1
|
Bessière C, Xue H, Guibert B, Boureux A, Rufflé F, Viot J, Chikhi R, Salson M, Marchet C, Commes T, Gautheret D. Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data. Genome Biol 2024; 25:266. [PMID: 39390592 PMCID: PMC11468207 DOI: 10.1186/s13059-024-03413-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 10/01/2024] [Indexed: 10/12/2024] Open
Abstract
Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.
Collapse
Affiliation(s)
- Chloé Bessière
- IRMB, INSERM U1183, Hopital Saint-Eloi, Universite de Montpellier, Montpellier, France
- CRCT, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Haoliang Xue
- I2BC, Université Paris-Saclay, CNRS, CEA, Gif sur Yvette, France
| | - Benoit Guibert
- IRMB, INSERM U1183, Hopital Saint-Eloi, Universite de Montpellier, Montpellier, France
| | - Anthony Boureux
- IRMB, INSERM U1183, Hopital Saint-Eloi, Universite de Montpellier, Montpellier, France
| | - Florence Rufflé
- IRMB, INSERM U1183, Hopital Saint-Eloi, Universite de Montpellier, Montpellier, France
| | - Julien Viot
- Department of Medical Oncology, Biotechnology and Immuno-Oncology Platform, University Hospital of Besançon, Besançon, France
- INSERM, EFS BFC, UMR1098, RIGHT, University of Franche-Comté, Interactions Greffon-Hôte-Tumeur/Ingénierie Cellulaire et Génique, Besançon, France
| | - Rayan Chikhi
- Institut Pasteur, Université Paris Cité, Paris, France
| | - Mikaël Salson
- Université de Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000, Lille, France
| | - Camille Marchet
- Université de Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000, Lille, France
| | - Thérèse Commes
- IRMB, INSERM U1183, Hopital Saint-Eloi, Universite de Montpellier, Montpellier, France.
| | - Daniel Gautheret
- I2BC, Université Paris-Saclay, CNRS, CEA, Gif sur Yvette, France.
| |
Collapse
|
2
|
Yu Y, Mai Y, Zheng Y, Shi L. Assessing and mitigating batch effects in large-scale omics studies. Genome Biol 2024; 25:254. [PMID: 39363244 PMCID: PMC11447944 DOI: 10.1186/s13059-024-03401-9] [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: 07/11/2023] [Accepted: 09/23/2024] [Indexed: 10/05/2024] Open
Abstract
Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.
Collapse
Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
- Cancer Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
| |
Collapse
|
3
|
Bonine N, Zanzani V, Van Hemelryk A, Vanneste B, Zwicker C, Thoné T, Roelandt S, Bekaert SL, Koster J, Janoueix-Lerosey I, Thirant C, Van Haver S, Roberts SS, Mus LM, De Wilde B, Van Roy N, Everaert C, Speleman F, Vermeirssen V, Scott CL, De Preter K. NBAtlas: A harmonized single-cell transcriptomic reference atlas of human neuroblastoma tumors. Cell Rep 2024; 43:114804. [PMID: 39368085 DOI: 10.1016/j.celrep.2024.114804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/11/2024] [Accepted: 09/12/2024] [Indexed: 10/07/2024] Open
Abstract
Neuroblastoma, a rare embryonic tumor arising from neural crest development, is responsible for 15% of pediatric cancer-related deaths. Recently, several single-cell transcriptome studies were performed on neuroblastoma patient samples to investigate the cell of origin and tumor heterogeneity. However, these individual studies involved a small number of tumors and cells, limiting the conclusions that could be drawn. To overcome this limitation, we integrated seven single-cell or single-nucleus datasets into a harmonized cell atlas covering 362,991 cells across 61 patients. We use this atlas to decipher the transcriptional landscape of neuroblastoma at single-cell resolution, revealing associations between transcriptomic profiles and clinical outcomes within the tumor compartment. In addition, we characterize the complex immune-cell landscape and uncover considerable heterogeneity among tumor-associated macrophages. Finally, we showcase the utility of our atlas as a resource by expanding it with additional data and using it as a reference for data-driven cell-type annotation.
Collapse
Affiliation(s)
- Noah Bonine
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Vittorio Zanzani
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium; Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium; Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR), Ghent University, Ghent, Belgium
| | - Annelies Van Hemelryk
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium; Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium; Laboratory of Myeloid Cell Biology in Tissue Damage and Inflammation, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, 9052 Ghent, Belgium
| | - Bavo Vanneste
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium; Laboratory of Myeloid Cell Biology in Tissue Damage and Inflammation, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, 9052 Ghent, Belgium
| | - Christian Zwicker
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium; Laboratory of Myeloid Cell Biology in Tissue Damage and Inflammation, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, 9052 Ghent, Belgium
| | - Tinne Thoné
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium; Laboratory of Myeloid Cell Biology in Tissue Damage and Inflammation, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, 9052 Ghent, Belgium
| | - Sofie Roelandt
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Sarah-Lee Bekaert
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Jan Koster
- Amsterdam UMC Location University of Amsterdam, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Isabelle Janoueix-Lerosey
- Inserm U830, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France
| | - Cécile Thirant
- Inserm U830, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France
| | - Stéphane Van Haver
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium; Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Stephen S Roberts
- Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA
| | - Liselot M Mus
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Bram De Wilde
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Nadine Van Roy
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Celine Everaert
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Frank Speleman
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Vanessa Vermeirssen
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium; Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium; Laboratory for Computational Biology, Integromics and Gene Regulation (CBIGR), Ghent University, Ghent, Belgium
| | - Charlotte L Scott
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium; Laboratory of Myeloid Cell Biology in Tissue Damage and Inflammation, VIB-UGent Center for Inflammation Research, Technologiepark-Zwijnaarde 71, 9052 Ghent, Belgium.
| | - Katleen De Preter
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent (CRIG), Ghent, Belgium.
| |
Collapse
|
4
|
Su Q, Long Y, Gou D, Quan J, Lian Q. Enhancing RNA-seq bias mitigation with the Gaussian self-benchmarking framework: towards unbiased sequencing data. BMC Genomics 2024; 25:904. [PMID: 39350040 PMCID: PMC11441123 DOI: 10.1186/s12864-024-10814-0] [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: 07/08/2024] [Accepted: 09/19/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND RNA sequencing is a vital technique for analyzing RNA behavior in cells, but it often suffers from various biases that distort the data. Traditional methods to address these biases are typically empirical and handle them individually, limiting their effectiveness. Our study introduces the Gaussian Self-Benchmarking (GSB) framework, a novel approach that leverages the natural distribution patterns of guanine (G) and cytosine (C) content in RNA to mitigate multiple biases simultaneously. This method is grounded in a theoretical model, organizing k-mers based on their GC content and applying a Gaussian model for alignment to ensure empirical sequencing data closely match their theoretical distribution. RESULTS The GSB framework demonstrated superior performance in mitigating sequencing biases compared to existing methods. Testing with synthetic RNA constructs and real human samples showed that the GSB approach not only addresses individual biases more effectively but also manages co-existing biases jointly. The framework's reliance on accurately pre-determined parameters like mean and standard deviation of GC content distribution allows for a more precise representation of RNA samples. This results in improved accuracy and reliability of RNA sequencing data, enhancing our understanding of RNA behavior in health and disease. CONCLUSIONS The GSB framework presents a significant advancement in RNA sequencing analysis by providing a well-validated, multi-bias mitigation strategy. It functions independently from previously identified dataset flaws and sets a new standard for unbiased RNA sequencing results. This development enhances the reliability of RNA studies, broadening the potential for scientific breakthroughs in medicine and biology, particularly in genetic disease research and the development of targeted treatments.
Collapse
Affiliation(s)
- Qiang Su
- Faculty of Synthetic Biology, Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Shenzhen University of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- State Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China.
| | - Yi Long
- Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Deming Gou
- Shenzhen Key Laboratory of Microbial Genetic Engineering, Vascular Disease Research Center, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Junmin Quan
- State Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China.
| | - Qizhou Lian
- Faculty of Synthetic Biology, Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Shenzhen University of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- Cord Blood Bank, Guangzhou Institute of Eugenics and Perinatology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| |
Collapse
|
5
|
Meier MJ, Harrill J, Johnson K, Thomas RS, Tong W, Rager JE, Yauk CL. Progress in toxicogenomics to protect human health. Nat Rev Genet 2024:10.1038/s41576-024-00767-1. [PMID: 39223311 DOI: 10.1038/s41576-024-00767-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/23/2024] [Indexed: 09/04/2024]
Abstract
Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.
Collapse
Affiliation(s)
- Matthew J Meier
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - Kamin Johnson
- Predictive Safety Center, Corteva Agriscience, Indianapolis, IN, USA
| | - Russell S Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR, USA
- Curriculum in Toxicology & Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Julia E Rager
- Curriculum in Toxicology & Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
- The Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina, Chapel Hill, NC, USA
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Carole L Yauk
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.
| |
Collapse
|
6
|
Cogan JA, Benova N, Kuklinkova R, Boyne JR, Anene CA. Meta-analysis of RNA interaction profiles of RNA-binding protein using the RBPInper tool. BIOINFORMATICS ADVANCES 2024; 4:vbae127. [PMID: 39233897 PMCID: PMC11374027 DOI: 10.1093/bioadv/vbae127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/29/2024] [Accepted: 08/22/2024] [Indexed: 09/06/2024]
Abstract
Motivation Recent RNA-centric experimental methods have significantly expanded our knowledge of proteins with known RNA-binding functions. However, the complete regulatory network and pathways for many of these RNA-binding proteins (RBPs) in different cellular contexts remain unknown. Although critical to understanding the role of RBPs in health and disease, experimentally mapping the RBP-RNA interactomes in every single context is an impossible task due the cost and manpower required. Additionally, identifying relevant RNAs bound by RBPs is challenging due to their diverse binding modes and function. Results To address these challenges, we developed RBP interaction mapper RBPInper an integrative framework that discovers global RBP interactome using statistical data fusion. Experiments on splicing factor proline and glutamine rich (SFPQ) datasets revealed cogent global SFPQ interactome. Several biological processes associated with this interactome were previously linked with SFPQ function. Furthermore, we conducted tests using independent dataset to assess the transferability of the SFPQ interactome to another context. The results demonstrated robust utility in generating interactomes that transfers to unseen cellular context. Overall, RBPInper is a fast and user-friendly method that enables a systems-level understanding of RBP functions by integrating multiple molecular datasets. The tool is designed with a focus on simplicity, minimal dependencies, and straightforward input requirements. This intentional design aims to empower everyday biologists, making it easy for them to incorporate the tool into their research. Availability and implementation The source code, documentation, and installation instructions as well as results for use case are freely available at https://github.com/AneneLab/RBPInper. A user can easily compile similar datasets for a target RBP.
Collapse
Affiliation(s)
- Joseph A Cogan
- School of Biological Sciences, University of Huddersfield, Huddersfield, HD1 3DH, United Kingdom
- School of Molecular and Cellular Biology, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Natalia Benova
- Centre for Biomedical Science Research, School of Health, Leeds Beckett University, Leeds, LS1 3HE, United Kingdom
| | - Rene Kuklinkova
- Centre for Biomedical Science Research, School of Health, Leeds Beckett University, Leeds, LS1 3HE, United Kingdom
| | - James R Boyne
- Centre for Biomedical Science Research, School of Health, Leeds Beckett University, Leeds, LS1 3HE, United Kingdom
| | - Chinedu A Anene
- Centre for Biomedical Science Research, School of Health, Leeds Beckett University, Leeds, LS1 3HE, United Kingdom
- Centre for Cancer Genomics and Computation Biology, Barts Cancer Institute, Queen Mary University of London, London, E1 4NS, United Kingdom
| |
Collapse
|
7
|
Cheng J, Ji D, Ma J, Zhang Q, Zhang W, Yang L. Proteomic analysis of serum small extracellular vesicles identifies diagnostic biomarkers for neuroblastoma. Front Oncol 2024; 14:1367159. [PMID: 39228987 PMCID: PMC11368728 DOI: 10.3389/fonc.2024.1367159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 07/30/2024] [Indexed: 09/05/2024] Open
Abstract
Background Neuroblastoma (NB) primarily arises in children who are <10 years of age, and originates from developing sympathetic nervous system, which results in tumors in adrenal glands and/or sympathetic ganglia. The diagnosis of NB involves a combination of laboratory and imaging tests, and biopsies. Small extracellular vesicles (sEVs) have gained attention as potential biomarkers for various types of tumors. Here, we performed proteomic analysis of serum sEVs and identified potential biomarkers for NB. Methods Label-free proteomics of serum sEVs were performed in the discovery phase. A bulk RNA-seq dataset of NB tissues was used to analyze the association between genes encoding sEVs proteins and prognosis. Potential biomarkers were validated via multiple reaction monitoring (MRM) or western blot analysis in the validation phase. A public single-cell RNA-seq (scRNA-seq) dataset was integrated to analyze the tissue origin of sEVs harboring biomarkers. Results A total of 104 differentially expressed proteins were identified in NB patients with label-free proteomics, and 26 potential biomarkers were validated with MRM analysis. Seven proteins BSG, HSP90AB1, SLC44A1, CHGA, ATP6V0A1, ITGAL and SELL showed the strong ability to distinguish NB patients from healthy controls and non-NB patients as well. Integrated analysis of scRNA-seq and sEVs proteomics revealed that these sEVs-derived biomarkers originated from different cell populations in tumor tissues. Conclusion sEVs-based biomarkers may aid the molecular diagnosis of NB, representing an innovative strategy to improve NB detection and management.
Collapse
Affiliation(s)
- Juan Cheng
- Department of Clinical Laboratory, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dongrui Ji
- Wayen Biotechnologies (Shanghai), Inc., Shanghai, China
| | - Jing Ma
- Department of Pathology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qinghua Zhang
- Wayen Biotechnologies (Shanghai), Inc., Shanghai, China
| | - Wanglin Zhang
- Department of Orthopaedics, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lin Yang
- Department of Clinical Laboratory, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
8
|
Kasianova AM, Penin AA, Schelkunov MI, Kasianov AS, Logacheva MD, Klepikova AV. Trans2express - de novo transcriptome assembly pipeline optimized for gene expression analysis. PLANT METHODS 2024; 20:128. [PMID: 39152473 PMCID: PMC11330051 DOI: 10.1186/s13007-024-01255-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 08/01/2024] [Indexed: 08/19/2024]
Abstract
BACKGROUND As genomes of many eukaryotic species, especially plants, are large and complex, their de novo sequencing and assembly is still a difficult task despite progress in sequencing technologies. An alternative to genome assembly is the assembly of transcriptome, the set of RNA products of the expressed genes. While a bunch of de novo transcriptome assemblers exists, the challenges of transcriptomes (the existence of isoforms, the uneven expression levels across genes) complicates the generation of high-quality assemblies suitable for downstream analyses. RESULTS We developed Trans2express - a web-based tool and a pipeline of de novo hybrid transcriptome assembly and postprocessing based on rnaSPAdes with a set of subsequent filtrations. The pipeline was tested on Arabidopsis thaliana cDNA sequencing data obtained using Illumina and Oxford Nanopore Technologies platforms and three non-model plant species. The comparison of structural characteristics of the transcriptome assembly with reference Arabidopsis genome revealed the high quality of assembled transcriptome with 86.1% of Arabidopsis expressed genes assembled as a single contig. We tested the applicability of the transcriptome assembly for gene expression analysis. For both Arabidopsis and non-model species the results showed high congruence of gene expression levels and sets of differentially expressed genes between analyses based on genome and based on the transcriptome assembly. CONCLUSIONS We present Trans2express - a protocol for de novo hybrid transcriptome assembly aimed at recovering of a single transcript per gene. We expect this protocol to promote the characterization of transcriptomes and gene expression analysis in non-model plants and web-based tool to be of use to a wide range of plant biologists.
Collapse
Affiliation(s)
- Aleksandra M Kasianova
- Institute for Information Transmission, Russian Academy of Sciences, Moscow, Russia
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Aleksey A Penin
- Institute for Information Transmission, Russian Academy of Sciences, Moscow, Russia
| | - Mikhail I Schelkunov
- Institute for Information Transmission, Russian Academy of Sciences, Moscow, Russia
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Artem S Kasianov
- Institute for Information Transmission, Russian Academy of Sciences, Moscow, Russia
| | - Maria D Logacheva
- Institute for Information Transmission, Russian Academy of Sciences, Moscow, Russia
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Anna V Klepikova
- Institute for Information Transmission, Russian Academy of Sciences, Moscow, Russia.
| |
Collapse
|
9
|
Gong B, Li D, Łabaj PP, Pan B, Novoradovskaya N, Thierry-Mieg D, Thierry-Mieg J, Chen G, Bergstrom Lucas A, LoCoco JS, Richmond TA, Tseng E, Kusko R, Happe S, Mercer TR, Pabón-Peña C, Salmans M, Tilgner HU, Xiao W, Johann DJ, Jones W, Tong W, Mason CE, Kreil DP, Xu J. Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing. Sci Data 2024; 11:892. [PMID: 39152166 PMCID: PMC11329654 DOI: 10.1038/s41597-024-03741-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine.
Collapse
Affiliation(s)
- Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Dan Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Paweł P Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Bioinformatics Research, Institute of Molecular Biotechnology, Boku University Vienna, Vienna, Austria
| | - Bohu Pan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | | | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | - Guangchun Chen
- Department of Immunology, Genomics and Microarray Core Facility, University of Texas Southwestern Medical Center, 5323 Harry Hine Blvd., Dallas, TX, 75390, USA
| | - Anne Bergstrom Lucas
- Agilent Technologies, Inc., 5301 Stevens Creek Blvd., Santa Clara, CA, 95051, USA
| | | | - Todd A Richmond
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., 4300 Hacienda Dr., Pleasanton, CA, 94588, USA
| | | | - Rebecca Kusko
- Cellino Bio, 750 Main Street, Cambridge, MA, 02143, USA
| | - Scott Happe
- Agilent Technologies, Inc., 1834 State Hwy 71 West, Cedar Creek, TX, 78612, USA
| | - Timothy R Mercer
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD, Australia
| | - Carlos Pabón-Peña
- Agilent Technologies, Inc., 5301 Stevens Creek Blvd., Santa Clara, CA, 95051, USA
| | | | - Hagen U Tilgner
- Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Wenzhong Xiao
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Donald J Johann
- Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, 4301W Markham St., Little Rock, AR, 72205, USA
| | - Wendell Jones
- Q squared Solutions Genomics, 2400 Elis Road, Durham, NC, 27703, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
| | - David P Kreil
- Bioinformatics Research, Institute of Molecular Biotechnology, Boku University Vienna, Vienna, Austria.
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA.
| |
Collapse
|
10
|
Baykal PI, Łabaj PP, Markowetz F, Schriml LM, Stekhoven DJ, Mangul S, Beerenwinkel N. Genomic reproducibility in the bioinformatics era. Genome Biol 2024; 25:213. [PMID: 39123217 PMCID: PMC11312195 DOI: 10.1186/s13059-024-03343-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific knowledge and medical applications. Initially, we examine different interpretations of reproducibility in genomics to clarify terms. Subsequently, we discuss the impact of bioinformatics tools on genomic reproducibility and explore methods for evaluating these tools regarding their effectiveness in ensuring genomic reproducibility. Finally, we recommend best practices to improve genomic reproducibility.
Collapse
Affiliation(s)
- Pelin Icer Baykal
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058, Basel, Switzerland
| | - Paweł Piotr Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387, Gronostajowa 7A, Krakow, Poland
- Department of Biotechnology, Boku University Vienna, Muthgasse 18, 1190, Vienna, Austria
| | - Florian Markowetz
- Cancer Research UK Cambridge Research Institute, Cambridge, CB2 0RE, UK
- Department of Oncology, University of Cambridge, Cambridge, CB2 2XZ, UK
| | - Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, HSFIII, 670 W. Baltimore St, Baltimore, MD, 21201, USA
| | - Daniel J Stekhoven
- SIB Swiss Institute of Bioinformatics, 4058, Basel, Switzerland
- NEXUS Personalized Health Technologies, ETH Zurich, 8952, Zurich, Switzerland
| | - Serghei Mangul
- Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, 1540 Alcazar Street, Los Angeles, CA, 90033, USA.
- Department of Quantitative and Computational Biology, University of Southern California Dornsife College of Letters, Arts, and Sciences, Los Angeles, CA, 90089, USA.
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, 4058, Basel, Switzerland.
| |
Collapse
|
11
|
Liu H, Hu K, O’Connor K, Kelliher MA, Zhu LJ. CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data. BIOTECH 2024; 13:30. [PMID: 39189209 PMCID: PMC11348166 DOI: 10.3390/biotech13030030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/01/2024] [Accepted: 07/14/2024] [Indexed: 08/28/2024] Open
Abstract
RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.
Collapse
Affiliation(s)
- Haibo Liu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA; (H.L.); (K.H.); (M.A.K.)
| | - Kai Hu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA; (H.L.); (K.H.); (M.A.K.)
| | - Kevin O’Connor
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA; (H.L.); (K.H.); (M.A.K.)
| | - Michelle A. Kelliher
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA; (H.L.); (K.H.); (M.A.K.)
| | - Lihua Julie Zhu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA; (H.L.); (K.H.); (M.A.K.)
- Department of Molecular Medicine, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
| |
Collapse
|
12
|
Bao L, Sun Z, Dang L, Zhang Q, Zheng L, Yang F, Zhang J. LncRNA RP11-818O24.3 promotes hair-follicle recovery via FGF2-PI3K/Akt signal pathway. Cytotechnology 2024; 76:425-439. [PMID: 38933868 PMCID: PMC11196536 DOI: 10.1007/s10616-024-00624-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 02/16/2024] [Indexed: 06/28/2024] Open
Abstract
A previous study indicated that patients with androgenic alopecia (AGA) have significantly reduced levels of LncRNA RP11-818O24.3. This study investigates whether LncRNA RP11-818O24.3 promotes hair-follicle recovery and its possible mechanism. Hair alteration and cutaneous histopathological changes induced by testosterone propionate were observed by H&E and bromodeoxyuridinc (BrdU) stain to evaluate the therapeutic effect of LncRNA RP11-818O24.3 in C57BL/6 J mice. The cellular viability was analyzed in LncRNA RP11-818O24.3-transfected human hair-follicle stem cells (HFSCs) in vitro. The signaling pathways and pro-proliferative factors were investigated by transcriptomic gene sequencing and qRT-PCR. LncRNA RP11-818O24.3 transfection successfully recovered hair growth and hair-follicle cells in AGA mice. In a series of HFSC studies in vitro, LncRNA RP11-818O24.3 transfection greatly promoted cellular proliferation and decreased cellular apoptosis. Transcriptome gene sequencing suggested that the phosphatidylinositol 3-kinase (PI3K)-Akt pathway was upregulated by LncRNA RP11-818O24.3. The qRT-PCR results showed that fibroblast growth factor (FGF)-2 was 14-times upregulated after LncRNA RP11-818O24.3 transfection. Hair-follicle recovery activity of LncRNA RP11-818O24.3 may involve the upregulation of FGF2 and PI3K-Akt to promote follicle stem cell survival. These data not only provide a theoretical basis for AGA development but also reveal a novel therapeutic method for AGA patients. Supplementary Information The online version contains supplementary material available at 10.1007/s10616-024-00624-3.
Collapse
Affiliation(s)
- Linlin Bao
- Department of Dermatology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017, Dongmen North Road, Luohu District, Shenzhen, 518020 Guangdong China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020 Guangdong China
| | - Zhaojun Sun
- Department of Dermatology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017, Dongmen North Road, Luohu District, Shenzhen, 518020 Guangdong China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020 Guangdong China
| | - Lin Dang
- Department of Dermatology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017, Dongmen North Road, Luohu District, Shenzhen, 518020 Guangdong China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020 Guangdong China
| | - Qianqian Zhang
- Department of Dermatology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017, Dongmen North Road, Luohu District, Shenzhen, 518020 Guangdong China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020 Guangdong China
| | - Lixiong Zheng
- Department of Dermatology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017, Dongmen North Road, Luohu District, Shenzhen, 518020 Guangdong China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020 Guangdong China
| | - Fang Yang
- Department of Dermatology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017, Dongmen North Road, Luohu District, Shenzhen, 518020 Guangdong China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020 Guangdong China
| | - Jianglin Zhang
- Department of Dermatology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017, Dongmen North Road, Luohu District, Shenzhen, 518020 Guangdong China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020 Guangdong China
| |
Collapse
|
13
|
Liu X, Pang Y, Shan J, Wang Y, Zheng Y, Xue Y, Zhou X, Wang W, Sun Y, Yan X, Shi J, Wang X, Gu H, Zhang F. Beyond the base pairs: comparative genome-wide DNA methylation profiling across sequencing technologies. Brief Bioinform 2024; 25:bbae440. [PMID: 39256199 PMCID: PMC11387064 DOI: 10.1093/bib/bbae440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/28/2024] [Accepted: 08/21/2024] [Indexed: 09/12/2024] Open
Abstract
Deoxyribonucleic acid (DNA) methylation plays a key role in gene regulation and is critical for development and human disease. Techniques such as whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) allow DNA methylation analysis at the genome scale, with Illumina NovaSeq 6000 and MGI Tech DNBSEQ-T7 being popular due to their efficiency and affordability. However, detailed comparative studies of their performance are not available. In this study, we constructed 60 WGBS and RRBS libraries for two platforms using different types of clinical samples and generated approximately 2.8 terabases of sequencing data. We systematically compared quality control metrics, genomic coverage, CpG methylation levels, intra- and interplatform correlations, and performance in detecting differentially methylated positions. Our results revealed that the DNBSEQ platform exhibited better raw read quality, although base quality recalibration indicated potential overestimation of base quality. The DNBSEQ platform also showed lower sequencing depth and less coverage uniformity in GC-rich regions than did the NovaSeq platform and tended to enrich methylated regions. Overall, both platforms demonstrated robust intra- and interplatform reproducibility for RRBS and WGBS, with NovaSeq performing better for WGBS, highlighting the importance of considering these factors when selecting a platform for bisulfite sequencing.
Collapse
Affiliation(s)
- Xin Liu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui Province 230031, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui Province 230031, China
| | - Yu Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Junqi Shan
- Department of Gastrointestinal Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
| | - Yunfei Wang
- Hangzhou ShengTing Biotech Co. Ltd, Hangzhou, Zhejiang Province 310018, China
| | - Yanhua Zheng
- Department of Hematology, The First Hospital of China Medical University, Shenyang, Liaoning, Shenyang, Liaoning province 110001, China
| | - Yuhang Xue
- Department of Hematology, The First Hospital of China Medical University, Shenyang, Liaoning, Shenyang, Liaoning province 110001, China
| | - Xuerong Zhou
- Department of Hematology, The First Hospital of China Medical University, Shenyang, Liaoning, Shenyang, Liaoning province 110001, China
| | - Wenjun Wang
- Hangzhou ShengTing Biotech Co. Ltd, Hangzhou, Zhejiang Province 310018, China
| | - Yanlai Sun
- Department of Gastrointestinal Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
| | - Xiaojing Yan
- Department of Hematology, The First Hospital of China Medical University, Shenyang, Liaoning, Shenyang, Liaoning province 110001, China
| | - Jiantao Shi
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoxue Wang
- Department of Hematology, The First Hospital of China Medical University, Shenyang, Liaoning, Shenyang, Liaoning province 110001, China
| | - Hongcang Gu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui Province 230031, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui Province 230031, China
| | - Fan Zhang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui Province 230031, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui Province 230031, China
| |
Collapse
|
14
|
Ruprecht NA, Kennedy JD, Bansal B, Singhal S, Sens D, Maggio A, Doe V, Hawkins D, Campbel R, O’Connell K, Gill JS, Schaefer K, Singhal SK. Transcriptomics and epigenetic data integration learning module on Google Cloud. Brief Bioinform 2024; 25:bbae352. [PMID: 39101486 PMCID: PMC11299028 DOI: 10.1093/bib/bbae352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 05/12/2024] [Accepted: 07/06/2024] [Indexed: 08/06/2024] Open
Abstract
Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. HIGHLIGHTS
Collapse
Affiliation(s)
- Nathan A Ruprecht
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Joshua D Kennedy
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
- Department of Chemistry and Physics, Drury University, 900 N. Benton Avenue, Springfield, MO 65802, United States
| | - Benu Bansal
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Sonalika Singhal
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| | - Donald Sens
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| | - Angela Maggio
- Deloitte, Health Data and AI, Deloitte Consulting LLP, 1919 N. Lynn Street, Suite 1500, Arlington, VA 22209, United States
| | - Valena Doe
- Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Dale Hawkins
- Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Ross Campbel
- NIH Center for Information Technology (CIT), 6555 Rock Spring Drive, Bethesda, MD 20892, United States
| | - Kyle O’Connell
- NIH Center for Information Technology (CIT), 6555 Rock Spring Drive, Bethesda, MD 20892, United States
| | - Jappreet Singh Gill
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Kalli Schaefer
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Sandeep K Singhal
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| |
Collapse
|
15
|
Wang D, Liu Y, Zhang Y, Chen Q, Han Y, Hou W, Liu C, Yu Y, Li Z, Li Z, Zhao J, Shi L, Zheng Y, Li J, Zhang R. A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials. Nat Commun 2024; 15:6167. [PMID: 39039053 PMCID: PMC11263697 DOI: 10.1038/s41467-024-50420-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 07/02/2024] [Indexed: 07/24/2024] Open
Abstract
Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of 'ground truth', we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes.
Collapse
Affiliation(s)
- Duo Wang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanfeng Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yanxi Han
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Cong Liu
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ziyang Li
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, PR China
| | - Ziqiang Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Jiaxin Zhao
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China.
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, PR China.
- National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
- Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| |
Collapse
|
16
|
Apostolides M, Choi B, Navickas A, Saberi A, Soto LM, Goodarzi H, Najafabadi HS. Accurate isoform quantification by joint short- and long-read RNA-sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.11.603067. [PMID: 39026819 PMCID: PMC11257535 DOI: 10.1101/2024.07.11.603067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.
Collapse
Affiliation(s)
- Michael Apostolides
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Victor P. Dahdaleh Institute of Genomic Medicine, Montreal, QC, Canada
| | - Benedict Choi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Albertas Navickas
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Present address: Institut Curie, PSL Research University, CNRS UMR3348, INSERM U1278, Orsay, France
| | - Ali Saberi
- Victor P. Dahdaleh Institute of Genomic Medicine, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, McGill University, Montreal, Canada
| | - Larisa M. Soto
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Victor P. Dahdaleh Institute of Genomic Medicine, Montreal, QC, Canada
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, 3181 Porter Drive, Palo Alto, CA, USA
| | - Hamed S. Najafabadi
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Victor P. Dahdaleh Institute of Genomic Medicine, Montreal, QC, Canada
- McGill Centre for RNA Sciences, McGill University, Montreal, Canada
| |
Collapse
|
17
|
Chepeleva M, Kaoma T, Zinovyev A, Toth R, Nazarov PV. consICA: an R package for robust reference-free deconvolution of multi-omics data. BIOINFORMATICS ADVANCES 2024; 4:vbae102. [PMID: 39027644 PMCID: PMC11257712 DOI: 10.1093/bioadv/vbae102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 06/25/2024] [Accepted: 07/12/2024] [Indexed: 07/20/2024]
Abstract
Motivation Deciphering molecular signals from omics data helps understanding cellular processes and disease progression. Effective algorithms for extracting these signals are essential, with a strong emphasis on robustness and reproducibility. Results R/Bioconductor package consICA implements consensus independent component analysis (ICA)-a data-driven deconvolution method to decompose heterogeneous omics data and extract features suitable for patient stratification and multimodal data integration. The method separates biologically relevant molecular signals from technical effects and provides information about the cellular composition and biological processes. Build-in annotation, survival analysis, and report generation provide useful tools for the interpretation of extracted signals. The implementation of parallel computing in the package ensures efficient analysis using modern multicore systems. The package offers a reproducible and efficient data-driven solution for the analysis of complex molecular profiles, with significant implications for cancer research. Availability and implementation The package is implemented in R and available under MIT license at Bioconductor (https://bioconductor.org/packages/consICA) or at GitHub (https://github.com/biomod-lih/consICA).
Collapse
Affiliation(s)
- Maryna Chepeleva
- Multiomics Data Science Research Group, Department of Cancer Research, Luxembourg Institute of Health, Strassen L-1445, Luxembourg
- Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette L-4365, Luxembourg
| | - Tony Kaoma
- Bioinformatics and AI Unit, Department of Medical Informatics, Luxembourg Institute of Health, Strassen L-1445, Luxembourg
| | | | - Reka Toth
- Multiomics Data Science Research Group, Department of Cancer Research, Luxembourg Institute of Health, Strassen L-1445, Luxembourg
- Bioinformatics and AI Unit, Department of Medical Informatics, Luxembourg Institute of Health, Strassen L-1445, Luxembourg
| | - Petr V Nazarov
- Multiomics Data Science Research Group, Department of Cancer Research, Luxembourg Institute of Health, Strassen L-1445, Luxembourg
- Bioinformatics and AI Unit, Department of Medical Informatics, Luxembourg Institute of Health, Strassen L-1445, Luxembourg
| |
Collapse
|
18
|
Yu Y, Hou W, Liu Y, Wang H, Dong L, Mai Y, Chen Q, Li Z, Sun S, Yang J, Cao Z, Zhang P, Zi Y, Liu R, Gao J, Zhang N, Li J, Ren L, Jiang H, Shang J, Zhu S, Wang X, Qing T, Bao D, Li B, Li B, Suo C, Pi Y, Wang X, Dai F, Scherer A, Mattila P, Han J, Zhang L, Jiang H, Thierry-Mieg D, Thierry-Mieg J, Xiao W, Hong H, Tong W, Wang J, Li J, Fang X, Jin L, Xu J, Qian F, Zhang R, Shi L, Zheng Y. Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling. Nat Biotechnol 2024; 42:1118-1132. [PMID: 37679545 PMCID: PMC11251996 DOI: 10.1038/s41587-023-01867-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 06/15/2023] [Indexed: 09/09/2023]
Abstract
Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.
Collapse
Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhihui Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yi Zi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jian Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Chen Suo
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yan Pi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xia Wang
- National Institute of Metrology, Beijing, China
| | | | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | - Pirkko Mattila
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | | | - Lijun Zhang
- Nanjing Vazyme Biotech Co. Ltd., Nanjing, China
| | | | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
- National Center of Gerontology, Beijing, China
| | - Xiang Fang
- National Institute of Metrology, Beijing, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
| | - Feng Qian
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
- National Center of Gerontology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
| |
Collapse
|
19
|
Dong L, Zhang Y, Fu B, Swart C, Jiang H, Liu Y, Huggett J, Wielgosz R, Niu C, Li Q, Zhang Y, Park SR, Sui Z, Yu L, Liu Y, Xie Q, Zhang H, Yang Y, Dai X, Shi L, Yin Y, Fang X. Reliable biological and multi-omics research through biometrology. Anal Bioanal Chem 2024; 416:3645-3663. [PMID: 38507042 DOI: 10.1007/s00216-024-05239-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 03/22/2024]
Abstract
Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.
Collapse
Affiliation(s)
- Lianhua Dong
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
| | - Yu Zhang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Boqiang Fu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Claudia Swart
- Physikalisch-Technische Bundesanstalt, 38116, Braunschweig, Germany
| | | | - Yahui Liu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Jim Huggett
- National Measurement Laboratory at LGC (NML), Teddington, Middlesex, UK
| | - Robert Wielgosz
- Bureau International Des Poids Et Mesures (BIPM), Pavillon de Breteuil, 92312, Sèvres Cedex, France
| | - Chunyan Niu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Qianyi Li
- BGI, BGI-Shenzhen, Shenzhen, 518083, China
| | - Yongzhuo Zhang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Sang-Ryoul Park
- Korea Research Institute of Standards and Science, Daejeon, Republic of Korea
| | - Zhiwei Sui
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Lianchao Yu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | | | - Qing Xie
- BGI, BGI-Shenzhen, Shenzhen, 518083, China
| | - Hongfu Zhang
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Xinhua Dai
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Ye Yin
- BGI, BGI-Shenzhen, Shenzhen, 518083, China.
| | - Xiang Fang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
| |
Collapse
|
20
|
Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nat Biotechnol 2024; 42:1133-1149. [PMID: 37679543 PMCID: PMC11252085 DOI: 10.1038/s41587-023-01934-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.
Collapse
Affiliation(s)
- Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | | | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Feng Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Ling Lin
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Jingwei Lou
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Ruiqiang Li
- Novogene Bioinformatics Institute, Beijing, China
| | - Jingchao Lin
- Metabo-Profile Biotechnology (Shanghai) Co. Ltd., Shanghai, China
| | | | | | - Depeng Wang
- Nextomics Biosciences Institute, Wuhan, China
| | | | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Yinbo Huo
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Gang Liu
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chengming Cao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Li Shao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xiaozhen Liang
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weida Tong
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
| |
Collapse
|
21
|
Hosen S, Ikeda-Yorifuji I, Yamashita T. Asporin and CD109, expressed in the injured neonatal spinal cord, attenuate axonal re-growth in vitro. Neurosci Lett 2024; 833:137832. [PMID: 38796094 DOI: 10.1016/j.neulet.2024.137832] [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: 01/26/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
Axonal regeneration is restricted in adults and causes irreversible motor dysfunction following spinal cord injury (SCI). In contrast, neonates have prominent regenerative potential and can restore their neural function. Although the distinct cellular responses in neonates have been studied, how they contribute to neural recovery remains unclear. To assess whether the secreted molecules in neonatal SCI can enhance neural regeneration, we re-analyzed the previously performed single-nucleus RNA-seq (snRNA-seq) and focused on Asporin and Cd109, the highly expressed genes in the injured neonatal spinal cord. In the present study, we showed that both these molecules were expressed in the injured spinal cords of adults and neonates. We treated the cortical neurons with recombinant Asporin or CD109 to observe their direct effects on neurons in vitro. We demonstrated that these molecules enhance neurite outgrowth in neurons. However, these molecules did not enhance re-growth of severed axons. Our results suggest that Asporin and CD109 influence neurites at the lesion site, rather than promoting axon regeneration, to restore neural function in neonates after SCI.
Collapse
Affiliation(s)
- Sakura Hosen
- Department of Molecular Neuroscience, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Iyo Ikeda-Yorifuji
- Department of Molecular Neuroscience, Graduate School of Medicine, Osaka University, Osaka, Japan.
| | - Toshihide Yamashita
- Department of Molecular Neuroscience, Graduate School of Medicine, Osaka University, Osaka, Japan; WPI Immunology Frontier Research Center, Osaka University, Suita, Japan; Department of Molecular Neuroscience, Graduate School of Frontier Biosciences, Osaka University, Suita, Japan; Department of Neuro-Medical Science, Graduate School of Medicine, Osaka University, Suita, Japan.
| |
Collapse
|
22
|
Li X, Liao C, Wu J, Yi B, Zha R, Deng Q, Xu J, Guo C, Lu J. Distinct serum exosomal miRNA profiles detected in acute and asymptomatic dengue infections: A community-based study in Baiyun District, Guangzhou. Heliyon 2024; 10:e31546. [PMID: 38807894 PMCID: PMC11130723 DOI: 10.1016/j.heliyon.2024.e31546] [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: 01/22/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/30/2024] Open
Abstract
Background In recent years, research on exosomal miRNAs has provided new insights into exploring the mechanism of viral infection and disease prevention. This study aimed to investigate the serum exosomal miRNA expression profile of dengue-infected individuals through a community survey of dengue virus (DENV) infection. Methods A seroprevalence study of 1253 healthy persons was first conducted to ascertain the DENV infection status in Baiyun District, Guangzhou. A total of 18 serum samples, including 6 healthy controls (HC), 6 asymptomatic DENV infections (AsymptDI), and 6 confirmed dengue fever patients (AcuteDI), were collected for exosome isolation and then sRNA sequencing. Through bioinformatics analysis, we discovered distinct serum exosomal miRNA profiles among the different groups and identified differentially expressed miRNAs (DEMs). These findings were further validated by qRT-PCR. Results The community survey of DENV infection indicated that the DENV IgG antibody positivity rate among the population was 11.97 % in the study area, with asymptomatic infected individuals accounting for 93.06 % of the anti-DENV IgG positives. The age and Guangzhou household registration were associated with DENV IgG antibody positivity by logistic regression analysis. Distinct miRNA profiles were observed between healthy individuals and DENV infections. A total of 1854 miRNAs were identified in 18 serum exosome samples from the initial analysis of the sequencing data. Comparative analysis revealed 23 DEMs comprising 5 upregulated and 18 downregulated miRNAs in the DENV-infected group (mergedDI). In comparison to AcuteDI, 18 upregulated miRNAs were identified in AsymptDI. Moreover, functional enrichment of the predicted target genes of DEMs indicated that these miRNAs were involved in biological processes and pathways related to cell adhesion, focal adhesion, endocytosis, and ECM-receptor interaction. Eight DEMs were validated by qRT-PCR. Conclusion The Baiyun District of Guangzhou exhibits a notable proportion of asymptomatic DENV infections as suggested in other research, highlighting the need for enhanced monitoring and screening of asymptomatic persons and the elderly. Differential miRNA expression among healthy, symptomatic and asymptomatic DENV-infected individuals suggests their potential as biomarkers for distinguishing DENV infection and offers new avenues of investigating the mechanisms underlying DENV asymptomatic infections.
Collapse
Affiliation(s)
- Xiaokang Li
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Conghui Liao
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jiani Wu
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Boyang Yi
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Renyun Zha
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Qiang Deng
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jianhua Xu
- Guangzhou Baiyun District Center for Disease Control and Prevention, Guangzhou, 510445, China
| | - Cheng Guo
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jiahai Lu
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
- National Medical Products Administration Key Laboratory for Quality Monitoring and Evaluation of Vaccines and Biological Products, Guangzhou, 510080, China
- Hainan Key Novel Thinktank “Hainan Medical University ‘One Health’ Research Center”, Haikou, 571199, China
- Research Institute of Sun Yat-Sen University in Shenzhen, Shenzhen, 518057, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-Sen University, Ministry of Education, Guangzhou, 510080, China
| |
Collapse
|
23
|
Su Y, Yu Z, Jin S, Ai Z, Yuan R, Chen X, Xue Z, Guo Y, Chen D, Liang H, Liu Z, Liu W. Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data. Nat Commun 2024; 15:3972. [PMID: 38730241 PMCID: PMC11087464 DOI: 10.1038/s41467-024-48117-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
The advancement of Long-Read Sequencing (LRS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing events and isoform expressions. Recently, numerous computational tools for isoform detection using long-read sequencing data have been developed. Nevertheless, there remains a deficiency in comparative studies that systemically evaluate the performance of these tools, which are implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmark analysis of thirteen methods implemented in nine tools capable of identifying isoform structures from long-read RNA-seq data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, RNA sequins (sequencing spike-ins) data, as well as experimental data. Our findings demonstrate IsoQuant as a highly effective tool for isoform detection with LRS, with Bambu and StringTie2 also exhibiting strong performance. These results offer valuable guidance for future research on alternative splicing analysis and the ongoing improvement of tools for isoform detection using LRS data.
Collapse
Affiliation(s)
- Yaqi Su
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
| | - Zhejian Yu
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Siqian Jin
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Zhipeng Ai
- Division of Human Reproduction and Developmental Genetics, Women's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Ruihong Yuan
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Xinyi Chen
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Ziwei Xue
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Yixin Guo
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Di Chen
- Center for Reproductive Medicine of the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China
- Centre for Regeneration and Cell Therapy of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Hongqing Liang
- Division of Human Reproduction and Developmental Genetics, Women's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Zuozhu Liu
- Zhejiang University-Angel Align Inc. R&D Center for Intelligent Healthcare, Zhejiang University-University of Illinois at Urbana-Champaign Institute (ZJU-UIUC Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China
| | - Wanlu Liu
- Department of Orthopedic Surgery of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China.
- Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), International Campus, Zhejiang University, Haining, 314400, Zhejiang, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314100, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
| |
Collapse
|
24
|
Saldana-Guerrero IM, Montano-Gutierrez LF, Boswell K, Hafemeister C, Poon E, Shaw LE, Stavish D, Lea RA, Wernig-Zorc S, Bozsaky E, Fetahu IS, Zoescher P, Pötschger U, Bernkopf M, Wenninger-Weinzierl A, Sturtzel C, Souilhol C, Tarelli S, Shoeb MR, Bozatzi P, Rados M, Guarini M, Buri MC, Weninger W, Putz EM, Huang M, Ladenstein R, Andrews PW, Barbaric I, Cresswell GD, Bryant HE, Distel M, Chesler L, Taschner-Mandl S, Farlik M, Tsakiridis A, Halbritter F. A human neural crest model reveals the developmental impact of neuroblastoma-associated chromosomal aberrations. Nat Commun 2024; 15:3745. [PMID: 38702304 PMCID: PMC11068915 DOI: 10.1038/s41467-024-47945-7] [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: 01/06/2023] [Accepted: 04/15/2024] [Indexed: 05/06/2024] Open
Abstract
Early childhood tumours arise from transformed embryonic cells, which often carry large copy number alterations (CNA). However, it remains unclear how CNAs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ female human embryonic stem cell (hESC) differentiation and single-cell transcriptome and epigenome analysis to assess the effects of chromosome 17q/1q gains, which are prevalent in the embryonal tumour neuroblastoma (NB). We show that CNAs impair the specification of trunk neural crest (NC) cells and their sympathoadrenal derivatives, the putative cells-of-origin of NB. This effect is exacerbated upon overexpression of MYCN, whose amplification co-occurs with CNAs in NB. Moreover, CNAs potentiate the pro-tumourigenic effects of MYCN and mutant NC cells resemble NB cells in tumours. These changes correlate with a stepwise aberration of developmental transcription factor networks. Together, our results sketch a mechanistic framework for the CNA-driven initiation of embryonal tumours.
Collapse
Affiliation(s)
- Ingrid M Saldana-Guerrero
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
- Sheffield Institute for Nucleic Acids (SInFoNiA), School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
| | | | - Katy Boswell
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | | | - Evon Poon
- Division of Clinical Studies, The Institute of Cancer Research (ICR) & Royal Marsden NHS Trust, London, UK
| | - Lisa E Shaw
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Dylan Stavish
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Rebecca A Lea
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Sara Wernig-Zorc
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Eva Bozsaky
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Irfete S Fetahu
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
- Medical University of Vienna, Department of Neurology, Division of Neuropathology and Neurochemistry, Vienna, Austria
| | - Peter Zoescher
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Ulrike Pötschger
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Marie Bernkopf
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
- Labdia Labordiagnostik GmbH, Vienna, Austria
| | | | - Caterina Sturtzel
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Celine Souilhol
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Sheffield Hallam University, Sheffield, UK
| | - Sophia Tarelli
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Mohamed R Shoeb
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Polyxeni Bozatzi
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Magdalena Rados
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Maria Guarini
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Michelle C Buri
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Wolfgang Weninger
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Eva M Putz
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Miller Huang
- Children's Hospital Los Angeles, Cancer and Blood Disease Institutes, and The Saban Research Institute, Los Angeles, CA, USA
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ruth Ladenstein
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Peter W Andrews
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
| | - Ivana Barbaric
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | | | - Helen E Bryant
- Sheffield Institute for Nucleic Acids (SInFoNiA), School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
| | - Martin Distel
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Louis Chesler
- Division of Clinical Studies, The Institute of Cancer Research (ICR) & Royal Marsden NHS Trust, London, UK
| | | | - Matthias Farlik
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Anestis Tsakiridis
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK.
- Neuroscience Institute, The University of Sheffield, Sheffield, UK.
| | | |
Collapse
|
25
|
Singh V, Kirtipal N, Song B, Lee S. Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference. Brief Bioinform 2024; 25:bbae241. [PMID: 38770720 PMCID: PMC11107385 DOI: 10.1093/bib/bbae241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/04/2024] [Accepted: 05/07/2024] [Indexed: 05/22/2024] Open
Abstract
The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel's Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression.
Collapse
Affiliation(s)
- Vikas Singh
- School of Life Sciences, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, 61005, Gwangju, South Korea
| | - Nikhil Kirtipal
- School of Life Sciences, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, 61005, Gwangju, South Korea
| | - Byeongsop Song
- School of Life Sciences, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, 61005, Gwangju, South Korea
| | - Sunjae Lee
- School of Life Sciences, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, 61005, Gwangju, South Korea
| |
Collapse
|
26
|
Abu-Zaid A, Fang J, Jin H, Singh S, Pichavaram P, Wu Q, Tillman H, Janke L, Rosikiewicz W, Xu B, Van De Velde LA, Guo Y, Li Y, Shendy NAM, Delahunty IM, Rankovic Z, Chen T, Chen X, Freeman KW, Hatley ME, Durbin AD, Murray PJ, Murphy AJ, Thomas PG, Davidoff AM, Yang J. Histone lysine demethylase 4 family proteins maintain the transcriptional program and adrenergic cellular state of MYCN-amplified neuroblastoma. Cell Rep Med 2024; 5:101468. [PMID: 38508144 PMCID: PMC10983111 DOI: 10.1016/j.xcrm.2024.101468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 12/21/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Neuroblastoma with MYCN amplification (MNA) is a high-risk disease that has a poor survival rate. Neuroblastoma displays cellular heterogeneity, including more differentiated (adrenergic) and more primitive (mesenchymal) cellular states. Here, we demonstrate that MYCN oncoprotein promotes a cellular state switch in mesenchymal cells to an adrenergic state, accompanied by induction of histone lysine demethylase 4 family members (KDM4A-C) that act in concert to control the expression of MYCN and adrenergic core regulatory circulatory (CRC) transcription factors. Pharmacologic inhibition of KDM4 blocks expression of MYCN and the adrenergic CRC transcriptome with genome-wide induction of transcriptionally repressive H3K9me3, resulting in potent anticancer activity against neuroblastomas with MNA by inducing neuroblastic differentiation and apoptosis. Furthermore, a short-term KDM4 inhibition in combination with conventional, cytotoxic chemotherapy results in complete tumor responses of xenografts with MNA. Thus, KDM4 blockade may serve as a transformative strategy to target the adrenergic CRC dependencies in MNA neuroblastomas.
Collapse
Affiliation(s)
- Ahmed Abu-Zaid
- Department of Surgery, St Jude Children's Research Hospital, Memphis, TN 38105, USA; College of Graduate Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jie Fang
- Department of Surgery, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Hongjian Jin
- Center for Applied Bioinformatics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Shivendra Singh
- Department of Surgery, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | | | - Qiong Wu
- Department of Surgery, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Heather Tillman
- Department of Pathology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Laura Janke
- Department of Pathology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Wojciech Rosikiewicz
- Center for Applied Bioinformatics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Beisi Xu
- Center for Applied Bioinformatics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Lee-Ann Van De Velde
- Department of Immunology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Yian Guo
- Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Yimei Li
- Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Noha A M Shendy
- Department of Molecular Oncology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Ian M Delahunty
- Department of Molecular Oncology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Zoran Rankovic
- Department of Chemical Biology and Therapeutics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Taosheng Chen
- Department of Chemical Biology and Therapeutics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Xiang Chen
- Department of Computational Biology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Kevin W Freeman
- Genetics, Genomics & Informatics, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
| | - Mark E Hatley
- Department of Molecular Oncology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Adam D Durbin
- Department of Molecular Oncology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Peter J Murray
- Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Andrew J Murphy
- Department of Surgery, St Jude Children's Research Hospital, Memphis, TN 38105, USA; Division of Pediatric Surgery, Department of Surgery, University of Tennessee Health Science Center, Memphis, TN 38105, USA
| | - Paul G Thomas
- Department of Immunology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Andrew M Davidoff
- Department of Surgery, St Jude Children's Research Hospital, Memphis, TN 38105, USA; Division of Pediatric Surgery, Department of Surgery, University of Tennessee Health Science Center, Memphis, TN 38105, USA; St Jude Graduate School of Biomedical Sciences, St Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Pathology and Laboratory Medicine, College of Medicine, The University of Tennessee Health Science Center, 930 Madison Avenue, Suite 500, Memphis, TN 38163, USA
| | - Jun Yang
- Department of Surgery, St Jude Children's Research Hospital, Memphis, TN 38105, USA; St Jude Graduate School of Biomedical Sciences, St Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Pathology and Laboratory Medicine, College of Medicine, The University of Tennessee Health Science Center, 930 Madison Avenue, Suite 500, Memphis, TN 38163, USA; College of Graduate Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA.
| |
Collapse
|
27
|
Kim WJ, Choi BR, Noh JJ, Lee YY, Kim TJ, Lee JW, Kim BG, Choi CH. Comparison of RNA-Seq and microarray in the prediction of protein expression and survival prediction. Front Genet 2024; 15:1342021. [PMID: 38463169 PMCID: PMC10920353 DOI: 10.3389/fgene.2024.1342021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/12/2024] [Indexed: 03/12/2024] Open
Abstract
Gene expression profiling using RNA-sequencing (RNA-seq) and microarray technologies is widely used in cancer research to identify biomarkers for clinical endpoint prediction. We compared the performance of these two methods in predicting protein expression and clinical endpoints using The Cancer Genome Atlas (TCGA) datasets of lung cancer, colorectal cancer, renal cancer, breast cancer, endometrial cancer, and ovarian cancer. We calculated the correlation coefficients between gene expression measured by RNA-seq or microarray and protein expression measured by reverse phase protein array (RPPA). In addition, after selecting the top 103 survival-related genes, we compared the random forest survival prediction model performance across test platforms and cancer types. Both RNA-seq and microarray data were retrieved from TCGA dataset. Most genes showed similar correlation coefficients between RNA-seq and microarray, but 16 genes exhibited significant differences between the two methods. The BAX gene was recurrently found in colorectal cancer, renal cancer, and ovarian cancer, and the PIK3CA gene belonged to renal cancer and breast cancer. Furthermore, the survival prediction model using microarray was better than the RNA-seq model in colorectal cancer, renal cancer, and lung cancer, but the RNA-seq model was better in ovarian and endometrial cancer. Our results showed good correlation between mRNA levels and protein measured by RPPA. While RNA-seq and microarray performance were similar, some genes showed differences, and further clinical significance should be evaluated. Additionally, our survival prediction model results were controversial.
Collapse
Affiliation(s)
- Won-Ji Kim
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Bo Ram Choi
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Joseph J Noh
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yoo-Young Lee
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Tae-Joong Kim
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeong-Won Lee
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Byoung-Gie Kim
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Chel Hun Choi
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| |
Collapse
|
28
|
Wienke J, Visser LL, Kholosy WM, Keller KM, Barisa M, Poon E, Munnings-Tomes S, Himsworth C, Calton E, Rodriguez A, Bernardi R, van den Ham F, van Hooff SR, Matser YAH, Tas ML, Langenberg KPS, Lijnzaad P, Borst AL, Zappa E, Bergsma FJ, Strijker JGM, Verhoeven BM, Mei S, Kramdi A, Restuadi R, Sanchez-Bernabeu A, Cornel AM, Holstege FCP, Gray JC, Tytgat GAM, Scheijde-Vermeulen MA, Wijnen MHWA, Dierselhuis MP, Straathof K, Behjati S, Wu W, Heck AJR, Koster J, Nierkens S, Janoueix-Lerosey I, de Krijger RR, Baryawno N, Chesler L, Anderson J, Caron HN, Margaritis T, van Noesel MM, Molenaar JJ. Integrative analysis of neuroblastoma by single-cell RNA sequencing identifies the NECTIN2-TIGIT axis as a target for immunotherapy. Cancer Cell 2024; 42:283-300.e8. [PMID: 38181797 PMCID: PMC10864003 DOI: 10.1016/j.ccell.2023.12.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 11/10/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024]
Abstract
Pediatric patients with high-risk neuroblastoma have poor survival rates and urgently need more effective treatment options with less side effects. Since novel and improved immunotherapies may fill this need, we dissect the immunoregulatory interactions in neuroblastoma by single-cell RNA-sequencing of 24 tumors (10 pre- and 14 post-chemotherapy, including 5 pairs) to identify strategies for optimizing immunotherapy efficacy. Neuroblastomas are infiltrated by natural killer (NK), T and B cells, and immunosuppressive myeloid populations. NK cells show reduced cytotoxicity and T cells have a dysfunctional profile. Interaction analysis reveals a vast immunoregulatory network and identifies NECTIN2-TIGIT as a crucial immune checkpoint. Combined blockade of TIGIT and PD-L1 significantly reduces neuroblastoma growth, with complete responses (CR) in vivo. Moreover, addition of TIGIT+PD-L1 blockade to standard relapse treatment in a chemotherapy-resistant Th-ALKF1174L/MYCN 129/SvJ syngeneic model induces CR. In conclusion, our integrative analysis provides promising targets and a rationale for immunotherapeutic combination strategies.
Collapse
Affiliation(s)
- Judith Wienke
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.
| | - Lindy L Visser
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Waleed M Kholosy
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Kaylee M Keller
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Marta Barisa
- Cancer Section, Developmental Biology and Cancer Programme, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Evon Poon
- Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | - Sophie Munnings-Tomes
- Cancer Section, Developmental Biology and Cancer Programme, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Courtney Himsworth
- Cancer Section, Developmental Biology and Cancer Programme, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Elizabeth Calton
- Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | | | - Ronald Bernardi
- Genentech, A Member of the Roche Group, South San Francisco, CA, USA
| | - Femke van den Ham
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | | | - Yvette A H Matser
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Michelle L Tas
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | | | - Philip Lijnzaad
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Anne L Borst
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Elisa Zappa
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | | | | | - Bronte M Verhoeven
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Shenglin Mei
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Amira Kramdi
- Institut Curie, Inserm U830, PSL Research University, Diversity and Plasticity of Childhood Tumors Lab, Paris, France; SIREDO: Care, Innovation and Research for Children, Adolescents and Young Adults with Cancer, Institut Curie, Paris, France
| | - Restuadi Restuadi
- Infection, Immunity and Inflammation Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK; NIHR Biomedical Research Centre, Great Ormond Street Hospital, London, UK
| | - Alvaro Sanchez-Bernabeu
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; Netherlands Proteomics Centre, Utrecht University, Utrecht, the Netherlands
| | - Annelisa M Cornel
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands; Center for Translational Immunology, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Juliet C Gray
- Centre for Cancer Immunology, University of Southampton, Southampton, UK
| | | | | | - Marc H W A Wijnen
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | | | - Karin Straathof
- University College London (UCL) Great Ormond Street Institute of Child Health, London, UK; UCL Cancer Institute, London, UK
| | - Sam Behjati
- Wellcome Sanger Institute, Hinxton, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Wei Wu
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; Netherlands Proteomics Centre, Utrecht University, Utrecht, the Netherlands; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; Netherlands Proteomics Centre, Utrecht University, Utrecht, the Netherlands
| | - Jan Koster
- Amsterdam UMC location University of Amsterdam, Center for Experimental and Molecular Medicine, Amsterdam, the Netherlands
| | - Stefan Nierkens
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands; Center for Translational Immunology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Isabelle Janoueix-Lerosey
- Institut Curie, Inserm U830, PSL Research University, Diversity and Plasticity of Childhood Tumors Lab, Paris, France; SIREDO: Care, Innovation and Research for Children, Adolescents and Young Adults with Cancer, Institut Curie, Paris, France
| | - Ronald R de Krijger
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ninib Baryawno
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Louis Chesler
- Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | - John Anderson
- Cancer Section, Developmental Biology and Cancer Programme, UCL Great Ormond Street Institute of Child Health, London, UK; Department of Oncology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, England, UK
| | | | | | - Max M van Noesel
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands; Division Imaging & Cancer, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jan J Molenaar
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands; Department of Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands
| |
Collapse
|
29
|
Brunner TM, Serve S, Marx AF, Fadejeva J, Saikali P, Dzamukova M, Durán-Hernández N, Kommer C, Heinrich F, Durek P, Heinz GA, Höfer T, Mashreghi MF, Kühn R, Pinschewer DD, Löhning M. A type 1 immunity-restricted promoter of the IL-33 receptor gene directs antiviral T-cell responses. Nat Immunol 2024; 25:256-267. [PMID: 38172258 PMCID: PMC10834369 DOI: 10.1038/s41590-023-01697-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 11/02/2023] [Indexed: 01/05/2024]
Abstract
The pleiotropic alarmin interleukin-33 (IL-33) drives type 1, type 2 and regulatory T-cell responses via its receptor ST2. Subset-specific differences in ST2 expression intensity and dynamics suggest that transcriptional regulation is key in orchestrating the context-dependent activity of IL-33-ST2 signaling in T-cell immunity. Here, we identify a previously unrecognized alternative promoter in mice and humans that is located far upstream of the curated ST2-coding gene and drives ST2 expression in type 1 immunity. Mice lacking this promoter exhibit a selective loss of ST2 expression in type 1- but not type 2-biased T cells, resulting in impaired expansion of cytotoxic T cells (CTLs) and T-helper 1 cells upon viral infection. T-cell-intrinsic IL-33 signaling via type 1 promoter-driven ST2 is critical to generate a clonally diverse population of antiviral short-lived effector CTLs. Thus, lineage-specific alternative promoter usage directs alarmin responsiveness in T-cell subsets and offers opportunities for immune cell-specific targeting of the IL-33-ST2 axis in infections and inflammatory diseases.
Collapse
Affiliation(s)
- Tobias M Brunner
- Experimental Immunology and Osteoarthritis Research, Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Pitzer Laboratory of Osteoarthritis Research, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany.
| | - Sebastian Serve
- Experimental Immunology and Osteoarthritis Research, Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pitzer Laboratory of Osteoarthritis Research, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany
| | - Anna-Friederike Marx
- Division of Experimental Virology, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Jelizaveta Fadejeva
- Experimental Immunology and Osteoarthritis Research, Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pitzer Laboratory of Osteoarthritis Research, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Philippe Saikali
- Experimental Immunology and Osteoarthritis Research, Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pitzer Laboratory of Osteoarthritis Research, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Maria Dzamukova
- Experimental Immunology and Osteoarthritis Research, Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pitzer Laboratory of Osteoarthritis Research, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Nayar Durán-Hernández
- Experimental Immunology and Osteoarthritis Research, Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pitzer Laboratory of Osteoarthritis Research, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Christoph Kommer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- BioQuant Center, University of Heidelberg, Heidelberg, Germany
| | - Frederik Heinrich
- Therapeutic Gene Regulation, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Pawel Durek
- Therapeutic Gene Regulation, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Gitta A Heinz
- Therapeutic Gene Regulation, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- BioQuant Center, University of Heidelberg, Heidelberg, Germany
| | - Mir-Farzin Mashreghi
- Therapeutic Gene Regulation, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
| | - Ralf Kühn
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Daniel D Pinschewer
- Division of Experimental Virology, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Max Löhning
- Experimental Immunology and Osteoarthritis Research, Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Pitzer Laboratory of Osteoarthritis Research, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany.
| |
Collapse
|
30
|
Whittle BJ, Izuogu OG, Lowes H, Deen D, Pyle A, Coxhead J, Lawson RA, Yarnall AJ, Jackson MS, Santibanez-Koref M, Hudson G. Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression. NPJ Parkinsons Dis 2024; 10:25. [PMID: 38245550 PMCID: PMC10799891 DOI: 10.1038/s41531-024-00636-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.
Collapse
Affiliation(s)
- Benjamin J Whittle
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Osagie G Izuogu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Hannah Lowes
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Dasha Deen
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Angela Pyle
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Jon Coxhead
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael A Lawson
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Michael S Jackson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Gavin Hudson
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
| |
Collapse
|
31
|
Gharibi H, Ashkarran AA, Jafari M, Voke E, Landry MP, Saei AA, Mahmoudi M. A uniform data processing pipeline enables harmonized nanoparticle protein corona analysis across proteomics core facilities. Nat Commun 2024; 15:342. [PMID: 38184668 PMCID: PMC10771434 DOI: 10.1038/s41467-023-44678-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/20/2023] [Indexed: 01/08/2024] Open
Abstract
Protein corona, a layer of biomolecules primarily comprising proteins, forms dynamically on nanoparticles in biological fluids and is crucial for predicting nanomedicine safety and efficacy. The protein composition of the corona layer is typically analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Our recent study, involving identical samples analyzed by 17 proteomics facilities, highlighted significant data variability, with only 1.8% of proteins consistently identified across these centers. Here, we implement an aggregated database search unifying parameters such as variable modifications, enzyme specificity, number of allowed missed cleavages and a stringent 1% false discovery rate at the protein and peptide levels. Such uniform search dramatically harmonizes the proteomics data, increasing the reproducibility and the percentage of consistency-identified unique proteins across distinct cores. Specifically, out of the 717 quantified proteins, 253 (35.3%) are shared among the top 5 facilities (and 16.2% among top 11 facilities). Furthermore, we note that reduction and alkylation are important steps in protein corona sample processing and as expected, omitting these steps reduces the number of total quantified peptides by around 20%. These findings underscore the need for standardized procedures in protein corona analysis, which is vital for advancing clinical applications of nanoscale biotechnologies.
Collapse
Affiliation(s)
- Hassan Gharibi
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Ali Akbar Ashkarran
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI, USA
| | - Maryam Jafari
- Division of ENT Diseases, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Elizabeth Voke
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA
| | - Markita P Landry
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA
- Innovative Genomics Institute, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Amir Ata Saei
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17165, Sweden.
- Biozentrum, University of Basel, 4056, Basel, Switzerland.
| | - Morteza Mahmoudi
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI, USA.
| |
Collapse
|
32
|
Che S, Pham PH, Barbut S, Bienzle D, Susta L. Transcriptomic Profiles of Pectoralis major Muscles Affected by Spaghetti Meat and Woody Breast in Broiler Chickens. Animals (Basel) 2024; 14:176. [PMID: 38254345 PMCID: PMC10812457 DOI: 10.3390/ani14020176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Spaghetti meat (SM) and woody breast (WB) are breast muscle myopathies of broiler chickens, characterized by separation of myofibers and by fibrosis, respectively. This study sought to investigate the transcriptomic profiles of breast muscles affected by SM and WB. Targeted sampling was conducted on a flock to obtain 10 WB, 10 SM, and 10 Normal Pectoralis major muscle samples from 37-day-old male chickens. Total RNA was extracted, cDNA was used for pair-end sequencing, and differentially expressed genes (DEGs) were determined by a false discovery rate of <0.1 and a >1.5-fold change. Principal component and heatmap cluster analyses showed that the SM and WB samples clustered together. No DEGs were observed between SM and WB fillets, while a total of 4018 and 2323 DEGs were found when comparing SM and WB, respectively, against Normal samples. In both the SM and WB samples, Gene Ontology terms associated with extracellular environment and immune response were enriched. The KEGG analysis showed enrichment of cytokine-cytokine receptor interaction and extracellular matrix-receptor interaction pathways in both myopathies. Although SM and WB are macroscopically different, the similar transcriptomic profiles suggest that these conditions may share a common pathogenesis. This is the first study to compare the transcriptomes of SM and WB, and it showed that, while both myopathies had profiles different from the normal breast muscle, SM and WB were similar, with comparable enriched metabolic pathways and processes despite presenting markedly different macroscopic features.
Collapse
Affiliation(s)
- Sunoh Che
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, ON N1G2W1, Canada; (S.C.); (P.H.P.)
| | - Phuc H. Pham
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, ON N1G2W1, Canada; (S.C.); (P.H.P.)
| | - Shai Barbut
- Department of Food Science, Ontario Agricultural College, University of Guelph, Guelph, ON N1G2W1, Canada;
| | - Dorothee Bienzle
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, ON N1G2W1, Canada; (S.C.); (P.H.P.)
| | - Leonardo Susta
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, ON N1G2W1, Canada; (S.C.); (P.H.P.)
| |
Collapse
|
33
|
Salib A, Jayatilleke N, Seneviratne JA, Mayoh C, De Preter K, Speleman F, Cheung BB, Carter DR, Marshall GM. MYCN and SNRPD3 cooperate to maintain a balance of alternative splicing events that drives neuroblastoma progression. Oncogene 2024; 43:363-377. [PMID: 38049564 PMCID: PMC10824661 DOI: 10.1038/s41388-023-02897-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 11/05/2023] [Accepted: 11/14/2023] [Indexed: 12/06/2023]
Abstract
Many of the pro-tumorigenic functions of the oncogene MYCN are attributed to its regulation of global gene expression programs. Alternative splicing is another important regulator of gene expression and has been implicated in neuroblastoma development, however, the molecular mechanisms remain unknown. We found that MYCN up-regulated the expression of the core spliceosomal protein, SNRPD3, in models of neuroblastoma initiation and progression. High mRNA expression of SNRPD3 in human neuroblastoma tissues was a strong, independent prognostic factor for poor patient outcome. Repression of SNRPD3 expression correlated with loss of colony formation in vitro and reduced tumorigenicity in vivo. The effect of SNRPD3 on cell viability was in part dependent on MYCN as an oncogenic co-factor. RNA-sequencing revealed a global increase in the number of genes being differentially spliced when MYCN was overexpressed. Surprisingly, depletion of SNRPD3 in the presence of overexpressed MYCN further increased differential splicing, particularly of cell cycle regulators, such as BIRC5 and CDK10. MYCN directly bound SNRPD3, and the protein arginine methyltransferase, PRMT5, consequently increasing SNRPD3 methylation. Indeed, the PRMT5 inhibitor, JNJ-64619178, reduced cell viability and SNRPD3 methylation in neuroblastoma cells with high SNRPD3 and MYCN expression. Our findings demonstrate a functional relationship between MYCN and SNRPD3, which maintains the fidelity of MYCN-driven alternative splicing in the narrow range required for neuroblastoma cell growth. SNRPD3 methylation and its protein-protein interface with MYCN represent novel therapeutic targets. Hypothetical model for SNRPD3 as a co-factor for MYCN oncogenesis. SNRPD3 and MYCN participate in a regulatory loop to balance splicing fidelity in neuroblastoma cells. First MYCN transactivates SNRPD3 to lead to high-level expression. Second, SNRPD3 and MYCN form a protein complex involving PRMT5. Third, this leads to balanced alterative splicing (AS) activitiy that is favorable to neuroblastoma. Together this forms as a therapeutic vulnerability where SNRPD3 perturbation or PRMT5 inhibitors are selectively toxic to neuroblastoma by conditionally disturbing splicing activity.
Collapse
Affiliation(s)
- Alice Salib
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, 2052, Australia
- School of Women's and Children's Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Nisitha Jayatilleke
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Janith A Seneviratne
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Chelsea Mayoh
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, 2052, Australia
- School of Women's and Children's Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Katleen De Preter
- Center for Medical Genetics (CMGG), Ghent University, Medical Research Building (MRB1), Ghent, Belgium
| | - Frank Speleman
- Center for Medical Genetics (CMGG), Ghent University, Medical Research Building (MRB1), Ghent, Belgium
| | - Belamy B Cheung
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, 2052, Australia
- School of Women's and Children's Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Daniel R Carter
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, 2052, Australia.
- School of Biomedical Engineering, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
| | - Glenn M Marshall
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, 2052, Australia.
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, 2031, Australia.
| |
Collapse
|
34
|
Blay E, Hardyman E, Morovic W. PCR-based analytics of gene therapies using adeno-associated virus vectors: Considerations for cGMP method development. Mol Ther Methods Clin Dev 2023; 31:101132. [PMID: 37964893 PMCID: PMC10641278 DOI: 10.1016/j.omtm.2023.101132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
The field of gene therapy has evolved and improved so that today the treatment of thousands of genetic diseases is now possible. An integral aspect of the drug development process is generating analytical methods to be used throughout clinical and commercial manufacturing. Enumeration and identification assays using genetic testing are critical to ensure the safety, efficacy, and stability of many active pharmaceutical ingredients. While nucleic acid-based methods are already reliable and rapid, there are unique biological, technological, and regulatory aspects in gene therapies that must be considered. This review surveys aspects of method development and validation using nucleic acid-based testing of gene therapies by focusing on adeno-associated virus (AAV) vectors and their co-transfection factors. Key differences between quantitative PCR and droplet digital technologies are discussed to show how improvements can be made while still adhering to regulatory guidance. Example validation parameters for AAV genome titers are described to demonstrate the scope of analytical development. Finally, several areas for improving analytical testing are presented to inspire future innovation, including next-generation sequencing and artificial intelligence. Reviewing the broad characteristics of gene therapy assessment serves as an introduction for new researchers, while clarifying processes for professionals already involved in pharmaceutical manufacturing.
Collapse
Affiliation(s)
- Emmanuel Blay
- Gene & Cell Therapy, PPD GMP Laboratories, Part of ThermoFisher Scientific, Middleton, WI, USA
| | - Elaine Hardyman
- Gene & Cell Therapy, PPD GMP Laboratories, Part of ThermoFisher Scientific, Middleton, WI, USA
| | - Wesley Morovic
- Gene & Cell Therapy, PPD GMP Laboratories, Part of ThermoFisher Scientific, Middleton, WI, USA
| |
Collapse
|
35
|
Szabelska-Beresewicz A, Zyprych-Walczak J, Siatkowski I, Okoniewski M. Ambiguous genes due to aligners and their impact on RNA-seq data analysis. Sci Rep 2023; 13:21770. [PMID: 38066001 PMCID: PMC10709571 DOI: 10.1038/s41598-023-41085-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/22/2023] [Indexed: 12/18/2023] Open
Abstract
The main scope of the study is ambiguous genes, i.e. genes whose expression is difficult to estimate from the data produced by next-generation sequencing technologies. We focused on the RNA sequencing (RNA-Seq) type of experiment performed on the Illumina platform. It is crucial to identify such genes and understand the cause of their difficulty, as these genes may be involved in some diseases. By giving misleading results, they could contribute to a misunderstanding of the cause of certain diseases, which could lead to inappropriate treatment. We thought that the ambiguous genes would be difficult to map because of their complex structure. So we looked at RNA-seq analysis using different mappers to find genes that would have different measurements from the aligners. We were able to identify such genes using a generalized linear model with two factors: mappers and groups introduced by the experiment. A large proportion of ambiguous genes are pseudogenes. High sequence similarity of pseudogenes to functional genes may indicate problems in alignment procedures. In addition, predictive analysis verified the performance of difficult genes in classification. The effectiveness of classifying samples into specific groups was compared, including the expression of difficult and not difficult genes as covariates. In almost all cases considered, ambiguous genes have less predictive power.
Collapse
Affiliation(s)
- Alicja Szabelska-Beresewicz
- Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637, Poznan, Poland
| | - Joanna Zyprych-Walczak
- Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637, Poznan, Poland.
| | - Idzi Siatkowski
- Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637, Poznan, Poland
| | - Michał Okoniewski
- Scientific IT Services, ETH Zurich, Weinbergstrasse 11, 8092, Zurich, Switzerland
| |
Collapse
|
36
|
Shi Q, Yu B, Zhang Y, Yang Y, Xu C, Zhang M, Chen G, Luo F, Sun B, Yang R, Li Y, Feng H. Targeting TRIM24 promotes neuroblastoma differentiation and decreases tumorigenicity via LSD1/CoREST complex. Cell Oncol (Dordr) 2023; 46:1763-1775. [PMID: 37466744 DOI: 10.1007/s13402-023-00843-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2023] [Indexed: 07/20/2023] Open
Abstract
PURPOSE High-risk neuroblastoma (NB) still has an unfavorable prognosis and inducing NB differentiation is a potential strategy in clinical treatment, yet underlying mechanisms are still elusive. Here we identify TRIM24 as an important regulator of NB differentiation. METHODS Multiple datasets and clinical specimens were analyzed to define the role of TRIM24 in NB. The effects of TRIM24 on differentiation and growth of NB were determined by cell morphology, spheres formation, soft agar assay, and subcutaneous xenograft in nude mice. RNA-Seq and qRT-PCR were used to identify genes and pathways involved. Mass spectrometry and co-immunoprecipitation were used to explore the interaction of proteins. RESULTS Trim24 is highly expressed in spontaneous NB in TH-MYCN transgenic mice and clinical NB specimens. It is associated with poor NB differentiation and unfavorable prognostic. Knockout of TRIM24 in neuroblastoma cells promotes cell differentiation, reduces cell stemness, and inhibits colony formation in soft agar and subcutaneous xenograft tumor growth in nude mice. Mechanistically, TRIM24 knockout alters genes and pathways related to neural differentiation and development by suppressing LSD1/CoREST complex formation. Besides, TRIM24 knockout activates the retinoic acid pathway. Targeting TRIM24 in combination with retinoic acid (RA) synergistically promotes NB cell differentiation and inhibits cell viability. CONCLUSION Our findings demonstrate that TRIM24 is critical for NB differentiation and suggest that TRIM24 is a promising therapeutic target in combination with RA in NB differentiation therapy.
Collapse
Affiliation(s)
- Qiqi Shi
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Bo Yu
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yingwen Zhang
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yi Yang
- Pediatric Translational Medicine Institute, Department of Hematology & Oncology, Shanghai Children's Medical Center, School of Medicine, National Health Committee Key Laboratory of Pediatric Hematology & Oncology, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Chenxin Xu
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Mingda Zhang
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Guoyu Chen
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Fei Luo
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Bowen Sun
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Ru Yang
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yanxin Li
- Pediatric Translational Medicine Institute, Department of Hematology & Oncology, Shanghai Children's Medical Center, School of Medicine, National Health Committee Key Laboratory of Pediatric Hematology & Oncology, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Haizhong Feng
- Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, Shanghai Cancer Institute, School of Medicine, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University, Shanghai, 200127, China.
| |
Collapse
|
37
|
Fiore A, Sala E, Laura C, Riba M, Nelli M, Fumagalli V, Oberrauch F, Mangione M, Cristofani C, Provero P, Iannacone M, Kuka M. A fluorescent reporter model for the visualization and characterization of T DC. Eur J Immunol 2023; 53:e2350529. [PMID: 37741290 DOI: 10.1002/eji.202350529] [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: 04/14/2023] [Revised: 09/04/2023] [Accepted: 09/22/2023] [Indexed: 09/25/2023]
Abstract
TDC are hematopoietic cells that combine dendritic cell (DC) and conventional T-cell markers and functional properties. They were identified in secondary lymphoid organs (SLOs) of naïve mice as cells expressing CD11c, major histocompatibility molecules (MHC)-II, and the T-cell receptor (TCR). Despite thorough characterization, a physiological role for TDC remains to be determined. Unfortunately, using CD11c as a marker for TDC has the caveat of its upregulation on different cells, including T cells, upon activation. Here, we took advantage of Zbtb46-GFP reporter mice to explore the frequency and localization of TDC in different tissues at steady state and upon viral infection. RNA sequencing analysis confirmed that TDC sorted from Zbtb46-GFP mice have a gene signature that is distinct from conventional T cells and DC. In addition, this reporter model allowed for identification of TDC in situ not only in SLOs but also in the liver and lung of naïve mice. Interestingly, we found that TDC numbers in the SLOs increased upon viral infection, suggesting that TDC might play a role during viral infections. In conclusion, we propose a visualization strategy that might shed light on the physiological role of TDC in several pathological contexts, including infection and cancer.
Collapse
Affiliation(s)
- Alessandra Fiore
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Eleonora Sala
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Chiara Laura
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Michela Riba
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Nelli
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Valeria Fumagalli
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Marta Mangione
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Claudia Cristofani
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Provero
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Department of Neurosciences "Rita Levi Montalcini", University of Turin, Turin, Italy
| | - Matteo Iannacone
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Experimental Imaging Centre, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Mirela Kuka
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| |
Collapse
|
38
|
Pereira EPV, da Silva Felipe SM, de Freitas RM, da Cruz Freire JE, Oliveira AER, Canabrava N, Soares PM, van Tilburg MF, Guedes MIF, Grueter CE, Ceccatto VM. Transcriptional Profiling of SARS-CoV-2-Infected Calu-3 Cells Reveals Immune-Related Signaling Pathways. Pathogens 2023; 12:1373. [PMID: 38003837 PMCID: PMC10674242 DOI: 10.3390/pathogens12111373] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
The COVID-19 disease, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), emerged in late 2019 and rapidly spread worldwide, becoming a pandemic that infected millions of people and caused significant deaths. COVID-19 continues to be a major threat, and there is a need to deepen our understanding of the virus and its mechanisms of infection. To study the cellular responses to SARS-CoV-2 infection, we performed an RNA sequencing of infected vs. uninfected Calu-3 cells. Total RNA was extracted from infected (0.5 MOI) and control Calu-3 cells and converted to cDNA. Sequencing was performed, and the obtained reads were quality-analyzed and pre-processed. Differential expression was assessed with the EdgeR package, and functional enrichment was performed in EnrichR for Gene Ontology, KEGG pathways, and WikiPathways. A total of 1040 differentially expressed genes were found in infected vs. uninfected Calu-3 cells, of which 695 were up-regulated and 345 were down-regulated. Functional enrichment analyses revealed the predominant up-regulation of genes related to innate immune response, response to virus, inflammation, cell proliferation, and apoptosis. These transcriptional changes following SARS-CoV-2 infection may reflect a cellular response to the infection and help to elucidate COVID-19 pathogenesis, in addition to revealing potential biomarkers and drug targets.
Collapse
Affiliation(s)
- Eric Petterson Viana Pereira
- Superior Institute of Biomedical Sciences, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (S.M.d.S.F.); (R.M.d.F.); (J.E.d.C.F.); (P.M.S.)
| | - Stela Mirla da Silva Felipe
- Superior Institute of Biomedical Sciences, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (S.M.d.S.F.); (R.M.d.F.); (J.E.d.C.F.); (P.M.S.)
| | - Raquel Martins de Freitas
- Superior Institute of Biomedical Sciences, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (S.M.d.S.F.); (R.M.d.F.); (J.E.d.C.F.); (P.M.S.)
| | - José Ednésio da Cruz Freire
- Superior Institute of Biomedical Sciences, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (S.M.d.S.F.); (R.M.d.F.); (J.E.d.C.F.); (P.M.S.)
| | | | - Natália Canabrava
- Biotechnology and Molecular Biology Laboratory, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (N.C.); (M.F.v.T.); (M.I.F.G.)
| | - Paula Matias Soares
- Superior Institute of Biomedical Sciences, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (S.M.d.S.F.); (R.M.d.F.); (J.E.d.C.F.); (P.M.S.)
| | - Mauricio Fraga van Tilburg
- Biotechnology and Molecular Biology Laboratory, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (N.C.); (M.F.v.T.); (M.I.F.G.)
| | - Maria Izabel Florindo Guedes
- Biotechnology and Molecular Biology Laboratory, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (N.C.); (M.F.v.T.); (M.I.F.G.)
| | - Chad Eric Grueter
- Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA;
| | - Vânia Marilande Ceccatto
- Superior Institute of Biomedical Sciences, State University of Ceará, Fortaleza 60714-903, CE, Brazil; (S.M.d.S.F.); (R.M.d.F.); (J.E.d.C.F.); (P.M.S.)
| |
Collapse
|
39
|
Xie Z, Chen C, Ma’ayan A. Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis. PeerJ 2023; 11:e16351. [PMID: 37953774 PMCID: PMC10638921 DOI: 10.7717/peerj.16351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/04/2023] [Indexed: 11/14/2023] Open
Abstract
Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.
Collapse
Affiliation(s)
- Zhuorui Xie
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clara Chen
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Avi Ma’ayan
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
40
|
Yang J, Liu Y, Shang J, Chen Q, Chen Q, Ren L, Zhang N, Yu Y, Li Z, Song Y, Yang S, Scherer A, Tong W, Hong H, Xiao W, Shi L, Zheng Y. The Quartet Data Portal: integration of community-wide resources for multiomics quality control. Genome Biol 2023; 24:245. [PMID: 37884999 PMCID: PMC10601216 DOI: 10.1186/s13059-023-03091-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.
Collapse
Affiliation(s)
- Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, Guangdong, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhihui Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yueqiang Song
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shengpeng Yang
- Intelligent Storage, Alibaba Cloud, Alibaba Group, Hangzhou, Zhejiang, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncological Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| |
Collapse
|
41
|
Hore Z, Royds J, Abuukar Abdullahi R, Lampa J, Al-Kaisy A, Denk F. Cerebrospinal fluid immune cells appear similar across neuropathic and non-neuropathic pain conditions. Wellcome Open Res 2023; 8:493. [PMID: 38707493 PMCID: PMC11069048 DOI: 10.12688/wellcomeopenres.20153.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 05/07/2024] Open
Abstract
Background Microglia have been implicated in the pathophysiology of neuropathic pain. Here, we sought to investigate whether cerebrospinal fluid (CSF) might be used as a proxy-measure of microglial activation in human participants. Methods We preformed fluorescence-activated cell sorting (FACS) of CSF immune cell populations derived from individuals who experienced pain with neuropathic features. We sorted CD4+, CD8+ T cells and monocytes and analyzed their transcriptome using RNA sequencing. We also performed Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing to characterize the expression of all CSF immune cells in a patient with postherpetic neuralgia and in a patient with neuropathic pain after failed back surgery. Results Immune cell numbers and phenotypes were not obviously different between individuals regardless of the etiology of their pain. This was true when examining our own dataset, as well as when comparing it to previously published single-cell RNA sequencing data of human CSF. In all instances, CSF monocytes showed expression of myeloid cell markers commonly associated with microglia ( P2RY12, TMEM119 and OLFML3), which will make it difficult to ascertain the origin of CSF proteins: do they derive directly from circulating CSF monocytes or could some originate in spinal cord microglia in the parenchyma? Conclusions We conclude that it will not be straightforward to use CSF as a biomarker for microglial function in humans.
Collapse
Affiliation(s)
- Zoe Hore
- Wolfson Centre for Age-Related Diseases, King's College London, London, England, UK
| | - Jonathan Royds
- Guy’s and St Thomas’ Chronic Pain Department, St Thomas Hospital, London, UK
| | | | - Jon Lampa
- Rheumatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Adnan Al-Kaisy
- Guy’s and St Thomas’ Chronic Pain Department, St Thomas Hospital, London, UK
| | - Franziska Denk
- Wolfson Centre for Age-Related Diseases, King's College London, London, England, UK
| |
Collapse
|
42
|
Zhang X, Yang C, Meng Z, Zhong H, Hou X, Wang F, Lu Y, Guo J, Zeng Y. miR-124 and VAMP3 Act Antagonistically in Human Neuroblastoma. Int J Mol Sci 2023; 24:14877. [PMID: 37834325 PMCID: PMC10573497 DOI: 10.3390/ijms241914877] [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: 09/05/2023] [Revised: 09/25/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
Neuroblastoma (NB) is the most common extracranial solid tumor that affects developing nerve cells in the fetus, infants, and children. miR-124 is a microRNA (miRNA) enriched in neuronal tissues, and VAMP3 (vesicle-associated membrane protein 3) has been reported to be an miR-124 target, although the relationship between NB and miR-124 or VAMP3 is unknown. Our current work identified that miR-124 levels are high in NB cases and that elevated miR-124 correlates with worse NB outcomes. Conversely, depressed VAMP3 correlates with worse NB outcomes. To investigate the mechanisms by which miR-124 and VAMP3 regulate NB, we altered miR-124 or VAMP3 expression in human NB cells and observed that increased miR-124 and reduced VAMP3 stimulated cell proliferation and suppressed apoptosis, while increased VAMP3 had the opposite effects. Genome-wide mRNA expression analyses identified gene and pathway changes which might explain the NB cell phenotypes. Together, our studies suggest that miR-124 and VAMP3 could be potential new markers of NB and targets of NB treatments.
Collapse
Affiliation(s)
- Xiaoxiao Zhang
- Department of Zoology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Chengyong Yang
- Department of Zoology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhen Meng
- Department of Zoology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Huanhuan Zhong
- Department of Zoology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Xutian Hou
- Department of Zoology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Fenfen Wang
- Department of Zoology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Yiping Lu
- Department of Zoology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Yan Zeng
- Department of Zoology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| |
Collapse
|
43
|
Kainth AS, Haddad GA, Hall JM, Ruthenburg AJ. Merging short and stranded long reads improves transcript assembly. PLoS Comput Biol 2023; 19:e1011576. [PMID: 37883581 PMCID: PMC10629667 DOI: 10.1371/journal.pcbi.1011576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 11/07/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
Long-read RNA sequencing has arisen as a counterpart to short-read sequencing, with the potential to capture full-length isoforms, albeit at the cost of lower depth. Yet this potential is not fully realized due to inherent limitations of current long-read assembly methods and underdeveloped approaches to integrate short-read data. Here, we critically compare the existing methods and develop a new integrative approach to characterize a particularly challenging pool of low-abundance long noncoding RNA (lncRNA) transcripts from short- and long-read sequencing in two distinct cell lines. Our analysis reveals severe limitations in each of the sequencing platforms. For short-read assemblies, coverage declines at transcript termini resulting in ambiguous ends, and uneven low coverage results in segmentation of a single transcript into multiple transcripts. Conversely, long-read sequencing libraries lack depth and strand-of-origin information in cDNA-based methods, culminating in erroneous assembly and quantitation of transcripts. We also discover a cDNA synthesis artifact in long-read datasets that markedly impacts the identity and quantitation of assembled transcripts. Towards remediating these problems, we develop a computational pipeline to "strand" long-read cDNA libraries that rectifies inaccurate mapping and assembly of long-read transcripts. Leveraging the strengths of each platform and our computational stranding, we also present and benchmark a hybrid assembly approach that drastically increases the sensitivity and accuracy of full-length transcript assembly on the correct strand and improves detection of biological features of the transcriptome. When applied to a challenging set of under-annotated and cell-type variable lncRNA, our method resolves the segmentation problem of short-read sequencing and the depth problem of long-read sequencing, resulting in the assembly of coherent transcripts with precise 5' and 3' ends. Our workflow can be applied to existing datasets for superior demarcation of transcript ends and refined isoform structure, which can enable better differential gene expression analyses and molecular manipulations of transcripts.
Collapse
Affiliation(s)
- Amoldeep S. Kainth
- Department of Molecular Genetics and Cell Biology, The University of Chicago, Chicago, Illinois, United States of America
| | - Gabriela A. Haddad
- Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, Illinois, United States of America
| | - Johnathon M. Hall
- Department of Molecular Genetics and Cell Biology, The University of Chicago, Chicago, Illinois, United States of America
| | - Alexander J. Ruthenburg
- Department of Molecular Genetics and Cell Biology, The University of Chicago, Chicago, Illinois, United States of America
- Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, Illinois, United States of America
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois, United States of America
| |
Collapse
|
44
|
Newman JRB, Long SA, Speake C, Greenbaum CJ, Cerosaletti K, Rich SS, Onengut-Gumuscu S, McIntyre LM, Buckner JH, Concannon P. Shifts in isoform usage underlie transcriptional differences in regulatory T cells in type 1 diabetes. Commun Biol 2023; 6:988. [PMID: 37758901 PMCID: PMC10533491 DOI: 10.1038/s42003-023-05327-7] [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: 08/16/2022] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Genome-wide association studies have identified numerous loci with allelic associations to Type 1 Diabetes (T1D) risk. Most disease-associated variants are enriched in regulatory sequences active in lymphoid cell types, suggesting that lymphocyte gene expression is altered in T1D. Here we assay gene expression between T1D cases and healthy controls in two autoimmunity-relevant lymphocyte cell types, memory CD4+/CD25+ regulatory T cells (Treg) and memory CD4+/CD25- T cells, using a splicing event-based approach to characterize tissue-specific transcriptomes. Limited differences in isoform usage between T1D cases and controls are observed in memory CD4+/CD25- T-cells. In Tregs, 402 genes demonstrate differences in isoform usage between cases and controls, particularly RNA recognition and splicing factor genes. Many of these genes are regulated by the variable inclusion of exons that can trigger nonsense mediated decay. Our results suggest that dysregulation of gene expression, through shifts in alternative splicing in Tregs, contributes to T1D pathophysiology.
Collapse
Affiliation(s)
- Jeremy R B Newman
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, 32601, USA
- University of Florida Genetics Institute, University of Florida, Gainesville, FL, 32601, USA
| | - S Alice Long
- Center for Translational Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA, 98101, USA
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA, 98101, USA
| | - Carla J Greenbaum
- Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA, 98101, USA
| | - Karen Cerosaletti
- Center for Translational Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA, 98101, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Lauren M McIntyre
- University of Florida Genetics Institute, University of Florida, Gainesville, FL, 32601, USA
- Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, 32601, USA
| | - Jane H Buckner
- Center for Translational Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA, 98101, USA
| | - Patrick Concannon
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, 32601, USA.
- University of Florida Genetics Institute, University of Florida, Gainesville, FL, 32601, USA.
| |
Collapse
|
45
|
Yi H, Lin Y, Chang Q, Jin W. A fast and globally optimal solution for RNA-seq quantification. Brief Bioinform 2023; 24:bbad298. [PMID: 37595963 DOI: 10.1093/bib/bbad298] [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: 05/05/2023] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 08/20/2023] Open
Abstract
Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation-Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation ATAx = ATb. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at https://github.com/yhg926/TQSLE.
Collapse
Affiliation(s)
- Huiguang Yi
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Rd, Shenzhen, 518000, Guangdong, China
- School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, China
| | - Yanling Lin
- School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, China
| | - Qing Chang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Rd, Shenzhen, 518000, Guangdong, China
| | - Wenfei Jin
- School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, China
| |
Collapse
|
46
|
Yuan Y, Alzrigat M, Rodriguez-Garcia A, Wang X, Bexelius TS, Johnsen JI, Arsenian-Henriksson M, Liaño-Pons J, Bedoya-Reina OC. Target Genes of c-MYC and MYCN with Prognostic Power in Neuroblastoma Exhibit Different Expressions during Sympathoadrenal Development. Cancers (Basel) 2023; 15:4599. [PMID: 37760568 PMCID: PMC10527308 DOI: 10.3390/cancers15184599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/06/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Deregulation of the MYC family of transcription factors c-MYC (encoded by MYC), MYCN, and MYCL is prevalent in most human cancers, with an impact on tumor initiation and progression, as well as response to therapy. In neuroblastoma (NB), amplification of the MYCN oncogene and over-expression of MYC characterize approximately 40% and 10% of all high-risk NB cases, respectively. However, the mechanism and stage of neural crest development in which MYCN and c-MYC contribute to the onset and/or progression of NB are not yet fully understood. Here, we hypothesized that subtle differences in the expression of MYCN and/or c-MYC targets could more accurately stratify NB patients in different risk groups rather than using the expression of either MYC gene alone. We employed an integrative approach using the transcriptome of 498 NB patients from the SEQC cohort and previously defined c-MYC and MYCN target genes to model a multigene transcriptional risk score. Our findings demonstrate that defined sets of c-MYC and MYCN targets with significant prognostic value, effectively stratify NB patients into different groups with varying overall survival probabilities. In particular, patients exhibiting a high-risk signature score present unfavorable clinical parameters, including increased clinical risk, higher INSS stage, MYCN amplification, and disease progression. Notably, target genes with prognostic value differ between c-MYC and MYCN, exhibiting distinct expression patterns in the developing sympathoadrenal system. Genes associated with poor outcomes are mainly found in sympathoblasts rather than in chromaffin cells during the sympathoadrenal development.
Collapse
Affiliation(s)
- Ye Yuan
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Mohammad Alzrigat
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Aida Rodriguez-Garcia
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Xueyao Wang
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Tomas Sjöberg Bexelius
- Paediatric Oncology Unit, Astrid Lindgren’s Children Hospital, SE-171 64 Solna, Sweden
- Department of Women’s and Children’s Health, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - John Inge Johnsen
- Department of Women’s and Children’s Health, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Marie Arsenian-Henriksson
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Judit Liaño-Pons
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| | - Oscar C. Bedoya-Reina
- Department of Microbiology, Tumor and Cell Biology (MTC), Biomedicum, Karolinska Institutet, SE-171 65 Stockholm, Sweden
| |
Collapse
|
47
|
Martínez-Pacheco ML, Hernández-Lemus E, Mejía C. Analysis of High-Risk Neuroblastoma Transcriptome Reveals Gene Co-Expression Signatures and Functional Features. BIOLOGY 2023; 12:1230. [PMID: 37759629 PMCID: PMC10525871 DOI: 10.3390/biology12091230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
Neuroblastoma represents a neoplastic expansion of neural crest cells in the developing sympathetic nervous system and is childhood's most common extracranial solid tumor. The heterogeneity of gene expression in different types of cancer is well-documented, and genetic features of neuroblastoma have been described by classification, development stage, malignancy, and progression of tumors. Here, we aim to analyze RNA sequencing datasets, publicly available in the GDC data portal, of neuroblastoma tumor samples from various patients and compare them with normal adrenal gland tissue from the GTEx data portal to elucidate the gene expression profile and regulation networks they share. Our results from the differential expression, weighted correlation network, and functional enrichment analyses that we performed with the count data from neuroblastoma and standard normal gland samples indicate that the analysis of transcriptome data from 58 patients diagnosed with high-risk neuroblastoma shares the expression pattern of 104 genes. More importantly, our analyses identify the co-expression relationship and the role of these genes in multiple biological processes and signaling pathways strongly associated with this disease phenotype. Our approach proposes a group of genes and their biological functions to be further investigated as essential molecules and possible therapeutic targets of neuroblastoma regardless of the etiology of individual tumors.
Collapse
Affiliation(s)
| | | | - Carmen Mejía
- Faculty of Natural Sciences, Autonomous University of Queretaro, Queretaro 76010, Mexico;
| |
Collapse
|
48
|
Tian S, Zhan D, Yu Y, Wang Y, Liu M, Tan S, Li Y, Song L, Qin Z, Li X, Liu Y, Li Y, Ji S, Wang S, Zheng Y, He F, Qin J, Ding C. Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics. Genome Biol 2023; 24:202. [PMID: 37674236 PMCID: PMC10483797 DOI: 10.1186/s13059-023-03048-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories. RESULTS Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics. CONCLUSION Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.
Collapse
Affiliation(s)
- Sha Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yunzhi Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Subei Tan
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yan Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Zhaoyu Qin
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Xianju Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yang Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yao Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Shuhui Ji
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Shanshan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| | - Fuchu He
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| |
Collapse
|
49
|
Yu Y, Zhang N, Mai Y, Ren L, Chen Q, Cao Z, Chen Q, Liu Y, Hou W, Yang J, Hong H, Xu J, Tong W, Dong L, Shi L, Fang X, Zheng Y. Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method. Genome Biol 2023; 24:201. [PMID: 37674217 PMCID: PMC10483871 DOI: 10.1186/s13059-023-03047-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/18/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. RESULTS As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. CONCLUSIONS Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.
Collapse
Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, Guangdong, China
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | | | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
| |
Collapse
|
50
|
Hughes AEO, Montgomery MC, Liu C, Weimer ET. Allele-specific quantification of human leukocyte antigen transcript isoforms by nanopore sequencing. Front Immunol 2023; 14:1199618. [PMID: 37662944 PMCID: PMC10471969 DOI: 10.3389/fimmu.2023.1199618] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/05/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction While tens of thousands of HLA alleles have been identified by DNA sequencing, the contribution of alternative splicing to HLA diversity is not well characterized. In this study, we sought to determine if long-read sequencing could be used to accurately quantify allele-specific HLA transcripts in primary human lymphocytes. Methods cDNA libraries were prepared from peripheral blood lymphocytes from 12 donors and sequenced by nanopore long-read sequencing. HLA reads were aligned to donor-specific reference sequences based on the known type of each donor. Allele-specific exon utilization was calculated as the proportion of reads aligning to each allele containing known exons, and transcript isoforms were quantified based on patterns of exon utilization within individual reads. Results Splice variants were rare among class I HLA genes (median exon retention rate 99%-100%), except for several HLA-C alleles with exon 5 spliced out of up to 15% of reads. Splice variants were also rare among class II HLA genes (median exon retention rate 98%-100%), except for HLA-DQB1. Consistent with previous work, exon 5 of HLA-DQB1 was spliced out in alleles with a mutated splice acceptor site at rs28688207. Surprisingly, a 28% loss of exon 5 was also observed in HLA-DQB1 alleles with an intact splice acceptor site at rs28688207. Discussion We describe a simple bioinformatic workflow to quantify allele-specific expression of HLA transcript isoforms. Further studies are warranted to characterize the repertoire of HLA transcripts expressed in different cell types and tissues across diverse populations.
Collapse
Affiliation(s)
- Andrew E. O. Hughes
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
| | - Maureen C. Montgomery
- Molecular Immunology Laboratory, McLendon Clinical Laboratories, University of North Carolina Hospitals, Chapel Hill, NC, United States
| | - Chang Liu
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric T. Weimer
- Molecular Immunology Laboratory, McLendon Clinical Laboratories, University of North Carolina Hospitals, Chapel Hill, NC, United States
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, United States
| |
Collapse
|