Medicine, Xiamen University, Xiamen, 361100, China
- Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen, 361100, China
| | - Chaoyong Yang
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| |
Collapse
|
21
|
Yang G, Xin Q, Dean J. Degradation and translation of maternal mRNA for embryogenesis. Trends Genet 2024; 40:238-249. [PMID: 38262796 DOI: 10.1016/j.tig.2023.12.008] [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: 09/25/2023] [Revised: 12/23/2023] [Accepted: 12/29/2023] [Indexed: 01/25/2024]
Abstract
Maternal mRNAs accumulate during egg growth and must be judiciously degraded or translated to ensure successful development of mammalian embryos. In this review we integrate recent investigations into pathways controlling rapid degradation of maternal mRNAs during the maternal-to-zygotic transition. Degradation is not indiscriminate, and some mRNAs are selectively protected and rapidly translated after fertilization for reprogramming the zygotic genome during early embryogenesis. Oocyte specific cofactors and pathways have been illustrated to control different futures of maternal mRNAs. We discuss mechanisms that control the fate of maternal mRNAs during late oogenesis and after fertilization. Issues to be resolved in current maternal mRNA research are described, and future research directions are proposed.
Collapse
Affiliation(s)
- Guanghui Yang
- Laboratory of Cellular and Developmental Biology, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Qiliang Xin
- Laboratory of Cellular and Developmental Biology, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jurrien Dean
- Laboratory of Cellular and Developmental Biology, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA.
| |
Collapse
|
22
|
Takakura Y, Machida M, Terada N, Katsumi Y, Kawamura S, Horie K, Miyauchi M, Ishikawa T, Akiyama N, Seki T, Miyao T, Hayama M, Endo R, Ishii H, Maruyama Y, Hagiwara N, Kobayashi TJ, Yamaguchi N, Takano H, Akiyama T, Yamaguchi N. Mitochondrial protein C15ORF48 is a stress-independent inducer of autophagy that regulates oxidative stress and autoimmunity. Nat Commun 2024; 15:953. [PMID: 38296961 PMCID: PMC10831050 DOI: 10.1038/s41467-024-45206-1] [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: 03/21/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024] Open
Abstract
Autophagy is primarily activated by cellular stress, such as starvation or mitochondrial damage. However, stress-independent autophagy is activated by unclear mechanisms in several cell types, such as thymic epithelial cells (TECs). Here we report that the mitochondrial protein, C15ORF48, is a critical inducer of stress-independent autophagy. Mechanistically, C15ORF48 reduces the mitochondrial membrane potential and lowers intracellular ATP levels, thereby activating AMP-activated protein kinase and its downstream Unc-51-like kinase 1. Interestingly, C15ORF48-dependent induction of autophagy upregulates intracellular glutathione levels, promoting cell survival by reducing oxidative stress. Mice deficient in C15orf48 show a reduction in stress-independent autophagy in TECs, but not in typical starvation-induced autophagy in skeletal muscles. Moreover, C15orf48-/- mice develop autoimmunity, which is consistent with the fact that the stress-independent autophagy in TECs is crucial for the thymic self-tolerance. These results suggest that C15ORF48 induces stress-independent autophagy, thereby regulating oxidative stress and self-tolerance.
Collapse
Affiliation(s)
- Yuki Takakura
- Department of Molecular Cardiovascular Pharmacology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
- Laboratory of Molecular Cell Biology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Moeka Machida
- Department of Molecular Cardiovascular Pharmacology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
- Laboratory of Molecular Cell Biology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
| | - Natsumi Terada
- Department of Molecular Cardiovascular Pharmacology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
- Laboratory of Molecular Cell Biology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
| | - Yuka Katsumi
- Department of Molecular Cardiovascular Pharmacology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
| | - Seika Kawamura
- Department of Molecular Cardiovascular Pharmacology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
| | - Kenta Horie
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Maki Miyauchi
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Immunobiology, Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan
| | - Tatsuya Ishikawa
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Immunobiology, Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan
| | - Nobuko Akiyama
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Takao Seki
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Takahisa Miyao
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Immunobiology, Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan
| | - Mio Hayama
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Immunobiology, Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan
| | - Rin Endo
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Immunobiology, Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan
| | - Hiroto Ishii
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Immunobiology, Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan
| | - Yuya Maruyama
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Immunobiology, Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan
| | - Naho Hagiwara
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Tetsuya J Kobayashi
- Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
| | - Naoto Yamaguchi
- Laboratory of Molecular Cell Biology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
| | - Hiroyuki Takano
- Department of Molecular Cardiovascular Pharmacology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan
| | - Taishin Akiyama
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
- Immunobiology, Graduate School of Medical Life Science, Yokohama City University, Yokohama, 230-0045, Japan.
| | - Noritaka Yamaguchi
- Department of Molecular Cardiovascular Pharmacology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan.
- Laboratory of Molecular Cell Biology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan.
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
| |
Collapse
|
23
|
Koga Y, Kajitani N, Miyako K, Takizawa H, Boku S, Takebayashi M. TCF7L2: A potential key regulator of antidepressant effects on hippocampal astrocytes in depression model mice. J Psychiatr Res 2024; 170:375-386. [PMID: 38215648 DOI: 10.1016/j.jpsychires.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/29/2023] [Accepted: 01/05/2024] [Indexed: 01/14/2024]
Abstract
Clinical and preclinical studies suggest that hippocampal astrocyte dysfunction is involved in the pathophysiology of depression; however, the underlying molecular mechanisms remain unclear. Here, we attempted to identify the hippocampal astrocytic transcripts associated with antidepressant effects in a mouse model of depression. We used a chronic corticosterone-induced mouse model of depression to assess the behavioral effects of amitriptyline, a tricyclic antidepressant. Hippocampal astrocytes were isolated using fluorescence-activated cell sorting, and RNA sequencing was performed to evaluate the transcriptional profiles associated with depressive effects and antidepressant responses. Depression model mice exhibited typical depression-like behaviors that improved after amitriptyline treatment; the depression group mice also had significantly reduced GFAP-positive astrocyte numbers in hippocampal subfields. Comprehensive transcriptome analysis of hippocampal astrocytes showed opposing responses to amitriptyline in depression group and control mice, suggesting the importance of using the depression model. Transcription factor 7 like 2 (TCF7L2) was the only upstream regulator gene altered in depression model mice and restored in amitriptyline-treated depression model mice. In fact, TCF7L2 expression was significantly decreased in the depression group. The level of TCF7L2 long non-coding RNA, which controls mRNA expression of the TCF7L2 gene, was also significantly decreased in this group and recovered after amitriptyline treatment. The Gene Ontology biological processes associated with astrocytic TCF7L2 included proliferation, differentiation, and cytokine production. We identified TCF7L2 as a gene associated with depression- and antidepressant-like behaviors in response to amitriptyline in hippocampal astrocytes. Our findings could provide valuable insights into the mechanism of astrocyte-mediated antidepressant effects.
Collapse
Affiliation(s)
- Yusaku Koga
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Naoto Kajitani
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan; Center for Metabolic Regulation of Healthy Aging, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Kotaro Miyako
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Hitoshi Takizawa
- Center for Metabolic Regulation of Healthy Aging, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan; International Research Center for Medical Sciences, Kumamoto University, 2-2-1, Honjo, Chuo-ku, Kumamoto, 860-0811, Japan
| | - Shuken Boku
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan.
| | - Minoru Takebayashi
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan.
| |
Collapse
|
24
|
da Silva JEH, de Carvalho PC, Camata JJ, de Oliveira IL, Bernardino HS. A Data-Distribution and Successive Spline Points based discretization approach for evolving gene regulatory networks from scRNA-Seq time-series data using Cartesian Genetic Programming. Biosystems 2024; 236:105126. [PMID: 38278505 DOI: 10.1016/j.biosystems.2024.105126] [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: 07/03/2023] [Revised: 11/18/2023] [Accepted: 01/19/2024] [Indexed: 01/28/2024]
Abstract
The inference of gene regulatory networks (GRNs) is a widely addressed problem in Systems Biology. GRNs can be modeled as Boolean networks, which is the simplest approach for this task. However, Boolean models need binarized data. Several approaches have been developed for the discretization of gene expression data (GED). Also, the advance of data extraction technologies, such as single-cell RNA-Sequencing (scRNA-Seq), provides a new vision of gene expression and brings new challenges for dealing with its specificities, such as a large occurrence of zero data. This work proposes a new discretization approach for dealing with scRNA-Seq time-series data, named Distribution and Successive Spline Points Discretization (DSSPD), which considers the data distribution and a proper preprocessing step. Here, Cartesian Genetic Programming (CGP) is used to infer GRNs using the results of DSSPD. The proposal is compared with CGP with the standard data handling and five state-of-the-art algorithms on curated models and experimental data. The results show that the proposal improves the results of CGP in all tested cases and outperforms the state-of-the-art algorithms in most cases.
Collapse
Affiliation(s)
| | | | - José J Camata
- Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil.
| | | | | |
Collapse
|
25
|
Miyako K, Kajitani N, Koga Y, Takizawa H, Boku S, Takebayashi M. Identification of the antidepressant effect of electroconvulsive stimulation-related genes in hippocampal astrocyte. J Psychiatr Res 2024; 170:318-327. [PMID: 38194849 DOI: 10.1016/j.jpsychires.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/13/2023] [Accepted: 01/03/2024] [Indexed: 01/11/2024]
Abstract
Major depressive disorder (MDD) remains a significant global health concern, with limited and slow efficacy of existing antidepressants. Electroconvulsive therapy (ECT) has superior and immediate efficacy for MDD, but its action mechanism remains elusive. Therefore, the elucidation of the action mechanism of ECT is expected to lead to the development of novel antidepressants with superior and immediate efficacy. Recent studies suggest a potential role of hippocampal astrocyte in MDD and ECT. Hence, we investigated antidepressant effect of electroconvulsive stimulation (ECS), an animal model of ECT, -related genes in hippocampal astrocyte with a mouse model of MDD, in which corticosterone (CORT)-induced depression-like behaviors were recovered by ECS. In this model, both of CORT-induced depression-like behaviors and the reduction of hippocampal astrocyte were recovered by ECS. Following it, astrocytes were isolated from the hippocampus of this model and RNA-seq was performed with these isolated astrocytes. Interestingly, gene expression patterns altered by CORT were reversed by ECS. Additionally, cell proliferation-related signaling pathways were inhibited by CORT and recovered by ECS. Finally, serum and glucocorticoid kinase-1 (SGK1), a multi-functional protein kinase, was identified as a candidate gene reciprocally regulated by CORT and ECS in hippocampal astrocyte. Our findings suggest a potential role of SGK1 in the antidepressant effect of ECS via the regulation of the proliferation of astrocyte and provide new insights into the involvement of hippocampal astrocyte in MDD and ECT. Targeting SGK1 may offer a novel approach to the development of new antidepressants which can replicate superior and immediate efficacy of ECT.
Collapse
Affiliation(s)
- Kotaro Miyako
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Naoto Kajitani
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; Center for Metabolic Regulation of Healthy Aging, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Yusaku Koga
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Hitoshi Takizawa
- International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Shuken Boku
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan.
| | - Minoru Takebayashi
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan.
| |
Collapse
|
26
|
Li S, Liu Y, Shen LC, Yan H, Song J, Yu DJ. GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference. Brief Bioinform 2024; 25:bbad529. [PMID: 38261340 PMCID: PMC10805180 DOI: 10.1093/bib/bbad529] [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: 09/29/2023] [Revised: 12/08/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
The recent advances of single-cell RNA sequencing (scRNA-seq) have enabled reliable profiling of gene expression at the single-cell level, providing opportunities for accurate inference of gene regulatory networks (GRNs) on scRNA-seq data. Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor-gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed <10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https://github.com/Lishuoyy/GMFGRN.
Collapse
Affiliation(s)
- Shuo Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Yan Liu
- School of information Engineering, Yangzhou University, 196 West Huayang, Yangzhou, 225000, China
| | - Long-Chen Shen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - He Yan
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| |
Collapse
|
27
|
Lodi MK, Chernikov A, Ghosh P. COFFEE: Consensus Single Cell-Type Specific Inference for Gene Regulatory Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574445. [PMID: 38260386 PMCID: PMC10802453 DOI: 10.1101/2024.01.05.574445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared to individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated and experimental datasets when compared to baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level.
Collapse
Affiliation(s)
- Musaddiq K Lodi
- Integrative Life Sciences, Virginia Commonwealth University, Richmond, VA 23284
| | - Anna Chernikov
- Center for Biological Data Science, Virginia Commonwealth University, Richmond, VA 23284
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284
| |
Collapse
|
28
|
Shi X, Yue C, Quan M, Li Y, Nashwan Sam H. A semi-supervised ensemble clustering algorithm for discovering relationships between different diseases by extracting cell-to-cell biological communications. J Cancer Res Clin Oncol 2024; 150:3. [PMID: 38168012 DOI: 10.1007/s00432-023-05559-4] [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: 07/14/2023] [Accepted: 11/01/2023] [Indexed: 01/05/2024]
Abstract
INTRODUCTION In recent decades, many theories have been proposed about the cause of hereditary diseases such as cancer. However, most studies state genetic and environmental factors as the most important parameters. It has been shown that gene expression data are valuable information about hereditary diseases and their analysis can identify the relationships between these diseases. OBJECTIVE Identification of damaged genes from various diseases can be done through the discovery of cell-to-cell biological communications. Also, extraction of intercellular communications can identify relationships between different diseases. For example, gene disorders that cause damage to the same cells in both breast and blood cancers. Hence, the purpose is to discover cell-to-cell biological communications in gene expression data. METHODOLOGY The identification of cell-to-cell biological communications for various cancer diseases has been widely performed by clustering algorithms. However, this field remains open due to the abundance of unprocessed gene expression data. Accordingly, this paper focuses on the development of a semi-supervised ensemble clustering algorithm that can discover relationships between different diseases through the extraction of cell-to-cell biological communications. The proposed clustering framework includes a stratified feature sampling mechanism and a novel similarity metric to deal with high-dimensional data and improve the diversity of primary partitions. RESULTS The performance of the proposed clustering algorithm is verified with several datasets from the UCI machine learning repository and then applied to the FANTOM5 dataset to extract cell-to-cell biological communications. The used version of this dataset contains 108 cells and 86,427 promoters from 702 samples. The strength of communication between two similar cells from different diseases indicates the relationship of those diseases. Here, the strength of communication is determined by promoter, so we found the highest cell-to-cell biological communication between "basophils" and "ciliary.epithelial.cells" with 62,809 promoters. CONCLUSION The maximum cell-to-cell biological similarity in each cluster can be used to detect the relationship between different diseases such as cancer.
Collapse
Affiliation(s)
- Xiuchao Shi
- College of Environment and Life Sciences, Weinan Normal University, Weinan, 714099, Shaanxi, China.
| | - Chunxiao Yue
- Weinan Junior Middle School, Weinan, 714000, Shaanxi, China
| | - Meiping Quan
- College of Environment and Life Sciences, Weinan Normal University, Weinan, 714099, Shaanxi, China
| | - Yalin Li
- College of Environment and Life Sciences, Weinan Normal University, Weinan, 714099, Shaanxi, China
| | - Hiba Nashwan Sam
- Department of Radiology and Sonar Techniques, Al-Noor University College, Nineveh, Iraq
| |
Collapse
|
29
|
Wang P, Wen X, Li H, Lang P, Li S, Lei Y, Shu H, Gao L, Zhao D, Zeng J. Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nat Commun 2023; 14:8459. [PMID: 38123534 PMCID: PMC10733330 DOI: 10.1038/s41467-023-44103-3] [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: 02/07/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.
Collapse
Affiliation(s)
- Peizhuo Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Xiao Wen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Peng Lang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Yipin Lei
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, Shaanxi Province, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China.
| |
Collapse
|
30
|
Chen Y, Paramo MI, Zhang Y, Yao L, Shah SR, Jin Y, Zhang J, Pan X, Yu H. Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants. Annu Rev Genet 2023; 57:201-222. [PMID: 37562413 DOI: 10.1146/annurev-genet-030723-120717] [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: 08/12/2023]
Abstract
Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.
Collapse
Affiliation(s)
- You Chen
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Mauricio I Paramo
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yingying Zhang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Li Yao
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Sagar R Shah
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yiyang Jin
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Junke Zhang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Xiuqi Pan
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| |
Collapse
|
31
|
Wang KT, Adler CE. CRISPR/Cas9-based depletion of 16S ribosomal RNA improves library complexity of single-cell RNA-sequencing in planarians. BMC Genomics 2023; 24:625. [PMID: 37864134 PMCID: PMC10588366 DOI: 10.1186/s12864-023-09724-4] [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: 06/28/2023] [Accepted: 10/08/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Single-cell RNA-sequencing (scRNA-seq) relies on PCR amplification to retrieve information from vanishingly small amounts of starting material. To selectively enrich mRNA from abundant non-polyadenylated transcripts, poly(A) selection is a key step during library preparation. However, some transcripts, such as mitochondrial genes, can escape this elimination and overwhelm libraries. Often, these transcripts are removed in silico, but whether physical depletion improves detection of rare transcripts in single cells is unclear. RESULTS We find that a single 16S ribosomal RNA is widely enriched in planarian scRNA-seq datasets, independent of the library preparation method. To deplete this transcript from scRNA-seq libraries, we design 30 single-guide RNAs spanning its length. To evaluate the effects of depletion, we perform a side-by-side comparison of the effects of eliminating the 16S transcript and find a substantial increase in the number of genes detected per cell, coupled with virtually complete loss of the 16S RNA. Moreover, we systematically determine that library complexity increases with a limited number of PCR cycles following CRISPR treatment. When compared to in silico depletion of 16S, physically removing it reduces dropout rates, retrieves more clusters, and reveals more differentially expressed genes. CONCLUSIONS Our results show that abundant transcripts reduce the retrieval of informative transcripts in scRNA-seq and distort the analysis. Physical removal of these contaminants enables the detection of rare transcripts at lower sequencing depth, and also outperforms in silico depletion. Importantly, this method can be easily customized to deplete any abundant transcript from scRNA-seq libraries.
Collapse
Affiliation(s)
- Kuang-Tse Wang
- Department of Molecular Medicine, Cornell University College of Veterinary Medicine, Ithaca, NY, USA
| | - Carolyn E Adler
- Department of Molecular Medicine, Cornell University College of Veterinary Medicine, Ithaca, NY, USA.
| |
Collapse
|
32
|
Kumazaki T, Yonekawa C, Tsubouchi T. Microscopic Analysis of Cell Fate Alteration Induced by Cell Fusion. Cell Reprogram 2023; 25:251-259. [PMID: 37847898 DOI: 10.1089/cell.2023.0073] [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] [Indexed: 10/19/2023] Open
Abstract
In mammals, differentiated cells generally do not de-differentiate nor undergo cell fate alterations. However, they can be experimentally guided toward a different lineage. Cell fusion involving two different cell types has long been used to study this process, as this method induces cell fate alterations within hours to days in a subpopulation of fused cells, as evidenced by changes in gene-expression profiles. Despite the robustness of this system, its use has been restricted by low fusion rates and difficulty in eliminating unfused populations, thereby compromising resolution. In this study, we address these limitations by isolating fused cells using antibody-conjugated beads. This approach enables the microscopic tracking of fused cells starting as early as 5 hours after fusion. By taking advantage of species-specific FISH probes, we show that a small population of fused cells resulting from the fusion of mouse ES and human B cells, expresses OCT4 from human nuclei at levels comparable to human induced pluripotent stem cells (iPSCs) as early as 25 hours after fusion. We also show that this response can vary depending on the fusion partner. Our study broadens the usage of the cell fusion system for comprehending the mechanisms underlying cell fate alterations. These findings hold promise for diverse fields, including regenerative medicine and cancer.
Collapse
Affiliation(s)
- Taisei Kumazaki
- Laboratory of Stem Cell Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan
- The Graduate University for Advanced Studies, SOKENDAI, Shonah Village, Hayama, 240-0193, Japan
| | - Chinatsu Yonekawa
- Laboratory of Stem Cell Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan
| | - Tomomi Tsubouchi
- Laboratory of Stem Cell Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan
- The Graduate University for Advanced Studies, SOKENDAI, Shonah Village, Hayama, 240-0193, Japan
| |
Collapse
|
33
|
Mao G, Pang Z, Zuo K, Wang Q, Pei X, Chen X, Liu J. Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks. Brief Bioinform 2023; 24:bbad414. [PMID: 37985457 PMCID: PMC10661972 DOI: 10.1093/bib/bbad414] [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: 08/03/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink.
Collapse
Affiliation(s)
- Guo Mao
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Zhengbin Pang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Ke Zuo
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Qinglin Wang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Xiangdong Pei
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Xinhai Chen
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
| | - Jie Liu
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, deya, 410073 Changsha, China
- Laboratory of Software Engineering for Complex System, National University of Defense Technology, deya, 410073 Changsha, China
| |
Collapse
|
34
|
Shojaee A, Huang SSC. Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions. Brief Bioinform 2023; 24:bbad370. [PMID: 37897702 PMCID: PMC10612495 DOI: 10.1093/bib/bbad370] [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: 06/18/2023] [Revised: 09/06/2023] [Accepted: 09/29/2023] [Indexed: 10/30/2023] Open
Abstract
Gene regulatory networks (GRNs) drive organism structure and functions, so the discovery and characterization of GRNs is a major goal in biological research. However, accurate identification of causal regulatory connections and inference of GRNs using gene expression datasets, more recently from single-cell RNA-seq (scRNA-seq), has been challenging. Here we employ the innovative method of Causal Inference Using Composition of Transactions (CICT) to uncover GRNs from scRNA-seq data. The basis of CICT is that if all gene expressions were random, a non-random regulatory gene should induce its targets at levels different from the background random process, resulting in distinct patterns in the whole relevance network of gene-gene associations. CICT proposes novel network features derived from a relevance network, which enable any machine learning algorithm to predict causal regulatory edges and infer GRNs. We evaluated CICT using simulated and experimental scRNA-seq data in a well-established benchmarking pipeline and showed that CICT outperformed existing network inference methods representing diverse approaches with many-fold higher accuracy. Furthermore, we demonstrated that GRN inference with CICT was robust to different levels of sparsity in scRNA-seq data, the characteristics of data and ground truth, the choice of association measure and the complexity of the supervised machine learning algorithm. Our results suggest aiming at directly predicting causality to recover regulatory relationships in complex biological networks substantially improves accuracy in GRN inference.
Collapse
Affiliation(s)
- Abbas Shojaee
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Shao-shan Carol Huang
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| |
Collapse
|
35
|
Wang J, Chen Y, Zou Q. Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model. PLoS Genet 2023; 19:e1010942. [PMID: 37703293 PMCID: PMC10519590 DOI: 10.1371/journal.pgen.1010942] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/25/2023] [Accepted: 08/29/2023] [Indexed: 09/15/2023] Open
Abstract
The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition.
Collapse
Affiliation(s)
- Jiacheng Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Yaojia Chen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| |
Collapse
|
36
|
Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
Collapse
Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| |
Collapse
|
37
|
Nishimura M, Takeyama H, Hosokawa M. Enhancing the sensitivity of bacterial single-cell RNA sequencing using RamDA-seq and Cas9-based rRNA depletion. J Biosci Bioeng 2023; 136:152-158. [PMID: 37311684 DOI: 10.1016/j.jbiosc.2023.05.010] [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: 03/03/2023] [Revised: 05/01/2023] [Accepted: 05/18/2023] [Indexed: 06/15/2023]
Abstract
Bacterial populations exhibit heterogeneity in gene expression, which facilitates their survival and adaptation to unstable and unpredictable environments through the bet-hedging strategy. However, unraveling the rare subpopulations and heterogeneity in gene expression using population-level gene expression analysis remains a challenging task. Single-cell RNA sequencing (scRNA-seq) has the potential to identify rare subpopulations and capture heterogeneity in bacterial populations, but standard methods for scRNA-seq in bacteria are still under development, mainly due to differences in mRNA abundance and structure between eukaryotic and prokaryotic organisms. In this study, we present a hybrid approach that combines random displacement amplification sequencing (RamDA-seq) with Cas9-based rRNA depletion for scRNA-seq in bacteria. This approach allows cDNA amplification and subsequent sequencing library preparation from low-abundance bacterial RNAs. We evaluated its sequenced read proportion, gene detection sensitivity, and gene expression patterns from the dilution series of total RNA or the sorted single Escherichia coli cells. Our results demonstrated the detection of more than 1000 genes, about 24% of the genes in the E. coli genome, from single cells with less sequencing effort compared to conventional methods. We observed gene expression clusters between different cellular proliferation states or heat shock treatment. The approach demonstrated high detection sensitivity in gene expression analysis compared to current bacterial scRNA-seq methods and proved to be an invaluable tool for understanding the ecology of bacterial populations and capturing the heterogeneity of bacterial gene expression.
Collapse
Affiliation(s)
- Mika Nishimura
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Haruko Takeyama
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan; Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan; Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan; Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Masahito Hosokawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan; Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan; Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan; Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
| |
Collapse
|
38
|
Yokomizo-Nakano T, Hamashima A, Kubota S, Bai J, Sorin S, Sun Y, Kikuchi K, Iimori M, Morii M, Kanai A, Iwama A, Huang G, Kurotaki D, Takizawa H, Matsui H, Sashida G. Exposure to microbial products followed by loss of Tet2 promotes myelodysplastic syndrome via remodeling HSCs. J Exp Med 2023; 220:e20220962. [PMID: 37071125 PMCID: PMC10120406 DOI: 10.1084/jem.20220962] [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: 06/03/2022] [Revised: 01/11/2023] [Accepted: 03/28/2023] [Indexed: 04/19/2023] Open
Abstract
Aberrant innate immune signaling in myelodysplastic syndrome (MDS) hematopoietic stem/progenitor cells (HSPCs) has been implicated as a driver of the development of MDS. We herein demonstrated that a prior stimulation with bacterial and viral products followed by loss of the Tet2 gene facilitated the development of MDS via up-regulating the target genes of the Elf1 transcription factor and remodeling the epigenome in hematopoietic stem cells (HSCs) in a manner that was dependent on Polo-like kinases (Plk) downstream of Tlr3/4-Trif signaling but did not increase genomic mutations. The pharmacological inhibition of Plk function or the knockdown of Elf1 expression was sufficient to prevent the epigenetic remodeling in HSCs and diminish the enhanced clonogenicity and the impaired erythropoiesis. Moreover, this Elf1-target signature was significantly enriched in MDS HSPCs in humans. Therefore, prior infection stress and the acquisition of a driver mutation remodeled the transcriptional and epigenetic landscapes and cellular functions in HSCs via the Trif-Plk-Elf1 axis, which promoted the development of MDS.
Collapse
Affiliation(s)
- Takako Yokomizo-Nakano
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
- Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Ai Hamashima
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Sho Kubota
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Jie Bai
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Supannika Sorin
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Yuqi Sun
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Kenta Kikuchi
- Laboratory of Chromatin Organization in Immune Cell Development, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Mihoko Iimori
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Mariko Morii
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Akinori Kanai
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Atsushi Iwama
- Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Gang Huang
- Department of Cell Systems & Anatomy, Department of Pathology and Laboratory Medicine, UT Health San Antonio, Joe R. and Teresa Lozano Long School of Medicine, Mays Cancer Center at UT Health San Antonio, San Antonio, TX, USA
| | - Daisuke Kurotaki
- Laboratory of Chromatin Organization in Immune Cell Development, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Hitoshi Takizawa
- Laboratory of Stem Cell Stress, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
- Center for Metabolic Regulation of Healthy Aging, Kumamoto University, Kumamoto, Japan
| | - Hirotaka Matsui
- Department of Molecular Laboratory Medicine, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Goro Sashida
- Laboratory of Transcriptional Regulation in Leukemogenesis, International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan
| |
Collapse
|
39
|
Wang KT, Adler CE. CRISPR/Cas9-based depletion of 16S ribosomal RNA improves library complexity of single-cell RNA-sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.25.542286. [PMID: 37292639 PMCID: PMC10246003 DOI: 10.1101/2023.05.25.542286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Background Single-cell RNA-sequencing (scRNA-seq) relies on PCR amplification to retrieve information from vanishingly small amounts of starting material. To selectively enrich mRNA from abundant non-polyadenylated transcripts, poly(A) selection is a key step during library preparation. However, some transcripts, such as mitochondrial genes, can escape this elimination and overwhelm libraries. Often, these transcripts are removed in silico, but whether physical depletion improves detection of rare transcripts in single cells is unclear. Results We find that a single 16S ribosomal RNA is widely enriched in planarian scRNA-seq datasets, independent of the library preparation method. To deplete this transcript from scRNA-seq libraries, we design 30 single-guide RNAs spanning its length. To evaluate the effects of depletion, we perform a side-by-side comparison of the effects of eliminating the 16S transcript and find a substantial increase in the number of genes detected per cell, coupled with virtually complete loss of the 16S RNA. Moreover, we systematically determine that library complexity increases with a limited number of PCR cycles following CRISPR treatment. When compared to in silico depletion of 16S, physically removing it reduces dropout rates, retrieves more clusters, and reveals more differentially-expressed genes. Conclusions Our results show that abundant transcripts reduce the retrieval of informative transcripts in scRNA-seq and distort the analysis. Physical removal of these contaminants enables the detection of rare transcripts at lower sequencing depth, and also outperforms in silico depletion. Importantly, this method can be easily customized to deplete any abundant transcript from scRNA-seq libraries.
Collapse
Affiliation(s)
- Kuang-Tse Wang
- Department of Molecular Medicine, Cornell University College of Veterinary Medicine, Ithaca, NY, USA
| | - Carolyn E. Adler
- Department of Molecular Medicine, Cornell University College of Veterinary Medicine, Ithaca, NY, USA
| |
Collapse
|
40
|
Roesch E, Greener JG, MacLean AL, Nassar H, Rackauckas C, Holy TE, Stumpf MPH. Julia for biologists. Nat Methods 2023; 20:655-664. [PMID: 37024649 PMCID: PMC10216852 DOI: 10.1038/s41592-023-01832-z] [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: 09/21/2021] [Accepted: 02/27/2023] [Indexed: 04/08/2023]
Abstract
Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and more realistic models in systems biology. Here we discuss how a relative newcomer among programming languages-Julia-is poised to meet the current and emerging demands in the computational biosciences and beyond. Speed, flexibility, a thriving package ecosystem and readability are major factors that make high-performance computing and data analysis available to an unprecedented degree. We highlight how Julia's design is already enabling new ways of analyzing biological data and systems, and we provide a list of resources that can facilitate the transition into Julian computing.
Collapse
Affiliation(s)
- Elisabeth Roesch
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
- JuliaHub, Somerville, MA, USA
| | - Joe G Greener
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
| | - Adam L MacLean
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | | | - Christopher Rackauckas
- JuliaHub, Somerville, MA, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Pumas-AI, Centreville, VA, USA
| | - Timothy E Holy
- Departments of Neuroscience and Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael P H Stumpf
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia.
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia.
- School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia.
- ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems, Melbourne, Victoria, Australia.
| |
Collapse
|
41
|
Gustafsson C, Hauenstein J, Frengen N, Krstic A, Luc S, Månsson R. T-RHEX-RNAseq - a tagmentation-based, rRNA blocked, random hexamer primed RNAseq method for generating stranded RNAseq libraries directly from very low numbers of lysed cells. BMC Genomics 2023; 24:205. [PMID: 37069502 PMCID: PMC10111750 DOI: 10.1186/s12864-023-09279-4] [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: 10/25/2022] [Accepted: 03/28/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND RNA sequencing has become the mainstay for studies of gene expression. Still, analysis of rare cells with random hexamer priming - to allow analysis of a broader range of transcripts - remains challenging. RESULTS We here describe a tagmentation-based, rRNA blocked, random hexamer primed RNAseq approach (T-RHEX-RNAseq) for generating stranded RNAseq libraries from very low numbers of FACS sorted cells without RNA purification steps. CONCLUSION T-RHEX-RNAseq provides an easy-to-use, time efficient and automation compatible method for generating stranded RNAseq libraries from rare cells.
Collapse
Affiliation(s)
- Charlotte Gustafsson
- Department of Laboratory Medicine, Division of Clinical Immunology, Karolinska Institutet, ANA Futura, Alfred Nobels Allé 8 floor 7, Huddinge, SE-141 52, Sweden
| | - Julia Hauenstein
- Department of Laboratory Medicine, Division of Clinical Immunology, Karolinska Institutet, ANA Futura, Alfred Nobels Allé 8 floor 7, Huddinge, SE-141 52, Sweden
| | - Nicolai Frengen
- Department of Laboratory Medicine, Division of Clinical Immunology, Karolinska Institutet, ANA Futura, Alfred Nobels Allé 8 floor 7, Huddinge, SE-141 52, Sweden
| | - Aleksandra Krstic
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Sidinh Luc
- Center for Hematology and Regenerative Medicine (HERM), Karolinska Institutet, Stockholm, Sweden
- Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Robert Månsson
- Department of Laboratory Medicine, Division of Clinical Immunology, Karolinska Institutet, ANA Futura, Alfred Nobels Allé 8 floor 7, Huddinge, SE-141 52, Sweden.
- Department of Clinical Immunology and Transfusion Medicine, Karolinska University Hospital, Stockholm, Sweden.
| |
Collapse
|
42
|
Reagor CC, Velez-Angel N, Hudspeth AJ. Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference. PNAS NEXUS 2023; 2:pgad113. [PMID: 37113980 PMCID: PMC10129065 DOI: 10.1093/pnasnexus/pgad113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 03/21/2023] [Accepted: 03/23/2023] [Indexed: 04/29/2023]
Abstract
Identifying the causal interactions in gene-regulatory networks requires an accurate understanding of the time-lagged relationships between transcription factors and their target genes. Here we describe DELAY (short for Depicting Lagged Causality), a convolutional neural network for the inference of gene-regulatory relationships across pseudotime-ordered single-cell trajectories. We show that combining supervised deep learning with joint probability matrices of pseudotime-lagged trajectories allows the network to overcome important limitations of ordinary Granger causality-based methods, for example, the inability to infer cyclic relationships such as feedback loops. Our network outperforms several common methods for inferring gene regulation and, when given partial ground-truth labels, predicts novel regulatory networks from single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data sets. To validate this approach, we used DELAY to identify important genes and modules in the regulatory network of auditory hair cells, as well as likely DNA-binding partners for two hair cell cofactors (Hist1h1c and Ccnd1) and a novel binding sequence for the hair cell-specific transcription factor Fiz1. We provide an easy-to-use implementation of DELAY under an open-source license at https://github.com/calebclayreagor/DELAY.
Collapse
Affiliation(s)
| | - Nicolas Velez-Angel
- Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY 10065, USA
| | | |
Collapse
|
43
|
Shen B, Coruzzi G, Shasha D. EnsInfer: a simple ensemble approach to network inference outperforms any single method. BMC Bioinformatics 2023; 24:114. [PMID: 36964499 PMCID: PMC10037858 DOI: 10.1186/s12859-023-05231-1] [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: 12/12/2022] [Accepted: 03/15/2023] [Indexed: 03/26/2023] Open
Abstract
This study evaluates both a variety of existing base causal inference methods and a variety of ensemble methods. We show that: (i) base network inference methods vary in their performance across different datasets, so a method that works poorly on one dataset may work well on another; (ii) a non-homogeneous ensemble method in the form of a Naive Bayes classifier leads overall to as good or better results than using the best single base method or any other ensemble method; (iii) for the best results, the ensemble method should integrate all methods that satisfy a statistical test of normality on training data. The resulting ensemble model EnsInfer easily integrates all kinds of RNA-seq data as well as new and existing inference methods. The paper categorizes and reviews state-of-the-art underlying methods, describes the EnsInfer ensemble approach in detail, and presents experimental results. The source code and data used will be made available to the community upon publication.
Collapse
Affiliation(s)
- Bingran Shen
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, 10012 USA
| | - Gloria Coruzzi
- Department of Biology, Center for Genomics and Systems Biology, New York University, 12 Waverly Pl, New York, 10003 USA
| | - Dennis Shasha
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, 10012 USA
| |
Collapse
|
44
|
Mao G, Pang Z, Zuo K, Liu J. Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data. J Comput Biol 2023; 30:619-631. [PMID: 36877552 DOI: 10.1089/cmb.2022.0355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to construct gene expression regulatory networks. On the other hand, the noise and dropout of single-cell data bring great difficulties to the analysis of scRNA-seq data, resulting in lower accuracy of gene regulatory networks reconstructed by traditional methods. In this article, we propose a novel supervised convolutional neural network (CNNSE), which can extract gene expression information from 2D co-expression matrices of gene doublets and identify interactions between genes. Our method can avoid the loss of extreme point interference by constructing a 2D co-expression matrix of gene pairs and significantly improve the regulation precision between gene pairs. And the CNNSE model is able to obtain detailed and high-level semantic information from the 2D co-expression matrix. Our method achieves satisfactory results on simulated data [accuracy (ACC): 0.712, F1: 0.724]. On two real scRNA-seq datasets, our method exhibits higher stability and accuracy in inference tasks compared with other existing gene regulatory network inference algorithms.
Collapse
Affiliation(s)
- Guo Mao
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Zhengbin Pang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Ke Zuo
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Jie Liu
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
- Laboratory of Software Engineering for Complex System, National University of Defense Technology, Changsha, China
| |
Collapse
|
45
|
Akiyama T, Yasuda T, Uchihara T, Yasuda-Yoshihara N, Tan BJY, Yonemura A, Semba T, Yamasaki J, Komohara Y, Ohnishi K, Wei F, Fu L, Zhang J, Kitamura F, Yamashita K, Eto K, Iwagami S, Tsukamoto H, Umemoto T, Masuda M, Nagano O, Satou Y, Saya H, Tan P, Baba H, Ishimoto T. Stromal Reprogramming through Dual PDGFRα/β Blockade Boosts the Efficacy of Anti-PD-1 Immunotherapy in Fibrotic Tumors. Cancer Res 2023; 83:753-770. [PMID: 36543251 DOI: 10.1158/0008-5472.can-22-1890] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 10/11/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022]
Abstract
Excess stroma and cancer-associated fibroblasts (CAF) enhance cancer progression and facilitate immune evasion. Insights into the mechanisms by which the stroma manipulates the immune microenvironment could help improve cancer treatment. Here, we aimed to elucidate potential approaches for stromal reprogramming and improved cancer immunotherapy. Platelet-derived growth factor C (PDGFC) and D expression were significantly associated with a poor prognosis in patients with gastric cancer, and PDGF receptor beta (PDGFRβ) was predominantly expressed in diffuse-type gastric cancer stroma. CAFs stimulated with PDGFs exhibited markedly increased expression of CXCL1, CXCL3, CXCL5, and CXCL8, which are involved in polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) recruitment. Fibrotic gastric cancer xenograft tumors exhibited increased PMN-MDSC accumulation and decreased lymphocyte infiltration, as well as resistance to anti-PD-1. Single-cell RNA sequencing and spatial transcriptomics revealed that PDGFRα/β blockade reversed the immunosuppressive microenvironment through stromal modification. Finally, combining PDGFRα/β blockade and anti-PD-1 treatment synergistically suppressed the growth of fibrotic tumors. These findings highlight the impact of stromal reprogramming on immune reactivation and the potential for combined immunotherapy for patients with fibrotic cancer. SIGNIFICANCE Stromal targeting with PDGFRα/β dual blockade reverses the immunosuppressive microenvironment and enhances the efficacy of immune checkpoint inhibitors in fibrotic cancer. See related commentary by Tauriello, p. 655.
Collapse
Affiliation(s)
- Takahiko Akiyama
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Tadahito Yasuda
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Tomoyuki Uchihara
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Noriko Yasuda-Yoshihara
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Benjy J Y Tan
- Division of Genomics and Transcriptomics, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Kumamoto, Japan
| | - Atsuko Yonemura
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Takashi Semba
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Juntaro Yamasaki
- Division of Gene Regulation, Institute for Advanced Medical Research, School of Medicine, Keio University, Tokyo, Japan
| | | | - Koji Ohnishi
- Department of Pathology, Aichi Medical University School of Medicine, Nagakute, Japan
| | - Feng Wei
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Lingfeng Fu
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Jun Zhang
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Fumimasa Kitamura
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Kohei Yamashita
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Kojiro Eto
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Shiro Iwagami
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Hirotake Tsukamoto
- Division of Clinical Immunology and Cancer Immunotherapy, Center for Cancer Immunotherapy and Immunobiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Terumasa Umemoto
- Laboratory of Hematopoietic Stem Cell Engineering, International Research Center for Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Mari Masuda
- Department of Proteomics, National Cancer Center Research Institute, Tokyo, Japan
| | - Osamu Nagano
- Division of Gene Regulation, Institute for Advanced Medical Research, School of Medicine, Keio University, Tokyo, Japan
| | - Yorifumi Satou
- Division of Genomics and Transcriptomics, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Kumamoto, Japan
| | - Hideyuki Saya
- Division of Gene Regulation, Institute for Advanced Medical Research, School of Medicine, Keio University, Tokyo, Japan.,Division of Gene Regulation, Cancer Center, Fujita Health University, Toyoake, Japan
| | - Patrick Tan
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Center for Metabolic Regulation of Healthy Aging, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Takatsugu Ishimoto
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.,Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| |
Collapse
|
46
|
Ma R, Sun ED, Zou J. A spectral method for assessing and combining multiple data visualizations. Nat Commun 2023; 14:780. [PMID: 36774377 PMCID: PMC9922271 DOI: 10.1038/s41467-023-36492-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/03/2023] [Indexed: 02/13/2023] Open
Abstract
Dimension reduction is an indispensable part of modern data science, and many algorithms have been developed. However, different algorithms have their own strengths and weaknesses, making it important to evaluate their relative performance, and to leverage and combine their individual strengths. This paper proposes a spectral method for assessing and combining multiple visualizations of a given dataset produced by diverse algorithms. The proposed method provides a quantitative measure - the visualization eigenscore - of the relative performance of the visualizations for preserving the structure around each data point. It also generates a consensus visualization, having improved quality over individual visualizations in capturing the underlying structure. Our approach is flexible and works as a wrapper around any visualizations. We analyze multiple real-world datasets to demonstrate the effectiveness of the method. We also provide theoretical justifications based on a general statistical framework, yielding several fundamental principles along with practical guidance.
Collapse
Affiliation(s)
- Rong Ma
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Eric D Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| |
Collapse
|
47
|
Zhai J, Ji H, Jiang H. Reconstruction of Single-Cell Trajectories Using Stochastic Tree Search. Genes (Basel) 2023; 14:318. [PMID: 36833245 PMCID: PMC9957497 DOI: 10.3390/genes14020318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/18/2023] [Accepted: 01/21/2023] [Indexed: 01/28/2023] Open
Abstract
The recent advancement in single-cell RNA sequencing technologies enables the understanding of dynamic cellular processes at the single-cell level. Using trajectory inference methods, pseudotimes can be estimated based on reconstructed single-cell trajectories which can be further used to gain biological knowledge. Existing methods for modeling cell trajectories, such as minimal spanning tree or k-nearest neighbor graph, often lead to locally optimal solutions. In this paper, we propose a penalized likelihood-based framework and introduce a stochastic tree search (STS) algorithm aiming at the global solution in a large and non-convex tree space. Both simulated and real data experiments show that our approach is more accurate and robust than other existing methods in terms of cell ordering and pseudotime estimation.
Collapse
Affiliation(s)
- Jingyi Zhai
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Hui Jiang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
48
|
Loganathan T, Doss C GP. Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets. Funct Integr Genomics 2023; 23:33. [PMID: 36625940 PMCID: PMC9838419 DOI: 10.1007/s10142-022-00947-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023]
Abstract
Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.
Collapse
Affiliation(s)
- Tamizhini Loganathan
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore- 632014, Tamil Nadu, India
| | - George Priya Doss C
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore- 632014, Tamil Nadu, India.
| |
Collapse
|
49
|
Matsumoto M, Yoshida H, Tsuneyama K, Oya T, Matsumoto M. Revisiting Aire and tissue-restricted antigens at single-cell resolution. Front Immunol 2023; 14:1176450. [PMID: 37207224 PMCID: PMC10191227 DOI: 10.3389/fimmu.2023.1176450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/20/2023] [Indexed: 05/21/2023] Open
Abstract
The thymus is a highly specialized organ that plays an indispensable role in the establishment of self-tolerance, a process characterized by the "education" of developing T-cells. To provide competent T-cells tolerant to self-antigens, medullary thymic epithelial cells (mTECs) orchestrate negative selection by ectopically expressing a wide range of genes, including various tissue-restricted antigens (TRAs). Notably, recent advancements in the high-throughput single-cell analysis have revealed remarkable heterogeneity in mTECs, giving us important clues for dissecting the mechanisms underlying TRA expression. We overview how recent single-cell studies have furthered our understanding of mTECs, with a focus on the role of Aire in inducing mTEC heterogeneity to encompass TRAs.
Collapse
Affiliation(s)
- Minoru Matsumoto
- Department of Molecular Pathology, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
- Division of Molecular Immunology, Institute for Enzyme Research, Tokushima University, Tokushima, Japan
- *Correspondence: Minoru Matsumoto,
| | - Hideyuki Yoshida
- YCI Laboratory for Immunological Transcriptomics, RIKEN Center for Integrative Medical Science, Yokohama, Japan
| | - Koichi Tsuneyama
- Department of Pathology and Laboratory Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
| | - Takeshi Oya
- Department of Molecular Pathology, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
| | - Mitsuru Matsumoto
- Division of Molecular Immunology, Institute for Enzyme Research, Tokushima University, Tokushima, Japan
| |
Collapse
|
50
|
Suzuki T. Overview of single-cell RNA sequencing analysis and its application to spermatogenesis research. Reprod Med Biol 2023; 22:e12502. [PMID: 36726594 PMCID: PMC9884325 DOI: 10.1002/rmb2.12502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/18/2022] [Accepted: 01/10/2023] [Indexed: 01/30/2023] Open
Abstract
Background Single-cell transcriptomics allows parallel analysis of multiple cell types in tissues. Because testes comprise somatic cells and germ cells at various stages of spermatogenesis, single-cell RNA sequencing is a powerful tool for investigating the complex process of spermatogenesis. However, single-cell RNA sequencing analysis needs extensive knowledge of experimental technologies and bioinformatics, making it difficult for many, particularly experimental biologists and clinicians, to use it. Methods Aiming to make single-cell RNA sequencing analysis familiar, this review article presents an overview of experimental and computational methods for single-cell RNA sequencing analysis with a history of transcriptomics. In addition, combining the PubMed search and manual curation, this review also provides a summary of recent novel insights into human and mouse spermatogenesis obtained using single-cell RNA sequencing analyses. Main Findings Single-cell RNA sequencing identified mesenchymal cells and type II innate lymphoid cells as novel testicular cell types in the adult mouse testes, as well as detailed subtypes of germ cells. This review outlines recent discoveries into germ cell development and subtypes, somatic cell development, and cell-cell interactions. Conclusion The findings on spermatogenesis obtained using single-cell RNA sequencing may contribute to a deeper understanding of spermatogenesis and provide new directions for male fertility therapy.
Collapse
Affiliation(s)
- Takahiro Suzuki
- RIKEN Center for Integrated Medical Science (IMS)Yokohama CityKanagawaJapan
- Graduate School of Medical Life ScienceYokohama City UniversityYokohama CityKanagawaJapan
| |
Collapse
|