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Ding J, Liu R, Wen H, Tang W, Li Z, Venegas J, Su R, Molho D, Jin W, Wang Y, Lu Q, Li L, Zuo W, Chang Y, Xie Y, Tang J. DANCE: a deep learning library and benchmark platform for single-cell analysis. Genome Biol 2024; 25:72. [PMID: 38504331 PMCID: PMC10949782 DOI: 10.1186/s13059-024-03211-z] [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: 03/05/2024] [Indexed: 03/21/2024] Open
Abstract
DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.
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Affiliation(s)
- Jiayuan Ding
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Hongzhi Wen
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA
| | - Wenzhuo Tang
- Department of Statistics and Probability, Michigan State University, East Lansing, USA
| | - Zhaoheng Li
- Department of Biostatistics, University of Washington, Seattle, USA
| | - Julian Venegas
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Runze Su
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
- Department of Statistics and Probability, Michigan State University, East Lansing, USA
| | - Dylan Molho
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA
| | - Wei Jin
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, USA
| | - Qiaolin Lu
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Lingxiao Li
- Department of Computer Science, Boston University, Boston, USA
| | - Wangyang Zuo
- Department of Computer Science, Zhejiang University of Technology, Zhejiang, China
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Yuying Xie
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
- Department of Statistics and Probability, Michigan State University, East Lansing, USA.
| | - Jiliang Tang
- Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
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Li R, Chen X, Yang X. Navigating the landscapes of spatial transcriptomics: How computational methods guide the way. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1839. [PMID: 38527900 DOI: 10.1002/wrna.1839] [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: 11/23/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
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Affiliation(s)
- Runze Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xu Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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Piran Z, Nitzan M. SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data. Nat Commun 2024; 15:760. [PMID: 38278815 PMCID: PMC10817921 DOI: 10.1038/s41467-024-44757-7] [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: 10/23/2023] [Accepted: 01/03/2024] [Indexed: 01/28/2024] Open
Abstract
Cellular populations simultaneously encode multiple biological attributes, including spatial configuration, temporal trajectories, and cell-cell interactions. Some of these signals may be overshadowed by others and harder to recover, despite the great progress made to computationally reconstruct biological processes from single-cell data. To address this, we present SiFT, a kernel-based projection method for filtering biological signals in single-cell data, thus uncovering underlying biological processes. SiFT applies to a wide range of tasks, from the removal of unwanted variation in the data to revealing hidden biological structures. We demonstrate how SiFT enhances the liver circadian signal by filtering spatial zonation, recovers regenerative cell subpopulations in spatially-resolved liver data, and exposes COVID-19 disease-related cells, pathways, and dynamics by filtering healthy reference signals. SiFT performs the correction at the gene expression level, can scale to large datasets, and compares favorably to state-of-the-art methods.
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Affiliation(s)
- Zoe Piran
- School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel.
- Racah Institute of Physics, The Hebrew University, Jerusalem, Israel.
- Faculty of Medicine, The Hebrew University, Jerusalem, Israel.
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Amini P, Hajihosseini M, Pyne S, Dinu I. Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity. Front Cell Dev Biol 2023; 11:1065586. [PMID: 36998245 PMCID: PMC10044624 DOI: 10.3389/fcell.2023.1065586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample.Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios.Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases.Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.
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Affiliation(s)
- Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- School of Medicine, Keele University, Keele, Staffordshire, United Kingdom
| | - Morteza Hajihosseini
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- Stanford Department of Urology, Center for Academic Medicine, Palo Alto, CA, United States
| | - Saumyadipta Pyne
- Health Analytics Network, Pittsburgh, PA, United States
- University of California, Santa Barbara, Santa Barbara, CA, United States
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
| | - Irina Dinu
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
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Banerjee TD, Tian S, Monteiro A. Laser Microdissection-Mediated Isolation of Butterfly Wing Tissue for Spatial Transcriptomics. Methods Protoc 2022; 5:mps5040067. [PMID: 36005768 PMCID: PMC9415384 DOI: 10.3390/mps5040067] [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: 07/16/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
The assignment of specific patterns of gene expression to specific cells in a complex tissue facilitates the connection between genotype and phenotype. Single-cell sequencing of whole tissues produces single-cell transcript resolution but lacks the spatial information of the derivation of each cell, whereas techniques such as multiplex FISH localize transcripts to specific cells in a tissue but require a priori information of the target transcripts to examine. Laser dissection of tissues followed by transcriptome analysis is an efficient and cost-effective technique that provides both unbiased gene expression discovery together with spatial information. Here, we detail a laser dissection protocol for total RNA extraction from butterfly larval and pupal wing tissues, without the need of paraffin embedding or the use of a microtome, that could be useful to researchers interested in the transcriptome of specific areas of the wing during development. This protocol can bypass difficulties in extracting high quality RNA from thick fixed tissues for sequencing applications.
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Affiliation(s)
- Tirtha Das Banerjee
- Department of Biological Sciences, National University of Singapore, Singapore 117557, Singapore
- Correspondence: (T.D.B.); (A.M.)
| | - Shen Tian
- Department of Biological Sciences, National University of Singapore, Singapore 117557, Singapore
| | - Antόnia Monteiro
- Department of Biological Sciences, National University of Singapore, Singapore 117557, Singapore
- Science Division, Yale-NUS College, National University of Singapore, Singapore 138609, Singapore
- Correspondence: (T.D.B.); (A.M.)
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Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics. Cancers (Basel) 2022; 14:cancers14071702. [PMID: 35406474 PMCID: PMC8997139 DOI: 10.3390/cancers14071702] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/11/2022] [Accepted: 03/21/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer. Abstract Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.
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Gong E, Perin L, Da Sacco S, Sedrakyan S. Emerging Technologies to Study the Glomerular Filtration Barrier. Front Med (Lausanne) 2021; 8:772883. [PMID: 34901088 PMCID: PMC8655839 DOI: 10.3389/fmed.2021.772883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/04/2021] [Indexed: 11/16/2022] Open
Abstract
Kidney disease is characterized by loss of glomerular function with clinical manifestation of proteinuria. Identifying the cellular and molecular changes that lead to loss of protein in the urine is challenging due to the complexity of the filtration barrier, constituted by podocytes, glomerular endothelial cells, and glomerular basement membrane. In this review, we will discuss how technologies like single cell RNA sequencing and bioinformatics-based spatial transcriptomics, as well as in vitro systems like kidney organoids and the glomerulus-on-a-chip, have contributed to our understanding of glomerular pathophysiology. Knowledge gained from these studies will contribute toward the development of personalized therapeutic approaches for patients affected by proteinuric diseases.
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Affiliation(s)
- Emma Gong
- Division of Urology, GOFARR Laboratory for Organ Regenerative Research and Cell Therapeutics, Children's Hospital Los Angeles, Saban Research Institute, Los Angeles, CA, United States
| | - Laura Perin
- Division of Urology, GOFARR Laboratory for Organ Regenerative Research and Cell Therapeutics, Children's Hospital Los Angeles, Saban Research Institute, Los Angeles, CA, United States.,Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Stefano Da Sacco
- Division of Urology, GOFARR Laboratory for Organ Regenerative Research and Cell Therapeutics, Children's Hospital Los Angeles, Saban Research Institute, Los Angeles, CA, United States.,Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sargis Sedrakyan
- Division of Urology, GOFARR Laboratory for Organ Regenerative Research and Cell Therapeutics, Children's Hospital Los Angeles, Saban Research Institute, Los Angeles, CA, United States.,Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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Moriel N, Senel E, Friedman N, Rajewsky N, Karaiskos N, Nitzan M. NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport. Nat Protoc 2021; 16:4177-4200. [PMID: 34349282 DOI: 10.1038/s41596-021-00573-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 05/17/2021] [Indexed: 11/09/2022]
Abstract
Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detailed protocol for using novoSpaRc, a computational framework that probabilistically assigns cells to tissue locations. At the core of this framework lies a structural correspondence hypothesis, that cells in physical proximity share similar gene expression profiles. Given scRNA-seq data, novoSpaRc spatially reconstructs tissues based on this hypothesis, and optionally, by including a reference atlas of marker genes to improve reconstruction. We describe the novoSpaRc algorithm, and its implementation in an open-source Python package ( https://pypi.org/project/novosparc ). NovoSpaRc maps a scRNA-seq dataset of 10,000 cells onto 1,000 locations in <5 min. We describe results obtained using novoSpaRc to reconstruct the mouse organ of Corti de novo based on the structural correspondence assumption and human osteosarcoma cultured cells based on marker gene information, and provide a step-by-step guide to Drosophila embryo reconstruction in the Procedure to demonstrate how these two strategies can be combined.
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Affiliation(s)
- Noa Moriel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Enes Senel
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Nir Friedman
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.,Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nikolaus Rajewsky
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Nikos Karaiskos
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. .,Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel. .,Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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Heo YJ, Hwa C, Lee GH, Park JM, An JY. Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes. Mol Cells 2021; 44:433-443. [PMID: 34238766 PMCID: PMC8334347 DOI: 10.14348/molcells.2021.0042] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/09/2021] [Accepted: 05/12/2021] [Indexed: 11/27/2022] Open
Abstract
Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.
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Affiliation(s)
- Yong Jin Heo
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
- Department of Integrated Biomedical and Life Science, Korea University, Seoul 02841, Korea
| | - Chanwoong Hwa
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
| | - Gang-Hee Lee
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
| | - Jae-Min Park
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
| | - Joon-Yong An
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
- Department of Integrated Biomedical and Life Science, Korea University, Seoul 02841, Korea
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Ramesh P, Shivde R, Jaishankar D, Saleiro D, Le Poole IC. A Palette of Cytokines to Measure Anti-Tumor Efficacy of T Cell-Based Therapeutics. Cancers (Basel) 2021; 13:821. [PMID: 33669271 PMCID: PMC7920025 DOI: 10.3390/cancers13040821] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/08/2021] [Accepted: 02/10/2021] [Indexed: 12/12/2022] Open
Abstract
Cytokines are key molecules within the tumor microenvironment (TME) that can be used as biomarkers to predict the magnitude of anti-tumor immune responses. During immune monitoring, it has been customary to predict outcomes based on the abundance of a single cytokine, in particular IFN-γ or TGF-β, as a readout of ongoing anti-cancer immunity. However, individual cytokines within the TME can exhibit dual opposing roles. For example, both IFN-γ and TGF-β have been associated with pro- and anti-tumor functions. Moreover, cytokines originating from different cellular sources influence the crosstalk between CD4+ and CD8+ T cells, while the array of cytokines expressed by T cells is also instrumental in defining the mechanisms of action and efficacy of treatments. Thus, it becomes increasingly clear that a reliable readout of ongoing immunity within the TME will have to include more than the measurement of a single cytokine. This review focuses on defining a panel of cytokines that could help to reliably predict and analyze the outcomes of T cell-based anti-tumor therapies.
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Affiliation(s)
- Prathyaya Ramesh
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA; (P.R.); (R.S.); (D.J.); (D.S.)
- Department of Dermatology, Northwestern University, Chicago, IL 60611, USA
| | - Rohan Shivde
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA; (P.R.); (R.S.); (D.J.); (D.S.)
- Department of Dermatology, Northwestern University, Chicago, IL 60611, USA
| | - Dinesh Jaishankar
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA; (P.R.); (R.S.); (D.J.); (D.S.)
- Department of Dermatology, Northwestern University, Chicago, IL 60611, USA
| | - Diana Saleiro
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA; (P.R.); (R.S.); (D.J.); (D.S.)
- Division of Hematology-Oncology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - I. Caroline Le Poole
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA; (P.R.); (R.S.); (D.J.); (D.S.)
- Department of Dermatology, Northwestern University, Chicago, IL 60611, USA
- Department of Microbiology and Immunology, Northwestern University at Chicago, Chicago, IL 60611, USA
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Wu D, Liu X, Zhang J, Li L, Wang X. Significance of single-cell and spatial transcriptomes in cell biology and toxicology. Cell Biol Toxicol 2021; 37:1-5. [PMID: 33398619 DOI: 10.1007/s10565-020-09576-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 12/01/2020] [Indexed: 02/07/2023]
Affiliation(s)
- Duojiao Wu
- Zhongshan Hospital Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Zhongshan Hospital, Fudan University, Shanghai, China; Jinshan Hospital Centre for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, China
| | - Xiaozhuan Liu
- Clinical Center for Single Cell Biomedicine, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiaqiang Zhang
- Clinical Center for Single Cell Biomedicine, People's Hospital of Zhengzhou University, Zhengzhou, China. .,Department of Anesthesiology and Perioperative Medicine, Henan Province People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.
| | - Li Li
- Clinical Center for Single Cell Biomedicine, People's Hospital of Zhengzhou University, Zhengzhou, China.
| | - Xiangdong Wang
- Zhongshan Hospital Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Zhongshan Hospital, Fudan University, Shanghai, China; Jinshan Hospital Centre for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, China.
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