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Cells
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Single-Cell Multi-Omics and Its Applications in Cancer Research
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Single-Cell Multi-Omics and Its Applications in Cancer Research

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cellular Biophysics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 16202

Special Issue Editors

Dr. Huihui Fan

E-Mail Website
Guest Editor
UT Health Science Center at Houston, Center for Precision Health, School of Biomedical Informatics, 7000 Fannin St. E755C, Houston, TX 77030, USA
Interests: large-scale multi-omics studies and artificial intelligence applications in human cancers; cancer-associated microenvironment at both single-cell and bulk level
Dr. Fulong Yu

E-Mail Website
Guest Editor
Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
Interests: single-cell genomics; functional genetics; bioinformatics; machine learning; precision medicine
Dr. Desi Shang

E-Mail Website
Guest Editor
The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
Interests: construction and analysis of biological network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Single-cell sequencing technologies, aiming to reveal cellular dynamics at true biological resolution, have substantially advanced our understanding of inter- and intra-heterogeneity as well as functional diversity among cell types and/or individual cells in human cancers. Single-cell multi-omics studies bring enormous opportunities for biology and cancer research. In precision medicine in particular, prior knowledge has been rapidly updated by studying cell subpopulations in response to drug treatments or external perturbances (e.g., SARS-CoV-2). Concurrent with single-cell multi-omics studies, deep learning has proved feasible to tackle the high-dimensional data yielded by single-cell profiling, and to improve signal-to-noise ratios while handling tasks like imputation, batch correction and clustering. Despite the most recent developments in single-cell multi-omics technologies and deep learning applications in cancer research, more exciting challenges and possible new perspectives remain to be explored.

This Special Issue will be devoted to single-cell multi-omics research in human cancers by incorporating the following studies: (1) integrative approaches of single-cell multi-omics data; (2) deep learning methodologies using single-cell profiling; and (3) drug discovery and repurposing using single-cell multi-omics data. 

Dr. Huihui Fan
Dr. Fulong Yu
Dr. Desi Shang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cells is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • single-cell RNA sequencing
  • single-cell sequencing for transposase-accessible chromatin
  • deep learning methodologies using single-cell omics data
  • integration methodologies of single-cell multi-omics data
  • drug discovery and repurpose using single-cell omics data
  • cellular heterogeneity in human cancers

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Published Papers (5 papers)

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Research

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12 pages, 11871 KiB  
Communication
scViewer: An Interactive Single-Cell Gene Expression Visualization Tool
by Abhijeet R. Patil, Gaurav Kumar, Huanyu Zhou and Liling Warren
Cells 2023, 12(11), 1489; https://doi.org/10.3390/cells12111489 - 27 May 2023
Cited by 3 | Viewed by 3174
Abstract
Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now [...] Read more.
Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer’s disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations. Full article
(This article belongs to the Special Issue Single-Cell Multi-Omics and Its Applications in Cancer Research)
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18 pages, 12535 KiB  
Communication
Single-Cell Transcriptome Analysis Revealed Heterogeneity and Identified Novel Therapeutic Targets for Breast Cancer Subtypes
by Radhakrishnan Vishnubalaji and Nehad M. Alajez
Cells 2023, 12(8), 1182; https://doi.org/10.3390/cells12081182 - 18 Apr 2023
Cited by 8 | Viewed by 2978
Abstract
Breast cancer (BC) is a heterogeneous disease, which is primarily classified according to hormone receptors and HER2 expression. Despite the many advances in BC diagnosis and management, the identification of novel actionable therapeutic targets expressed by cancerous cells has always been a daunting [...] Read more.
Breast cancer (BC) is a heterogeneous disease, which is primarily classified according to hormone receptors and HER2 expression. Despite the many advances in BC diagnosis and management, the identification of novel actionable therapeutic targets expressed by cancerous cells has always been a daunting task due to the large heterogeneity of the disease and the presence of non-cancerous cells (i.e., immune cells and stromal cells) within the tumor microenvironment. In the current study, we employed computational algorithms to decipher the cellular composition of estrogen receptor-positive (ER+), HER2+, ER+HER2+, and triple-negative BC (TNBC) subtypes from a total of 49,899 single cells’ publicly available transcriptomic data derived from 26 BC patients. Restricting the analysis to EPCAM+Lin− tumor epithelial cells, we identified the enriched gene sets in each BC molecular subtype. Integration of single-cell transcriptomic with CRISPR-Cas9 functional screen data identified 13 potential therapeutic targets for ER+, 44 potential therapeutic targets for HER2+, and 29 potential therapeutic targets for TNBC. Interestingly, several of the identified therapeutic targets outperformed the current standard of care for each BC subtype. Given the aggressive nature and lack of targeted therapies for TNBC, elevated expression of ENO1, FDPS, CCT6A, TUBB2A, and PGK1 predicted worse relapse-free survival (RFS) in basal BC (n = 442), while elevated expression of ENO1, FDPS, CCT6A, and PGK1 was observed in the most aggressive BLIS TNBC subtype. Mechanistically, targeted depletion of ENO1 and FDPS halted TNBC cell proliferation, colony formation, and organoid tumor growth under 3-dimensional conditions and increased cell death, suggesting their potential use as novel therapeutic targets for TNBC. Differential expression and gene set enrichment analysis in TNBC revealed enrichment in the cycle and mitosis functional categories in FDPShigh, while ENO1high was associated with numerous functional categories, including cell cycle, glycolysis, and ATP metabolic processes. Taken together, our data are the first to unravel the unique gene signatures and to identify novel dependencies and therapeutic vulnerabilities for each BC molecular subtype, thus setting the foundation for the future development of more effective targeted therapies for BC. Full article
(This article belongs to the Special Issue Single-Cell Multi-Omics and Its Applications in Cancer Research)
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15 pages, 2758 KiB  
Article
Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data
by Kaiyuan Xing, Bo Zhang, Zixuan Wang, Yanru Zhang, Tengyue Chai, Jingkai Geng, Xuexue Qin, Xi Steven Chen, Xinxin Zhang and Chaohan Xu
Cells 2023, 12(3), 367; https://doi.org/10.3390/cells12030367 - 19 Jan 2023
Cited by 5 | Viewed by 3340
Abstract
Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease [...] Read more.
Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future. Full article
(This article belongs to the Special Issue Single-Cell Multi-Omics and Its Applications in Cancer Research)
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Review

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13 pages, 876 KiB  
Review
Revealing the Heterogeneity of the Tumor Ecosystem of Cholangiocarcinoma through Single-Cell Transcriptomics
by Jihye L. Golino, Xin Wang, Hoyoung M. Maeng and Changqing Xie
Cells 2023, 12(6), 862; https://doi.org/10.3390/cells12060862 - 10 Mar 2023
Cited by 4 | Viewed by 2431
Abstract
The prognosis of cholangiocarcinoma remains poor. The heterogeneity of the tumor ecosystem of cholangiocarcinoma plays a critical role in tumorigenesis and therapeutic resistance, thereby affecting the clinical outcome of patients with cholangiocarcinoma. Recent progress in single-cell RNA sequencing (scRNA-seq) has enabled detailed characterization [...] Read more.
The prognosis of cholangiocarcinoma remains poor. The heterogeneity of the tumor ecosystem of cholangiocarcinoma plays a critical role in tumorigenesis and therapeutic resistance, thereby affecting the clinical outcome of patients with cholangiocarcinoma. Recent progress in single-cell RNA sequencing (scRNA-seq) has enabled detailed characterization of intratumoral stromal and malignant cells, which has vastly improved our understanding of the heterogeneity of various cell components in the tumor ecosystem of cholangiocarcinoma. It also provides an unprecedented view of the phenotypical and functional diversity in tumor and stromal cells including infiltrating immune cells. This review focuses on examining tumor heterogeneity and the interaction between various cellular components in the tumor ecosystem of cholangiocarcinoma derived from an scRNA-seq dataset, discussing limitations in current studies, and proposing future directions along with potential clinical applications. Full article
(This article belongs to the Special Issue Single-Cell Multi-Omics and Its Applications in Cancer Research)
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33 pages, 3914 KiB  
Review
Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets
by Qing Ye and Nancy Lan Guo
Cells 2023, 12(1), 101; https://doi.org/10.3390/cells12010101 - 26 Dec 2022
Viewed by 2845
Abstract
There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead [...] Read more.
There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes. Full article
(This article belongs to the Special Issue Single-Cell Multi-Omics and Its Applications in Cancer Research)
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