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Liu T, Wang Z, Xue X, Wang Z, Zhang Y, Mi Z, Zhao Q, Sun L, Wang C, Shi P, Yu G, Wang M, Sun Y, Xue F, Liu H, Zhang F. Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease. Nat Commun 2024; 15:5949. [PMID: 39009587 PMCID: PMC11251189 DOI: 10.1038/s41467-024-50283-3] [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: 11/30/2023] [Accepted: 07/06/2024] [Indexed: 07/17/2024] Open
Abstract
Bullous pemphigoid (BP) is a type 2 inflammation- and immunity-driven skin disease, yet a comprehensive understanding of the immune landscape, particularly immune-stromal crosstalk in BP, remains elusive. Herein, using single-cell RNA sequencing (scRNA-seq) and in vitro functional analyzes, we pinpoint Th2 cells, dendritic cells (DCs), and fibroblasts as crucial cell populations. The IL13-IL13RA1 ligand-receptor pair is identified as the most significant mediator of immune-stromal crosstalk in BP. Notably, fibroblasts and DCs expressing IL13RA1 respond to IL13-secreting Th2 cells, thereby amplifying Th2 cell-mediated cascade responses, which occurs through the specific upregulation of PLA2G2A in fibroblasts and CCL17 in myeloid cells, creating a positive feedback loop integral to immune-stromal crosstalk. Furthermore, PLA2G2A and CCL17 contribute to an increased titer of pathogenic anti-BP180-NC16A autoantibodies in BP patients. Our work provides a comprehensive insight into BP pathogenesis and shows a mechanism governing immune-stromal interactions, providing potential avenues for future therapeutic research.
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Affiliation(s)
- Tingting Liu
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Zhenzhen Wang
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Xiaotong Xue
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Zhe Wang
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yuan Zhang
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Zihao Mi
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Qing Zhao
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Lele Sun
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Chuan Wang
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Peidian Shi
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Gongqi Yu
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Meng Wang
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yonghu Sun
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Hong Liu
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China.
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China.
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
| | - Furen Zhang
- Hospital for Skin Diseases, Shandong First Medical University, Jinan, Shandong, China.
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China.
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
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2
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Arigoni M, Ratto ML, Riccardo F, Balmas E, Calogero L, Cordero F, Beccuti M, Calogero RA, Alessandri L. A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity. Sci Data 2024; 11:159. [PMID: 38307867 PMCID: PMC10837414 DOI: 10.1038/s41597-024-03002-y] [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: 10/27/2023] [Accepted: 01/25/2024] [Indexed: 02/04/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.
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Affiliation(s)
- Maddalena Arigoni
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Maria Luisa Ratto
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Federica Riccardo
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Elisa Balmas
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Lorenzo Calogero
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, Torino, Italy
| | | | - Marco Beccuti
- Department of Computer Science, University of Torino, Torino, Italy
| | - Raffaele A Calogero
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.
| | - Luca Alessandri
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
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3
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Salemme V, Vedelago M, Sarcinella A, Moietta F, Piccolantonio A, Moiso E, Centonze G, Manco M, Guala A, Lamolinara A, Angelini C, Morellato A, Natalini D, Calogero R, Incarnato D, Oliviero S, Conti L, Iezzi M, Tosoni D, Bertalot G, Freddi S, Tucci FA, De Sanctis F, Frusteri C, Ugel S, Bronte V, Cavallo F, Provero P, Gai M, Taverna D, Turco E, Pece S, Defilippi P. p140Cap inhibits β-Catenin in the breast cancer stem cell compartment instructing a protective anti-tumor immune response. Nat Commun 2023; 14:2350. [PMID: 37169737 PMCID: PMC10175288 DOI: 10.1038/s41467-023-37824-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/03/2023] [Indexed: 05/13/2023] Open
Abstract
The p140Cap adaptor protein is a tumor suppressor in breast cancer associated with a favorable prognosis. Here we highlight a function of p140Cap in orchestrating local and systemic tumor-extrinsic events that eventually result in inhibition of the polymorphonuclear myeloid-derived suppressor cell function in creating an immunosuppressive tumor-promoting environment in the primary tumor, and premetastatic niches at distant sites. Integrative transcriptomic and preclinical studies unravel that p140Cap controls an epistatic axis where, through the upstream inhibition of β-Catenin, it restricts tumorigenicity and self-renewal of tumor-initiating cells limiting the release of the inflammatory cytokine G-CSF, required for polymorphonuclear myeloid-derived suppressor cells to exert their local and systemic tumor conducive function. Mechanistically, p140Cap inhibition of β-Catenin depends on its ability to localize in and stabilize the β-Catenin destruction complex, promoting enhanced β-Catenin inactivation. Clinical studies in women show that low p140Cap expression correlates with reduced presence of tumor-infiltrating lymphocytes and more aggressive tumor types in a large cohort of real-life female breast cancer patients, highlighting the potential of p140Cap as a biomarker for therapeutic intervention targeting the β-Catenin/ Tumor-initiating cells /G-CSF/ polymorphonuclear myeloid-derived suppressor cell axis to restore an efficient anti-tumor immune response.
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Affiliation(s)
- Vincenzo Salemme
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
| | - Mauro Vedelago
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Alessandro Sarcinella
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Federico Moietta
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Alessio Piccolantonio
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
| | - Enrico Moiso
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Giorgia Centonze
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
| | - Marta Manco
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Andrea Guala
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Alessia Lamolinara
- Immuno-Oncology Laboratory, Center for Advanced Studies and Technology (CAST), Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti-Pescara, Italy
| | - Costanza Angelini
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Alessandro Morellato
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
| | - Dora Natalini
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Raffaele Calogero
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
| | - Danny Incarnato
- Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, the Netherlands
| | - Salvatore Oliviero
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
- Department of Life Sciences and Systems Biology, University of Turin, Torino, Italy and IIGM, Candiolo, Italy
| | - Laura Conti
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
| | - Manuela Iezzi
- Immuno-Oncology Laboratory, Center for Advanced Studies and Technology (CAST), Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti-Pescara, Italy
| | - Daniela Tosoni
- European Institute of Oncology IRCCS, 20141, Milan, Italy
| | | | - Stefano Freddi
- European Institute of Oncology IRCCS, 20141, Milan, Italy
| | - Francesco A Tucci
- European Institute of Oncology IRCCS, 20141, Milan, Italy
- School of Pathology, University of Milan, Milan, Italy
| | - Francesco De Sanctis
- Immunology Section, Department of Medicine, University of Verona, 37134, Verona, Italy
| | - Cristina Frusteri
- Immunology Section, Department of Medicine, University of Verona, 37134, Verona, Italy
| | - Stefano Ugel
- Immunology Section, Department of Medicine, University of Verona, 37134, Verona, Italy
| | - Vincenzo Bronte
- Immunology Section, Department of Medicine, University of Verona, 37134, Verona, Italy
- Istituto Oncologico Veneto, IRCCS, 35128, Padova, Italy
| | - Federica Cavallo
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
| | - Paolo Provero
- Neuroscience Department "Rita Levi Montalcini", University of Torino, Via Cherasco 15, 10126, Torino, Italy
| | - Marta Gai
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Daniela Taverna
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy
| | - Emilia Turco
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Salvatore Pece
- European Institute of Oncology IRCCS, 20141, Milan, Italy.
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20142, Milano, Italy.
| | - Paola Defilippi
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy.
- Molecular Biotechnology Center (MBC) "Guido Tarone", Via Nizza, 52, 10126, Turin, Italy.
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4
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Single-Cell RNAseq Complexity Reduction. Methods Mol Biol 2022; 2584:217-230. [PMID: 36495452 DOI: 10.1007/978-1-0716-2756-3_10] [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: 12/13/2022]
Abstract
An important step in single-cell RNAseq data analysis is the preparation of the single cell transcription data for cell sub-population partitioning. In this chapter, we describe how to perform complexity reduction for 3' end single-cell RNAseq transcriptomics data.
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5
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Abstract
rCASC is a modular workflow providing an integrated environment for single-cell RNA-seq (scRNA-Seq) data analysis exploiting Docker containers to achieve functional and computational reproducibility. It was initially developed as an R package usable also through a Java GUI. However, the Java frontend cannot be employed when running rCASC on a remote server, a typical setup due to the significant computational resources commonly needed to analyze scRNA-Seq data.To allow the use of rCASC through a graphical user interface on the client side and to harness the many advantages provided by the Galaxy platform, we have made rCASC available as a Galaxy set of tools, also providing a dedicated public instance of Galaxy named "Galaxy-rCASC." To integrate rCASC into Galaxy, all its functions, originally implemented as a set of Docker containers to maximize reproducibility, have been extensively reworked to become independent from the R package functions that launch them in the original implementation. Furthermore, suitable Galaxy wrappers have been developed for most functions of rCASC. We provide a detailed reference document to the use of Galaxy-rCASC with insights and explanations on the platform functionalities, parameters, and output while guiding the reader through the typical rCASC analysis workflow of a scRNA-Seq dataset.
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6
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Danielski K. Guidance on Processing the 10x Genomics Single Cell Gene Expression Assay. Methods Mol Biol 2022; 2584:1-28. [PMID: 36495443 DOI: 10.1007/978-1-0716-2756-3_1] [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: 12/13/2022]
Abstract
The demand for technologies that allow the study of gene expression at single cell resolution continues to increase. One such assay was launched in 2016 by the US-based company 10x Genomics Inc. Utilizing the power of the single cell on a large scale (Zheng et al. Nat Commun 8:14049, 2017)-capturing thousands of cells at once-has shaped life sciences ever since and allowed researchers to discover new insights within their respective fields of study such as oncology, neurobiology, and immunology (among others). Obtaining high-data quality is the key to being able to make these meaningful discoveries, which in turn is directly linked to the quality of the initial cell (or nuclei) suspension that is used to load the 10x Genomics Chromium Single Cell Gene Expression assay. A successful workflow relies on a cell suspension which is fully dissociated, extremely clean, and of high viability. While the workflow itself has been detailed elsewhere (De Simone et al. Methods Mol Biol 1979:87-110, 2019), in this chapter we will focus on the importance of the quality of the initial cell suspension, as well as common mistakes that can occur while running a Single Cell Gene Expression assay. The descriptions of these tips and tricks refer to the current version of the 10x Genomics User Guide (Chromium Single Cell 3' Reagent Kits User Guide (v3.1 Chemistry Dual Index). https://support10xgenomicscom/single-cell-gene-expression/index/doc/user-guide-chromium-single-cell-3-reagent-kits-user-guide-v31-chemistry-dual-index) which can be downloaded from the Support section on the 10x Genomics website (10x Genomics website. https://www10xgenomicscom). These documents and user guides are continuously improved and updated; hence, it is important to regularly check the company's website for the most recent version.
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Alessandri L, Calogero RA. Functional-Feature-Based Data Reduction Using Sparsely Connected Autoencoders. Methods Mol Biol 2022; 2584:231-240. [PMID: 36495453 DOI: 10.1007/978-1-0716-2756-3_11] [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: 12/13/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) allows for the creation of large collections of individual cells transcriptome. Unsupervised clustering is an essential element for the analysis of these data, and it represents the initial step for the identification of different cell types to investigate the cell subpopulation structure of a biological sample. However, it is possible that the clustering aggregation features do not perfectly match the underlying biology since scRNA-seq data are characterized by high noise. In this chapter, we describe a functional feature-driven data reduction approach, which could provide a better link among cell clusters and their underlying cell biology.
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Affiliation(s)
- Luca Alessandri
- Molecular Biotechnology Center, University of Torino, Turin, Italy.
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8
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Olivero M, Calogero RA. Single-Cell RNAseq Data QC and Preprocessing. Methods Mol Biol 2022; 2584:205-215. [PMID: 36495451 DOI: 10.1007/978-1-0716-2756-3_9] [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: 12/13/2022]
Abstract
The first step in single-cell RNAseq data analysis is the evaluation of the overall quality of the cell transcriptome and the preparation of the single-cell transcription data for clustering. In this chapter, we describe one of the possible approaches to perform single-cell data preprocessing for 3' end single-cell RNAseq transcriptomics data.
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Affiliation(s)
- Martina Olivero
- Department of Oncology, University of Torino, Torino, Italy. .,Candiolo Cancer Institute-FPO, IRCCS, Candiolo, TO, Italy.
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9
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Beccuti M, Calogero RA. Single-Cell RNAseq Clustering. Methods Mol Biol 2022; 2584:241-250. [PMID: 36495454 DOI: 10.1007/978-1-0716-2756-3_12] [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: 12/13/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) allows the creation of large collections of individual cells transcriptome. Unsupervised clustering is an essential element for the analysis of these data, and it represents the initial step for the identification of different cell types to investigate the cell subpopulation organization of a sample. In this chapter, we describe how to approach the clustering of single-cell RNAseq transcriptomics data using various clustering tools, and we provide some information on the limitations affecting the clustering procedure.
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Affiliation(s)
- Marco Beccuti
- Department of Computer Science, University of Torino, Turin, Italy.
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10
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Antico F, Gai M, Arigoni M. Tissue RNA Integrity in Visium Spatial Protocol (Fresh Frozen Samples). Methods Mol Biol 2022; 2584:191-203. [PMID: 36495450 DOI: 10.1007/978-1-0716-2756-3_8] [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: 12/13/2022]
Abstract
The transcriptome of a tissue can be acquired both by single-cell RNAseq (scRNA-seq) and by spatial transcriptomics (ST). The dissociation step, which is mandatory in scRNA-seq methods, might lead to the loss of fragile cells and of spatial information, thus limiting the acquisition of the tissue cellular organization. Spatial transcriptomics methods moderate the above-mentioned issues and provide single-cell transcripts detection over an intact fresh frozen tissue section. Visium platform, commercialized from 10× Genomics, provides a whole transcriptome spatial transcriptomics platform, which does not require dedicated instruments, other than those available in any pathology laboratory. In spatial transcriptomics, proper tissue handling is mandatory to preserve the morphological quality of the tissue sections and the integrity of mRNA transcripts. Proper tissue handling is critical for downstream library preparation and sequencing performance. In this chapter, we describe the most critical steps of Visium protocol on fresh frozen tissues and we provide indications on how to interpret the data obtained from the quality control analysis recommended during the workflow.
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Affiliation(s)
- Federica Antico
- Molecular Biotechnology Center, University of Torino, Torino, Italy
| | - Marta Gai
- Molecular Biotechnology Center, University of Torino, Torino, Italy
| | - Maddalena Arigoni
- Molecular Biotechnology Center, University of Torino, Torino, Italy.
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Abstract
The idea behind novel single-cell RNA sequencing (scRNA-seq) pipelines is to isolate single cells through microfluidic approaches and generate sequencing libraries in which the transcripts are tagged to track their cell of origin. Modern scRNA-seq platforms are capable of analyzing up to many thousands of cells in each run. Then, combined with massive high-throughput sequencing producing billions of reads, scRNA-seq allows the assessment of fundamental biological properties of cell populations and biological systems at unprecedented resolution.In this chapter, we describe how cell subpopulation discovery algorithms, integrated into rCASC, could be efficiently executed on cloud-HPC infrastructure. To achieve this task, we focus on the StreamFlow framework which provides container-native runtime support for scientific workflows in cloud/HPC environments.
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12
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Identifying Gene Markers Associated with Cell Subpopulations. Methods Mol Biol 2022; 2584:251-268. [PMID: 36495455 DOI: 10.1007/978-1-0716-2756-3_13] [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: 12/13/2022]
Abstract
An important point of the analysis of a single-cell RNA experiment is the identification of the key elements, i.e., genes, characterizing each cell subpopulation cluster. In this chapter, we describe the use of sparsely connected autoencoder, as a tool to convert single-cell clusters in pseudo-RNAseq experiments to be used as input for differential expression analysis, and the use of COMET, as a tool to depict cluster-specific gene markers.
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13
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Avesani S, Viesi E, Alessandrì L, Motterle G, Bonnici V, Beccuti M, Calogero R, Giugno R. Stardust: improving spatial transcriptomics data analysis through space-aware modularity optimization-based clustering. Gigascience 2022; 11:6659721. [PMID: 35946989 PMCID: PMC9364686 DOI: 10.1093/gigascience/giac075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 04/27/2022] [Accepted: 06/30/2022] [Indexed: 01/24/2023] Open
Abstract
Background Spatial transcriptomics (ST) combines stained tissue images with spatially resolved high-throughput RNA sequencing. The spatial transcriptomic analysis includes challenging tasks like clustering, where a partition among data points (spots) is defined by means of a similarity measure. Improving clustering results is a key factor as clustering affects subsequent downstream analysis. State-of-the-art approaches group data by taking into account transcriptional similarity and some by exploiting spatial information as well. However, it is not yet clear how much the spatial information combined with transcriptomics improves the clustering result. Results We propose a new clustering method, Stardust, that easily exploits the combination of space and transcriptomic information in the clustering procedure through a manual or fully automatic tuning of algorithm parameters. Moreover, a parameter-free version of the method is also provided where the spatial contribution depends dynamically on the expression distances distribution in the space. We evaluated the proposed methods results by analyzing ST data sets available on the 10x Genomics website and comparing clustering performances with state-of-the-art approaches by measuring the spots' stability in the clusters and their biological coherence. Stability is defined by the tendency of each point to remain clustered with the same neighbors when perturbations are applied. Conclusions Stardust is an easy-to-use methodology allowing to define how much spatial information should influence clustering on different tissues and achieving more stable results than state-of-the-art approaches.
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Affiliation(s)
- Simone Avesani
- Department of Computer Science, University of Verona, Verona 37134, Italy
| | - Eva Viesi
- Department of Computer Science, University of Verona, Verona 37134, Italy
| | - Luca Alessandrì
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin 10126, Italy
| | - Giovanni Motterle
- Department of Computer Science, University of Verona, Verona 37134, Italy
| | - Vincenzo Bonnici
- Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma 43121, Italy
| | - Marco Beccuti
- Department of Computer Science, University of Turin, Turin 10149, Italy
| | - Raffaele Calogero
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin 10126, Italy
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Verona 37134, Italy
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14
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Abondio P, De Intinis C, da Silva Gonçalves Vianez Júnior JL, Pace L. SINGLE CELL MULTIOMIC APPROACHES TO DISENTANGLE T CELL HETEROGENEITY. Immunol Lett 2022; 246:37-51. [DOI: 10.1016/j.imlet.2022.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/16/2022] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
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15
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Bao S, Li K, Yan C, Zhang Z, Qu J, Zhou M. Deep learning-based advances and applications for single-cell RNA-sequencing data analysis. Brief Bioinform 2021; 23:6444320. [PMID: 34849562 DOI: 10.1093/bib/bbab473] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/24/2021] [Accepted: 10/15/2021] [Indexed: 11/14/2022] Open
Abstract
The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique challenges in upstream (quality control and normalization) and downstream (cell-, gene- and pathway-level) analysis of scRNA-seq data. In the present study, recent advances and applications of deep learning-based methods, together with specific tools for scRNA-seq data analysis, were summarized. Moreover, the future perspectives and challenges of deep-learning techniques regarding the appropriate analysis and interpretation of scRNA-seq data were investigated. The present study aimed to provide evidence supporting the biomedical application of deep learning-based tools and may aid biologists and bioinformaticians in navigating this exciting and fast-moving area.
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Affiliation(s)
- Siqi Bao
- School of Information and Communication Engineering, Hainan University, Haikou 570228, P. R. China.,School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China.,Hainan Institute of Real World Data, Haikou 570228, P. R. China
| | - Ke Li
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Congcong Yan
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Zicheng Zhang
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
| | - Jia Qu
- School of Information and Communication Engineering, Hainan University, Haikou 570228, P. R. China.,School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China.,Hainan Institute of Real World Data, Haikou 570228, P. R. China
| | - Meng Zhou
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China
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16
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Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis. Int J Mol Sci 2021; 22:ijms222312755. [PMID: 34884559 PMCID: PMC8657975 DOI: 10.3390/ijms222312755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/12/2021] [Accepted: 11/23/2021] [Indexed: 02/02/2023] Open
Abstract
Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. Methods: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. Results: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. Conclusions: In our opinion, SCA represents the bioinformatics version of a universal “Swiss-knife” for the extraction of hidden knowledgeable features from single cell omics data.
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17
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Tangaro MA, Mandreoli P, Chiara M, Donvito G, Antonacci M, Parisi A, Bianco A, Romano A, Bianchi DM, Cangelosi D, Uva P, Molineris I, Nosi V, Calogero RA, Alessandri L, Pedrini E, Mordenti M, Bonetti E, Sangiorgi L, Pesole G, Zambelli F. Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service. BMC Bioinformatics 2021; 22:544. [PMID: 34749633 PMCID: PMC8574934 DOI: 10.1186/s12859-021-04401-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment. RESULTS "Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform. CONCLUSIONS During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.
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Affiliation(s)
- Marco Antonio Tangaro
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126, Bari, Italy
- National Institute for Nuclear Physics (INFN), Section of Bari, Via Orabona 4, 70126, Bari, Italy
| | - Pietro Mandreoli
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126, Bari, Italy
- Department of Biosciences, University of Milan, Via Celoria 26, 20133, Milano, Italy
| | - Matteo Chiara
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126, Bari, Italy
- Department of Biosciences, University of Milan, Via Celoria 26, 20133, Milano, Italy
| | - Giacinto Donvito
- National Institute for Nuclear Physics (INFN), Section of Bari, Via Orabona 4, 70126, Bari, Italy
| | - Marica Antonacci
- National Institute for Nuclear Physics (INFN), Section of Bari, Via Orabona 4, 70126, Bari, Italy
| | - Antonio Parisi
- Istituto Zooprofilattico Sperimentale Della Puglia e Della Basilicata, Via Manfredonia 20, 71121, Foggia, Italy
| | - Angelica Bianco
- Istituto Zooprofilattico Sperimentale Della Puglia e Della Basilicata, Via Manfredonia 20, 71121, Foggia, Italy
| | - Angelo Romano
- National Reference Laboratory for Coagulase-Positive Staphylococci Including Staphylococcus Aureus, Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Daniela Manila Bianchi
- National Reference Laboratory for Coagulase-Positive Staphylococci Including Staphylococcus Aureus, Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Davide Cangelosi
- Clinical Bioinformatics Unit, Scientific Direction, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147, Genova, Italy
| | - Paolo Uva
- Clinical Bioinformatics Unit, Scientific Direction, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147, Genova, Italy
- Italian Institute of Technology, Via Morego 30, 16163, Genova, Italy
| | - Ivan Molineris
- Department of Life Science and System Biology, University of Turin, Via Accademia Albertina, 13-1023, Turin, Italy
| | - Vladimir Nosi
- Department of Computer Science, University of Turin, Via Pessinetto 12, 10049, Turin, Italy
| | - Raffaele A Calogero
- Department of Molecular Biotechnology and Health Sciences, Via Nizza 52, 10126, Turin, Italy
| | - Luca Alessandri
- Department of Molecular Biotechnology and Health Sciences, Via Nizza 52, 10126, Turin, Italy
| | - Elena Pedrini
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy
| | - Marina Mordenti
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy
| | - Emanuele Bonetti
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139, Milan, Italy
| | - Luca Sangiorgi
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy
| | - Graziano Pesole
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126, Bari, Italy.
- Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, Via Orabona 4, 70126, Bari, Italy.
| | - Federico Zambelli
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126, Bari, Italy.
- Department of Biosciences, University of Milan, Via Celoria 26, 20133, Milano, Italy.
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18
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Zhao Y, Panzer U, Bonn S, Krebs CF. Single-cell biology to decode the immune cellular composition of kidney inflammation. Cell Tissue Res 2021; 385:435-443. [PMID: 34125286 PMCID: PMC8200789 DOI: 10.1007/s00441-021-03483-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 06/03/2021] [Indexed: 12/26/2022]
Abstract
Single-cell biology is transforming the ability of researchers to understand cellular signaling and identity across medical and biological disciplines. Especially for immune-mediated diseases, a single-cell look at immune cell subtypes, signaling, and activity might yield fundamental insights into the disease etiology, mechanisms, and potential therapeutic interventions. In this review, we highlight recent advances in the field of single-cell RNA profiling and their application to understand renal function in health and disease. With a focus on the immune system, in particular on T cells, we propose some key directions of understanding renal inflammation using single-cell approaches. We detail the benefits and shortcomings of the various technological approaches outlined and give advice on potential pitfalls and challenges in experimental setup and computational analysis. Finally, we conclude with a brief outlook into a promising future for single-cell technologies to elucidate kidney function.
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Affiliation(s)
- Yu Zhao
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Translational Immunology, III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Ulf Panzer
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Translational Immunology, III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian F Krebs
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Translational Immunology, III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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19
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Alessandri L, Cordero F, Beccuti M, Licheri N, Arigoni M, Olivero M, Di Renzo MF, Sapino A, Calogero R. Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining. NPJ Syst Biol Appl 2021; 7:1. [PMID: 33402683 PMCID: PMC7785742 DOI: 10.1038/s41540-020-00162-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 11/26/2020] [Indexed: 01/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.
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Affiliation(s)
- Luca Alessandri
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy
| | - Francesca Cordero
- Department of Computer Sciences, University of Torino, Torino, Italy
| | - Marco Beccuti
- Department of Computer Sciences, University of Torino, Torino, Italy
| | - Nicola Licheri
- Department of Computer Sciences, University of Torino, Torino, Italy
| | - Maddalena Arigoni
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy
| | - Martina Olivero
- Department of Oncology, University of Torino, Torino, Italy.,Candiolo Cancer Institute-FPO, IRCCS, Candiolo (To), Candiolo, Italy
| | - Maria Flavia Di Renzo
- Department of Oncology, University of Torino, Torino, Italy.,Candiolo Cancer Institute-FPO, IRCCS, Candiolo (To), Candiolo, Italy
| | - Anna Sapino
- Candiolo Cancer Institute-FPO, IRCCS, Candiolo (To), Candiolo, Italy.,Department of Medical Sciences, University of Torino, Torino, Italy
| | - Raffaele Calogero
- Department of Molecular Biotechnology and Health Science, University of Torino, Torino, Italy.
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20
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Cheung P, Xiol J, Dill MT, Yuan WC, Panero R, Roper J, Osorio FG, Maglic D, Li Q, Gurung B, Calogero RA, Yilmaz ÖH, Mao J, Camargo FD. Regenerative Reprogramming of the Intestinal Stem Cell State via Hippo Signaling Suppresses Metastatic Colorectal Cancer. Cell Stem Cell 2020; 27:590-604.e9. [PMID: 32730753 PMCID: PMC10114498 DOI: 10.1016/j.stem.2020.07.003] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 04/01/2020] [Accepted: 07/01/2020] [Indexed: 12/13/2022]
Abstract
Although the Hippo transcriptional coactivator YAP is considered oncogenic in many tissues, its roles in intestinal homeostasis and colorectal cancer (CRC) remain controversial. Here, we demonstrate that the Hippo kinases LATS1/2 and MST1/2, which inhibit YAP activity, are required for maintaining Wnt signaling and canonical stem cell function. Hippo inhibition induces a distinct epithelial cell state marked by low Wnt signaling, a wound-healing response, and transcription factor Klf6 expression. Notably, loss of LATS1/2 or overexpression of YAP is sufficient to reprogram Lgr5+ cancer stem cells to this state and thereby suppress tumor growth in organoids, patient-derived xenografts, and mouse models of primary and metastatic CRC. Finally, we demonstrate that genetic deletion of YAP and its paralog TAZ promotes the growth of these tumors. Collectively, our results establish the role of YAP as a tumor suppressor in the adult colon and implicate Hippo kinases as therapeutic vulnerabilities in colorectal malignancies.
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Affiliation(s)
- Priscilla Cheung
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Jordi Xiol
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Michael T Dill
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Wei-Chien Yuan
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Riccardo Panero
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, 10126 Torino, Italy
| | - Jatin Roper
- Division of Gastroenterology, Department of Medicine, Duke University, Durham, NC 27710, USA; Department of Pharmacology and Cancer Biology, Duke University, Durham, NC 27710, USA
| | - Fernando G Osorio
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Dejan Maglic
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Qi Li
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA; Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Basanta Gurung
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Raffaele A Calogero
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, 10126 Torino, Italy
| | - Ömer H Yilmaz
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA 02139, USA; Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Junhao Mao
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Fernando D Camargo
- Stem Cell Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA.
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21
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Macedo A, Gontijo AM. The intersectional genetics landscape for humans. Gigascience 2020; 9:giaa083. [PMID: 32761099 PMCID: PMC7407247 DOI: 10.1093/gigascience/giaa083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 04/05/2020] [Accepted: 07/08/2020] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND The human body is made up of hundreds-perhaps thousands-of cell types and states, most of which are currently inaccessible genetically. Intersectional genetic approaches can increase the number of genetically accessible cells, but the scope and safety of these approaches have not been systematically assessed. A typical intersectional method acts like an "AND" logic gate by converting the input of 2 or more active, yet unspecific, regulatory elements (REs) into a single cell type specific synthetic output. RESULTS Here, we systematically assessed the intersectional genetics landscape of the human genome using a subset of cells from a large RE usage atlas (Functional ANnoTation Of the Mammalian genome 5 consortium, FANTOM5) obtained by cap analysis of gene expression sequencing (CAGE-seq). We developed the heuristics and algorithms to retrieve and quality-rank "AND" gate intersections. Of the 154 primary cell types surveyed, >90% can be distinguished from each other with as few as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential "versatile entry codes" (VEnCodes). Each of the 158 cancer cell types surveyed could also be distinguished from the healthy primary cell types with small VEnCodes, most of which were robust to intra- and interindividual variation. Methods for the cross-validation of CAGE-seq-derived VEnCodes and for the extraction of VEnCodes from pooled single-cell sequencing data are also presented. CONCLUSIONS Our work provides a systematic view of the intersectional genetics landscape in humans and demonstrates the potential of these approaches for future gene delivery technologies.
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Affiliation(s)
- Andre Macedo
- Chronic Diseases Research Center, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Rua do Instituto Bacteriológico 5, 1150–190, Lisbon, Portugal
| | - Alisson M Gontijo
- Chronic Diseases Research Center, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Rua do Instituto Bacteriológico 5, 1150–190, Lisbon, Portugal
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22
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Alessandrì L, Cordero F, Beccuti M, Arigoni M, Olivero M, Romano G, Rabellino S, Licheri N, De Libero G, Pace L, Calogero RA. rCASC: reproducible classification analysis of single-cell sequencing data. Gigascience 2020; 8:5565135. [PMID: 31494672 PMCID: PMC6732171 DOI: 10.1093/gigascience/giz105] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 04/12/2019] [Accepted: 08/08/2019] [Indexed: 01/05/2023] Open
Abstract
Background Single-cell RNA sequencing is essential for investigating cellular heterogeneity and highlighting cell subpopulation-specific signatures. Single-cell sequencing applications have spread from conventional RNA sequencing to epigenomics, e.g., ATAC-seq. Many related algorithms and tools have been developed, but few computational workflows provide analysis flexibility while also achieving functional (i.e., information about the data and the tools used are saved as metadata) and computational reproducibility (i.e., a real image of the computational environment used to generate the data is stored) through a user-friendly environment. Findings rCASC is a modular workflow providing an integrated analysis environment (from count generation to cell subpopulation identification) exploiting Docker containerization to achieve both functional and computational reproducibility in data analysis. Hence, rCASC provides preprocessing tools to remove low-quality cells and/or specific bias, e.g., cell cycle. Subpopulation discovery can instead be achieved using different clustering techniques based on different distance metrics. Cluster quality is then estimated through the new metric "cell stability score" (CSS), which describes the stability of a cell in a cluster as a consequence of a perturbation induced by removing a random set of cells from the cell population. CSS provides better cluster robustness information than the silhouette metric. Moreover, rCASC's tools can identify cluster-specific gene signatures. Conclusions rCASC is a modular workflow with new features that could help researchers define cell subpopulations and detect subpopulation-specific markers. It uses Docker for ease of installation and to achieve a computation-reproducible analysis. A Java GUI is provided to welcome users without computational skills in R.
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Affiliation(s)
- Luca Alessandrì
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10125 Torino, Italy
| | - Francesca Cordero
- Department of Computer Science, University of Torino, Corso Svizzera 185, 10149 Torino, Italy
| | - Marco Beccuti
- Department of Computer Science, University of Torino, Corso Svizzera 185, 10149 Torino, Italy
| | - Maddalena Arigoni
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10125 Torino, Italy
| | - Martina Olivero
- Department of Oncology, University of Torino, SP142, 95, 10060 Candiolo (TO), Italy
| | - Greta Romano
- Department of Computer Science, University of Torino, Corso Svizzera 185, 10149 Torino, Italy
| | - Sergio Rabellino
- Department of Computer Science, University of Torino, Corso Svizzera 185, 10149 Torino, Italy
| | - Nicola Licheri
- Department of Computer Science, University of Torino, Corso Svizzera 185, 10149 Torino, Italy
| | - Gennaro De Libero
- Department Biomedizin, University of Basel, Hebelstrasse 20, 4031 Basel, Switzerland
| | - Luigia Pace
- Italian Istitute for Genomic Medicine, IIGM, c/o IRCCS 10060 Candiolo (TO), Italy
| | - Raffaele A Calogero
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, 10125 Torino, Italy
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