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Nguyen H, Nguyen H, Tran D, Draghici S, Nguyen T. Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges. Nucleic Acids Res 2024; 52:4761-4783. [PMID: 38619038 PMCID: PMC11109966 DOI: 10.1093/nar/gkae267] [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: 10/26/2023] [Revised: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Duc Tran
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, USA
- Advaita Bioinformatics, Ann Arbor, MI, USA
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
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2
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Wong CJ, Friedman SD, Snider L, Bennett SR, Jones TI, Jones PL, Shaw DWW, Blemker SS, Riem L, DuCharme O, Lemmers RJFL, van der Maarel SM, Wang LH, Tawil R, Statland JM, Tapscott SJ. Regional and bilateral MRI and gene signatures in facioscapulohumeral dystrophy: implications for clinical trial design and mechanisms of disease progression. Hum Mol Genet 2024; 33:698-708. [PMID: 38268317 PMCID: PMC11000661 DOI: 10.1093/hmg/ddae007] [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: 09/24/2023] [Revised: 11/11/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Identifying the aberrant expression of DUX4 in skeletal muscle as the cause of facioscapulohumeral dystrophy (FSHD) has led to rational therapeutic development and clinical trials. Several studies support the use of MRI characteristics and the expression of DUX4-regulated genes in muscle biopsies as biomarkers of FSHD disease activity and progression. We performed lower-extremity MRI and muscle biopsies in the mid-portion of the tibialis anterior (TA) muscles bilaterally in FSHD subjects and validated our prior reports of the strong association between MRI characteristics and expression of genes regulated by DUX4 and other gene categories associated with FSHD disease activity. We further show that measurements of normalized fat content in the entire TA muscle strongly predict molecular signatures in the mid-portion of the TA, indicating that regional biopsies can accurately measure progression in the whole muscle and providing a strong basis for inclusion of MRI and molecular biomarkers in clinical trial design. An unanticipated finding was the strong correlations of molecular signatures in the bilateral comparisons, including markers of B-cells and other immune cell populations, suggesting that a systemic immune cell infiltration of skeletal muscle might have a role in disease progression.
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Affiliation(s)
- Chao-Jen Wong
- Division of Human Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States
| | - Seth D Friedman
- Department of Radiology, Seattle Children’s Hospital, 4540 Sandpoint Way, Seattle, WA 98105, United States
| | - Lauren Snider
- Division of Human Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States
| | - Sean R Bennett
- Division of Human Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States
| | - Takako I Jones
- Department of Pharmacology, University of Nevada, Reno School of Medicine, 1664 North Virginia Street, Reno, NV 89557, United States
| | - Peter L Jones
- Department of Pharmacology, University of Nevada, Reno School of Medicine, 1664 North Virginia Street, Reno, NV 89557, United States
| | - Dennis W W Shaw
- Department of Radiology, Seattle Children’s Hospital, 4540 Sandpoint Way, Seattle, WA 98105, United States
| | - Silvia S Blemker
- Springbok Analytics, 100 W South St, Charlottesville, VA 22902, United States
| | - Lara Riem
- Springbok Analytics, 100 W South St, Charlottesville, VA 22902, United States
| | - Olivia DuCharme
- Springbok Analytics, 100 W South St, Charlottesville, VA 22902, United States
| | - Richard J F L Lemmers
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
| | - Silvère M van der Maarel
- Department of Human Genetics, Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
| | - Leo H Wang
- Department of Neurology, University of Washington, 1959 NE Pacific St, Seattle, WA 98105, United States
| | - Rabi Tawil
- Department of Neurology, University of Rochester Medical Center, 601 Elm St, Rochester, NY 14642, United States
| | - Jeffrey M Statland
- Department of Neurology, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KA 66160, United States
| | - Stephen J Tapscott
- Division of Human Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States
- Department of Neurology, University of Washington, 1959 NE Pacific St, Seattle, WA 98105, United States
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3
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Liu Y, Zeng X, Zhang H. An Emerging Approach of Age-Related Hearing Loss Research: Application of Integrated Multi-Omics Analysis. Adv Biol (Weinh) 2024; 8:e2300613. [PMID: 38279573 DOI: 10.1002/adbi.202300613] [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: 11/13/2023] [Revised: 01/03/2024] [Indexed: 01/28/2024]
Abstract
As one of the most common otologic diseases in the elderly, age-related hearing loss (ARHL) usually characterized by hearing loss and cognitive disorders, which have a significant impact on the elderly's physical and mental health and quality of life. However, as a typical disease of aging, it is unclear why aging causes widespread hearing impairment in the elderly. As molecular biological experiments have been conducted for research recently, ARHL is gradually established at various levels with the application and development of integrated multi-omics analysis in the studies of ARHL. Here, the recent progress in the application of multi-omics analysis in the molecular mechanisms of ARHL development and therapeutic regimens, including the combined analysis of different omics, such as transcriptome, proteome, and metabolome, to screen for risk sites, risk genes, and differences in lipid metabolism, etc., is outlined and the integrated histological data further promote the profound understanding of the disease process as well as physiological mechanisms of ARHL. The advantages and disadvantages of multi-omics analysis in disease research are also discussed and the authors speculate on the future prospects and applications of this part-to-whole approach, which may provide more comprehensive guidance for ARHL and aging disease prevention and treatment.
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Affiliation(s)
- Yue Liu
- Department of Otolaryngology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, China
- Department of Otolaryngology, Longgang E.N.T. Hospital and Shenzhen Key Laboratory of E.N.T, Institute of E.N.T., Shenzhen, 518172, China
- Department of Graduate and Scientific Research, Zunyi Medical University Zhuhai Campus, Zhuhai, 519041, China
| | - Xianhai Zeng
- Department of Otolaryngology, Longgang E.N.T. Hospital and Shenzhen Key Laboratory of E.N.T, Institute of E.N.T., Shenzhen, 518172, China
- Department of Graduate and Scientific Research, Zunyi Medical University Zhuhai Campus, Zhuhai, 519041, China
| | - Huasong Zhang
- Department of Otolaryngology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, China
- Department of Otolaryngology, Longgang E.N.T. Hospital and Shenzhen Key Laboratory of E.N.T, Institute of E.N.T., Shenzhen, 518172, China
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4
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Vathrakokoili Pournara A, Miao Z, Beker OY, Nolte N, Brazma A, Papatheodorou I. CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues. BIOINFORMATICS ADVANCES 2024; 4:vbae048. [PMID: 38638280 PMCID: PMC11023940 DOI: 10.1093/bioadv/vbae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/20/2024] [Accepted: 03/21/2024] [Indexed: 04/20/2024]
Abstract
Motivation Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods. Results In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods. Availability and implementation https://github.com/Papatheodorou-Group/CATD_snakemake.
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Affiliation(s)
- Anna Vathrakokoili Pournara
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Zhichao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- GMU-GIBH Joint School of Life Sciences, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, 511436, China
| | - Ozgur Yilimaz Beker
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956, Turkey
| | - Nadja Nolte
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 121-1000, Slovenia
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, United Kingdom
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5
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Takasawa K, Asada K, Kaneko S, Shiraishi K, Machino H, Takahashi S, Shinkai N, Kouno N, Kobayashi K, Komatsu M, Mizuno T, Okubo Y, Mukai M, Yoshida T, Yoshida Y, Horinouchi H, Watanabe SI, Ohe Y, Yatabe Y, Kohno T, Hamamoto R. Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability. Exp Mol Med 2024; 56:646-655. [PMID: 38433247 PMCID: PMC10985003 DOI: 10.1038/s12276-024-01173-7] [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: 05/11/2023] [Revised: 11/27/2023] [Accepted: 12/13/2023] [Indexed: 03/05/2024] Open
Abstract
DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.
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Affiliation(s)
- Ken Takasawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Satoshi Takahashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Nobuji Kouno
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Takaaki Mizuno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
- Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yu Okubo
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Masami Mukai
- Division of Medical Informatics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Tatsuya Yoshida
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yukihiro Yoshida
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Hidehito Horinouchi
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Shun-Ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yuichiro Ohe
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
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6
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Mohammad Mirzaei N, Shahriyari L. Modeling cancer progression: an integrated workflow extending data-driven kinetic models to bio-mechanical PDE models. Phys Biol 2024; 21:022001. [PMID: 38330444 DOI: 10.1088/1478-3975/ad2777] [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: 10/07/2023] [Accepted: 02/08/2024] [Indexed: 02/10/2024]
Abstract
Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There are many mathematical models which investigate cancer through different approaches, from sub-cellular to tissue scale, and from treatment to diagnostic points of view. In this study, we lay out a step-by-step methodology for a data-driven mechanistic model of the tumor microenvironment. We discuss data acquisition strategies, data preparation, parameter estimation, and sensitivity analysis techniques. Furthermore, we propose a possible approach to extend mechanistic ordinary differential equation models to PDE models coupled with mechanical growth. The workflow discussed in this article can help understand the complex temporal and spatial interactions between cells and cytokines in the tumor microenvironment and their effect on tumor growth.
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Affiliation(s)
- Navid Mohammad Mirzaei
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, United States of America
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, United States of America
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7
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Halimani N, Nesterchuk M, Tsitrina AA, Sabirov M, Andreichenko IN, Dashenkova NO, Petrova E, Kulikov AM, Zatsepin TS, Romanov RA, Mikaelyan AS, Kotelevtsev YV. Knockdown of Hyaluronan synthase 2 suppresses liver fibrosis in mice via induction of transcriptomic changes similar to 4MU treatment. Sci Rep 2024; 14:2797. [PMID: 38307876 PMCID: PMC10837461 DOI: 10.1038/s41598-024-53089-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: 08/25/2023] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
Hepatic fibrosis remains a significant clinical challenge due to ineffective treatments. 4-methylumbelliferone (4MU), a hyaluronic acid (HA) synthesis inhibitor, has proven safe in phase one clinical trials. In this study, we aimed to ameliorate liver fibrosis by inhibiting HA synthesis. We compared two groups of mice with CCl4-induced fibrosis, treated with 4-methylumbelliferone (4MU) and hyaluronan synthase 2 (HAS2) targeting siRNA (siHAS2). The administration of 4MU and siHAS2 significantly reduced collagen and HA deposition, as well as biochemical markers of hepatic damage induced by repeated CCl4 injections. The transcriptomic analysis revealed converging pathways associated with downstream HA signalling. 4MU- and siHAS2-treated fibrotic livers shared 405 upregulated and 628 downregulated genes. These genes were associated with xenobiotic and cholesterol metabolism, mitosis, endoplasmic reticulum stress, RNA processing, and myeloid cell migration. The functional annotation of differentially expressed genes (DEGs) in siHAS2-treated mice revealed attenuation of extracellular matrix-associated pathways. In comparison, in the 4MU-treated group, DEGs were related to lipid and bile metabolism pathways and cell cycle. These findings confirm that HAS2 is an important pharmacological target for suppressing hepatic fibrosis using siRNA.
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Affiliation(s)
- Noreen Halimani
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation and Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, 143025, Russia.
| | - Mikhail Nesterchuk
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation and Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, 143025, Russia
| | - Alexandra A Tsitrina
- IKI-Ilse Katz Institute for Nanoscale Science & Technology, Nem Gurion University of the Negev, Beersheba, Israel
| | - Marat Sabirov
- Koltzov Institute of Developmental Biology of Russian Academy of Sciences, 26 Vavilov Street, Moscow, 119334, Russia
| | - Irina N Andreichenko
- AO Reproduction Head Centre of Agricultural Animals, Tsentralnaya Street, 3., Podolsk, Moscow Region, 142143, Russia
| | - Nataliya O Dashenkova
- Koltzov Institute of Developmental Biology of Russian Academy of Sciences, 26 Vavilov Street, Moscow, 119334, Russia
| | - Elizaveta Petrova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation and Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, 143025, Russia
| | - Alexey M Kulikov
- Koltzov Institute of Developmental Biology of Russian Academy of Sciences, 26 Vavilov Street, Moscow, 119334, Russia
| | - Timofei S Zatsepin
- Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Roman A Romanov
- Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Arsen S Mikaelyan
- Koltzov Institute of Developmental Biology of Russian Academy of Sciences, 26 Vavilov Street, Moscow, 119334, Russia
| | - Yuri V Kotelevtsev
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation and Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, 143025, Russia
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8
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Guo X, Huang Z, Ju F, Zhao C, Yu L. Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306329. [PMID: 38072669 PMCID: PMC10870031 DOI: 10.1002/advs.202306329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/27/2023] [Indexed: 02/17/2024]
Abstract
Accurately identifies the cellular composition of complex tissues, which is critical for understanding disease pathogenesis, early diagnosis, and prevention. However, current methods for deconvoluting bulk RNA sequencing (RNA-seq) typically rely on matched single-cell RNA sequencing (scRNA-seq) as a reference, which can be limiting due to differences in sequencing distribution and the potential for invalid information from single-cell references. Hence, a novel computational method named SCROAM is introduced to address these challenges. SCROAM transforms scRNA-seq and bulk RNA-seq into a shared feature space, effectively eliminating distributional differences in the latent space. Subsequently, cell-type-specific expression matrices are generated from the scRNA-seq data, facilitating the precise identification of cell types within bulk tissues. The performance of SCROAM is assessed through benchmarking against simulated and real datasets, demonstrating its accuracy and robustness. To further validate SCROAM's performance, single-cell and bulk RNA-seq experiments are conducted on mouse spinal cord tissue, with SCROAM applied to identify cell types in bulk tissue. Results indicate that SCROAM is a highly effective tool for identifying similar cell types. An integrated analysis of liver cancer and primary glioblastoma is then performed. Overall, this research offers a novel perspective for delivering precise insights into disease pathogenesis and potential therapeutic strategies.
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Affiliation(s)
- Xinyang Guo
- School of Computer Science and TechnologyXidian UniversityXi'an710071China
| | - Zhaoyang Huang
- School of Computer Science and TechnologyXidian UniversityXi'an710071China
| | - Fen Ju
- Department of Rehabilitation MedicineXijing HospitalFourth Military Medical UniversityXi'an710032China
| | - Chenguang Zhao
- Department of Rehabilitation MedicineXijing HospitalFourth Military Medical UniversityXi'an710032China
| | - Liang Yu
- School of Computer Science and TechnologyXidian UniversityXi'an710071China
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9
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Gerassy-Vainberg S, Starosvetsky E, Gaujoux R, Blatt A, Maimon N, Gorelik Y, Pressman S, Alpert A, Bar-Yoseph H, Dubovik T, Perets B, Katz A, Milman N, Segev M, Chowers Y, Shen-Orr SS. A personalized network framework reveals predictive axis of anti-TNF response across diseases. Cell Rep Med 2024; 5:101300. [PMID: 38118442 PMCID: PMC10829759 DOI: 10.1016/j.xcrm.2023.101300] [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: 02/21/2023] [Revised: 08/20/2023] [Accepted: 10/31/2023] [Indexed: 12/22/2023]
Abstract
Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations. Applying Disruption Networks to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.
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Affiliation(s)
- Shiran Gerassy-Vainberg
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel; Department of Gastroenterology, Rambam Health Care Campus, Haifa 3109601, Israel
| | - Elina Starosvetsky
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Renaud Gaujoux
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel; CytoReason, Tel Aviv 67012, Israel
| | - Alexandra Blatt
- Department of Gastroenterology, Rambam Health Care Campus, Haifa 3109601, Israel
| | - Naama Maimon
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel; Department of Gastroenterology, Rambam Health Care Campus, Haifa 3109601, Israel
| | - Yuri Gorelik
- Department of Gastroenterology, Rambam Health Care Campus, Haifa 3109601, Israel
| | - Sigal Pressman
- Department of Gastroenterology, Rambam Health Care Campus, Haifa 3109601, Israel; Clinical Research Institute, Rambam Health Care Campus, Haifa 3109601, Israel
| | - Ayelet Alpert
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Haggai Bar-Yoseph
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel; Department of Gastroenterology, Rambam Health Care Campus, Haifa 3109601, Israel
| | - Tania Dubovik
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Benny Perets
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | | | - Neta Milman
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Meital Segev
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Yehuda Chowers
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel; Department of Gastroenterology, Rambam Health Care Campus, Haifa 3109601, Israel; Clinical Research Institute, Rambam Health Care Campus, Haifa 3109601, Israel.
| | - Shai S Shen-Orr
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 32000, Israel.
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10
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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11
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Al Kamran Khan MA, Wu J, Sun Y, Barrow AD, Papenfuss AT, Mangiola S. cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling. Bioinformatics 2023; 39:btad685. [PMID: 37952182 PMCID: PMC10692870 DOI: 10.1093/bioinformatics/btad685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 09/19/2023] [Accepted: 11/10/2023] [Indexed: 11/14/2023] Open
Abstract
MOTIVATION The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation. RESULTS Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks. AVAILABILITY AND IMPLEMENTATION Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.
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Affiliation(s)
- Md Abdullah Al Kamran Khan
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3010, Australia
| | - Jian Wu
- Cancer Biology And Therapy, Olivia Newton-John Cancer Research Institute, Heidelberg, VIC 3038, Australia
| | - Yuhan Sun
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3010, Australia
| | - Alexander D Barrow
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3010, Australia
| | - Anthony T Papenfuss
- Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3010, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Stefano Mangiola
- Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3010, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC 3010, Australia
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12
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Pan B, Luo Y, Ye D, Qiu J, Zhang X, Wu X, Yao Y, Wang X, Tang N. A modified immune cell infiltration score achieves ideal stratification for CD8 + T cell efficacy and immunotherapy benefit in hepatocellular carcinoma. Cancer Immunol Immunother 2023; 72:4103-4119. [PMID: 37755466 DOI: 10.1007/s00262-023-03546-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
Immunotherapy, which aims to enhance the function of T cells, has emerged as a novel therapeutic approach for hepatocellular carcinoma (HCC). Nevertheless, the clinical utility of using flow cytometry to assess immune cell infiltration (ICI) is hindered by its cumbersome procedures, prompting the need for more accessible methods. Here, we acquired gene expression profiles and survival data of HCC from TCGA and GSE10186 datasets. The patients were categorized into two clusters of ICI, and a set of 11 characteristic genes responsible for the differentiation performance of these ICI clusters were identified. Subsequently, we successfully developed a modified ICI score (mICIS) by utilizing the expression levels of these genes. The efficacy of our mICIS was confirmed via mass cytometry, flow cytometry, and immunohistochemistry. Our research indicated that the favorable overall survival (OS) rate could be attributed to the improved function of anti-tumor leukocytes rather than their infiltration. Furthermore, we observed that the low score group exhibited lower expression levels of T-cell exhaustion-associated genes, which was confirmed in both HCC tissues from patients and mice, which demonstrated that the benefits of the low scores were due to enhanced active/cytotoxic CD8+ T cells and reduced exhausted CD8+ T cells. Additionally, our mICIS stratified the benefits derived from immunotherapies. Lastly, we observed a misalignment between CD8+ T-cell infiltration and function in HCC. In summary, our mICIS demonstrated proficiency in assessing the OS rate of HCC and offering significant stratified data pertaining to distinct responses to immunotherapy.
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Affiliation(s)
- Banglun Pan
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yue Luo
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Dongjie Ye
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jiacheng Qiu
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xiaoxia Zhang
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xiaoxuan Wu
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yuxin Yao
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xiaoqian Wang
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Nanhong Tang
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- Cancer Center of Fujian Medical University, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, 350122, China.
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13
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Morita K, Mizuno T, Azuma I, Suzuki Y, Kusuhara H. Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases. Toxicol Sci 2023; 197:kfad117. [PMID: 37941435 PMCID: PMC10823770 DOI: 10.1093/toxsci/kfad117] [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] [Indexed: 11/10/2023] Open
Abstract
Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could extend immune information. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats to retrieve information regarding immune cells from toxicogenomics databases. The rat-specific deconvolution showed high correlations for several types of immune cells between spleen and blood, and between liver treated with toxicants compared with those based on human and mouse data. Additionally, we found 4 clusters of compounds in Open TG-GATEs database based on estimated immune cell trafficking, which are different from those based on transcriptome data itself. The contributions of this work are three-fold. First, we obtained the gene expression profiles of 6 rat immune cells necessary for deconvolution. Second, we clarified the importance of species differences on deconvolution. Third, we retrieved immune cell trafficking from toxicogenomics databases. Accumulated and comparable immune cell profiles of massive data of immune cell trafficking in rats could deepen our understanding of enable us to clarify the relationship between the order and the contribution rate of immune cells, chemokines and cytokines, and pathologies. Ultimately, these findings will lead to the evaluation of organ responses in Adverse Outcome Pathway.
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Affiliation(s)
- Katsuhisa Morita
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Tadahaya Mizuno
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Iori Azuma
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Yutaka Suzuki
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Hiroyuki Kusuhara
- Department of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
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14
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Smith GR, Zhao B, Lindholm ME, Raja A, Viggars M, Pincas H, Gay NR, Sun Y, Ge Y, Nair VD, Sanford JA, Amper MAS, Vasoya M, Smith KS, Montgomery S, Zaslavsky E, Bodine SC, Esser KA, Walsh MJ, Snyder MP. Multi-omic identification of key transcriptional regulatory programs during endurance exercise training. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.10.523450. [PMID: 36711841 PMCID: PMC9882056 DOI: 10.1101/2023.01.10.523450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Transcription factors (TFs) play a key role in regulating gene expression and responses to stimuli. We conducted an integrated analysis of chromatin accessibility, DNA methylation, and RNA expression across eight rat tissues following endurance exercise training (EET) to map epigenomic changes to transcriptional changes and determine key TFs involved. We uncovered tissue-specific changes and TF motif enrichment across all omic layers, differentially accessible regions (DARs), differentially methylated regions (DMRs), and differentially expressed genes (DEGs). We discovered distinct routes of EET-induced regulation through either epigenomic alterations providing better access for TFs to affect target genes, or via changes in TF expression or activity enabling target gene response. We identified TF motifs enriched among correlated epigenomic and transcriptomic alterations, DEGs correlated with exercise-related phenotypic changes, and EET-induced activity changes of TFs enriched for DEGs among their gene targets. This analysis elucidates the unique transcriptional regulatory mechanisms mediating diverse organ effects of EET.
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Affiliation(s)
- Gregory R Smith
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- These authors contributed equally
| | - Bingqing Zhao
- Department of Genetics, Stanford University, Stanford, CA 94305
- These authors contributed equally
| | - Malene E Lindholm
- Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305
| | - Archana Raja
- Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305
| | - Mark Viggars
- Department of Physiology and Aging, University of Florida, Gainesville, Florida 32610
| | - Hanna Pincas
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Nicole R Gay
- Department of Genetics, Stanford University, Stanford, CA 94305
| | - Yifei Sun
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Yongchao Ge
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Venugopalan D Nair
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - James A Sanford
- Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Mary Anne S Amper
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Mital Vasoya
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kevin S Smith
- Department of Genetics, Stanford University, Stanford, CA 94305
- Department of Pathology, Stanford University, Stanford, CA 94305
| | - Stephen Montgomery
- Department of Genetics, Stanford University, Stanford, CA 94305
- Department of Pathology, Stanford University, Stanford, CA 94305
| | - Elena Zaslavsky
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sue C Bodine
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Karyn A Esser
- Department of Physiology and Aging, University of Florida, Gainesville, Florida 32610
| | - Martin J Walsh
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029
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15
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Eljilany I, Saghand PG, Chen J, Ratan A, McCarter M, Carpten J, Colman H, Ikeguchi AP, Puzanov I, Arnold S, Churchman M, Hwu P, Conejo-Garcia J, Dalton WS, Weiner GJ, El Naqa IM, Tarhini AA. The T Cell Immunoscore as a Reference for Biomarker Development Utilizing Real-World Data from Patients with Advanced Malignancies Treated with Immune Checkpoint Inhibitors. Cancers (Basel) 2023; 15:4913. [PMID: 37894280 PMCID: PMC10605389 DOI: 10.3390/cancers15204913] [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: 08/24/2023] [Revised: 09/14/2023] [Accepted: 09/29/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND We aimed to determine the prognostic value of an immunoscore reflecting CD3+ and CD8+ T cell density estimated from real-world transcriptomic data of a patient cohort with advanced malignancies treated with immune checkpoint inhibitors (ICIs) in an effort to validate a reference for future machine learning-based biomarker development. METHODS Transcriptomic data was collected under the Total Cancer Care Protocol (NCT03977402) Avatar® project. The real-world immunoscore for each patient was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells utilizing CIBERSORTx and the LM22 gene signature matrix. Then, the immunoscore association with overall survival (OS) was estimated using Cox regression and analyzed using Kaplan-Meier curves. The OS predictions were assessed using Harrell's concordance index (C-index). The Youden index was used to identify the optimal cut-off point. Statistical significance was assessed using the log-rank test. RESULTS Our study encompassed 522 patients with four cancer types. The median duration to death was 10.5 months for the 275 participants who encountered an event. For the entire cohort, the results demonstrated that transcriptomics-based immunoscore could significantly predict patients at risk of death (p-value < 0.001). Notably, patients with an intermediate-high immunoscore achieved better OS than those with a low immunoscore. In subgroup analysis, the prediction of OS was significant for melanoma and head and neck cancer patients but did not reach significance in the non-small cell lung cancer or renal cell carcinoma cohorts. CONCLUSIONS Calculating CD3+ and CD8+ T cell immunoscore using real-world transcriptomic data represents a promising signature for estimating OS with ICIs and can be used as a reference for future machine learning-based biomarker development.
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Affiliation(s)
- Islam Eljilany
- Departments of Cutaneous Oncology and Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Payman Ghasemi Saghand
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - James Chen
- Department of Internal Medicine, Division of Medical Oncology, Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Aakrosh Ratan
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Martin McCarter
- Division of Surgical Oncology, Department of Surgery, School of Medicine, University of Colorado, Aurora, CO 80045, USA
| | - John Carpten
- USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
| | - Howard Colman
- Department of Neurosurgery, School of Medicine, University of Utah, Salt Lake City, UT 84132, USA
- Huntsman Cancer Institute, Salt Lake City, UT 84132, USA
| | | | - Igor Puzanov
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA
| | - Susanne Arnold
- University of Kentucky Markey Cancer Center, Lexington, KY 40536, USA
| | - Michelle Churchman
- Clinical & Life Sciences Department, Aster Insights, Hudson, FL 34667, USA
| | - Patrick Hwu
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Jose Conejo-Garcia
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | | | - George J. Weiner
- Department of Internal Medicine, Carver College of Medicine, University of Iowa Health Care, Iowa City, IA 52242, USA
| | - Issam M. El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Ahmad A. Tarhini
- Departments of Cutaneous Oncology and Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
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16
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Wrobel TJ, Brilhaus D, Stefanski A, Stühler K, Weber APM, Linka N. Mapping the castor bean endosperm proteome revealed a metabolic interaction between plastid, mitochondria, and peroxisomes to optimize seedling growth. FRONTIERS IN PLANT SCIENCE 2023; 14:1182105. [PMID: 37868318 PMCID: PMC10588648 DOI: 10.3389/fpls.2023.1182105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/07/2023] [Indexed: 10/24/2023]
Abstract
In this work, we studied castor-oil plant Ricinus communis as a classical system for endosperm reserve breakdown. The seeds of castor beans consist of a centrally located embryo with the two thin cotyledons surrounded by the endosperm. The endosperm functions as major storage tissue and is packed with nutritional reserves, such as oil, proteins, and starch. Upon germination, mobilization of the storage reserves requires inter-organellar interplay of plastids, mitochondria, and peroxisomes to optimize growth for the developing seedling. To understand their metabolic interactions, we performed a large-scale organellar proteomic study on castor bean endosperm. Organelles from endosperm of etiolated seedlings were isolated and subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS). Computer-assisted deconvolution algorithms were applied to reliably assign the identified proteins to their correct subcellular localization and to determine the abundance of the different organelles in the heterogeneous protein samples. The data obtained were used to build a comprehensive metabolic model for plastids, mitochondria, and peroxisomes during storage reserve mobilization in castor bean endosperm.
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Affiliation(s)
- Thomas J. Wrobel
- Institute of Plant Biochemistry and Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University, Düsseldorf, Germany
| | - Dominik Brilhaus
- Institute of Plant Biochemistry and Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University, Düsseldorf, Germany
| | - Anja Stefanski
- Molecular Proteomics Laboratory, Biologisch-Medizinisches Forschungszentrum (BMFZ), Universitätsklinikum, Düsseldorf, Germany
| | - Kai Stühler
- Molecular Proteomics Laboratory, Biologisch-Medizinisches Forschungszentrum (BMFZ), Universitätsklinikum, Düsseldorf, Germany
| | - Andreas P. M. Weber
- Institute of Plant Biochemistry and Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University, Düsseldorf, Germany
| | - Nicole Linka
- Institute of Plant Biochemistry and Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University, Düsseldorf, Germany
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17
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Studham M, Vazquez‐Mateo C, Samy E, Haselmayer P, Aydemir A, Rolfe PA, Merrill JT, Morand EF, DeMartino J, Kao A, Townsend R. Identifying lupus Patient Subsets Through Immune Cell Deconvolution of Gene Expression Data in Two Atacicept Phase II Studies. ACR Open Rheumatol 2023; 5:536-546. [PMID: 37710418 PMCID: PMC10570667 DOI: 10.1002/acr2.11594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 06/06/2023] [Accepted: 07/03/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVE To use cell-based gene signatures to identify patients with systemic lupus erythematous (SLE) in the phase II/III APRIL-SLE and phase IIb ADDRESS II trials most likely to respond to atacicept. METHODS A published immune cell deconvolution algorithm based on Affymetrix gene array data was applied to whole blood gene expression from patients entering APRIL-SLE. Five distinct patient clusters were identified. Patient characteristics, biomarkers, and clinical response to atacicept were assessed per cluster. A modified immune cell deconvolution algorithm was developed based on RNA sequencing data and applied to ADDRESS II data to identify similar patient clusters and their responses. RESULTS Patients in APRIL-SLE (N = 105) were segregated into the following five clusters (P1-5) characterized by dominant cell subset signatures: high neutrophils, T helper cells and natural killer (NK) cells (P1), high plasma cells and activated NK cells (P2), high B cells and neutrophils (P3), high B cells and low neutrophils (P4), or high activated dendritic cells, activated NK cells, and neutrophils (P5). Placebo- and atacicept-treated patients in clusters P2,4,5 had markedly higher British Isles Lupus Assessment Group (BILAG) A/B flare rates than those in clusters P1,3, with a greater treatment effect of atacicept on lowering flares in clusters P2,4,5. In ADDRESS II, placebo-treated patients from P2,4,5 were less likely to be SLE Responder Index (SRI)-4, SRI-6, and BILAG-Based Combined Lupus Assessment responders than those in P1,3; the response proportions again suggested lower placebo effect and a greater treatment differential for atacicept in P2,4,5. CONCLUSION This exploratory analysis indicates larger differences between placebo- and atacicept-treated patients with SLE in a molecularly defined patient subset.
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Affiliation(s)
| | | | | | | | | | | | - Joan T. Merrill
- University of Oklahoma Health Sciences CenterOklahoma CityOKUnited States
| | - Eric F. Morand
- Monash University School of Clinical SciencesClaytonAustralia
| | | | - Amy Kao
- EMD SeronoBillericaMAUnited States
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18
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Tran KA, Addala V, Johnston RL, Lovell D, Bradley A, Koufariotis LT, Wood S, Wu SZ, Roden D, Al-Eryani G, Swarbrick A, Williams ED, Pearson JV, Kondrashova O, Waddell N. Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures. Nat Commun 2023; 14:5758. [PMID: 37717006 PMCID: PMC10505141 DOI: 10.1038/s41467-023-41385-5] [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: 07/31/2022] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.
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Affiliation(s)
- Khoa A Tran
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia
| | - Venkateswar Addala
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Rebecca L Johnston
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - David Lovell
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- QUT Centre for Data Science, Brisbane, QLD, 4000, Australia
| | - Andrew Bradley
- Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Lambros T Koufariotis
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Scott Wood
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Sunny Z Wu
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Daniel Roden
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Ghamdan Al-Eryani
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Elizabeth D Williams
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia
- Australian Prostate Cancer Research Centre - Queensland (APCRC-Q) and Queensland Bladder Cancer Initiative (QBCI), Brisbane, QLD, 4000, Australia
| | - John V Pearson
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Olga Kondrashova
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Nicola Waddell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia.
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia.
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19
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Collins SM, Alexander KA, Lundh S, Dimitri AJ, Zhang Z, Good CR, Fraietta JA, Berger SL. TOX2 coordinates with TET2 to positively regulate central memory differentiation in human CAR T cells. SCIENCE ADVANCES 2023; 9:eadh2605. [PMID: 37467321 PMCID: PMC10355826 DOI: 10.1126/sciadv.adh2605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 06/14/2023] [Indexed: 07/21/2023]
Abstract
Chimeric antigen receptor (CAR) T cell therapy is used in treating human hematological malignancies, but its efficacy is limited by T cell exhaustion (TEX). TEX arises at the expense of central memory T cells (TCM), which exhibit robust antitumor efficacy. Reduction of the TET2 gene led to increased TCM differentiation in a patient with leukemia who experienced a complete remission. We show that loss of TET2 led to increased chromatin accessibility at exhaustion regulators TOX and TOX2, plus increased expression of TOX2. Knockdown of TOX increased the percentage of TCM. However, unexpectedly, knockdown of TOX2 decreased TCM percentage and reduced proliferation. Consistently, a TCM gene signature was reduced in the TOX2 knockdown, and TOX2 bound to promoters of numerous TCM genes. Our results thus suggest a role for human TOX2, in contrast to exhaustion regulator TOX, as a potentiator of central memory differentiation of CAR T cells, with plausible utility in CAR T cell cancer therapy via modulated TOX2 expression.
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Affiliation(s)
- Sierra M. Collins
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Katherine A. Alexander
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stefan Lundh
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Alexander J. Dimitri
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhen Zhang
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Charly R. Good
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joseph A. Fraietta
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA 19104, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shelley L. Berger
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Epigenetics Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Biology, University of Pennsylvania, Philadelphia PA 19104, USA
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20
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Chiu Y, Ni C, Huang Y. Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients. Cancer Med 2023; 12:15736-15760. [PMID: 37366298 PMCID: PMC10417088 DOI: 10.1002/cam4.6197] [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: 04/13/2022] [Revised: 05/02/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Many studies have utilized computational methods, including cell composition deconvolution (CCD), to correlate immune cell polarizations with the survival of cancer patients, including those with hepatocellular carcinoma (HCC). However, currently available cell deconvolution estimated (CDE) tools do not cover the wide range of immune cell changes that are known to influence tumor progression. RESULTS A new CCD tool, HCCImm, was designed to estimate the abundance of tumor cells and 16 immune cell types in the bulk gene expression profiles of HCC samples. HCCImm was validated using real datasets derived from human peripheral blood mononuclear cells (PBMCs) and HCC tissue samples, demonstrating that HCCImm outperforms other CCD tools. We used HCCImm to analyze the bulk RNA-seq datasets of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples. We found that the proportions of memory CD8+ T cells and Tregs were negatively associated with patient overall survival (OS). Furthermore, the proportion of naïve CD8+ T cells was positively associated with patient OS. In addition, the TCGA-LIHC samples with a high tumor mutational burden had a significantly high abundance of nonmacrophage leukocytes. CONCLUSIONS HCCImm was equipped with a new set of reference gene expression profiles that allowed for a more robust analysis of HCC patient expression data. The source code is provided at https://github.com/holiday01/HCCImm.
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Affiliation(s)
- Yen‐Jung Chiu
- Institute of Biomedical InformaticsNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of Biomedical EngineeringMing Chuan UniversityTaoyuanTaiwan
| | - Chung‐En Ni
- Institute of Biomedical InformaticsNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Yen‐Hua Huang
- Institute of Biomedical InformaticsNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Systems and Synthetic BiologyNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
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21
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Momeni K, Ghorbian S, Ahmadpour E, Sharifi R. Identification of molecular mechanisms causing skin lesions of cutaneous leishmaniasis using weighted gene coexpression network analysis (WGCNA). Sci Rep 2023; 13:9836. [PMID: 37330553 PMCID: PMC10276835 DOI: 10.1038/s41598-023-35868-0] [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: 02/23/2023] [Accepted: 05/25/2023] [Indexed: 06/19/2023] Open
Abstract
Leishmaniasis is an infectious disease, caused by a protozoan parasite. Its most common form is cutaneous leishmaniasis, which leaves scars on exposed body parts from bites by infected female phlebotomine sandflies. Approximately 50% of cases of cutaneous leishmaniasis fail to respond to standard treatments, creating slow-healing wounds which cause permanent scars on the skin. We performed a joint bioinformatics analysis to identify differentially expressed genes (DEGs) in healthy skin biopsies and Leishmania cutaneous wounds. DEGs and WGCNA modules were analyzed based on the Gene Ontology function, and the Cytoscape software. Among almost 16,600 genes that had significant expression changes on the skin surrounding Leishmania wounds, WGCNA determined that one of the modules, with 456 genes, has the strongest correlation with the size of the wounds. Functional enrichment analysis indicated that this module includes three gene groups with significant expression changes. These produce tissue-damaging cytokines or disrupt the production and activation of collagen, fibrin proteins, and the extracellular matrix, causing skin wounds or preventing them from healing. The hub genes of these groups are OAS1, SERPINH1, and FBLN1 respectively. This information can provide new ways to deal with unwanted and harmful effects of cutaneous leishmaniasis.
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Affiliation(s)
- Kavoos Momeni
- Department of Molecular Genetics, Ahar Branch, Islamic Azad University, Ahar, Iran
| | - Saeid Ghorbian
- Department of Molecular Genetics, Ahar Branch, Islamic Azad University, Ahar, Iran.
| | - Ehsan Ahmadpour
- Infectious and Tropical Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Rasoul Sharifi
- Department of Biology, Faculty of Basic Science, Ahar Branch, Islamic Azad University, Ahar, Iran
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22
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Zhou M, Zhang H, Baii Z, Mann-Krzisnik D, Wang F, Li Y. Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.31.526312. [PMID: 36778483 PMCID: PMC9915637 DOI: 10.1101/2023.01.31.526312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The advent of single-cell multi-omics sequencing technology makes it possible for re-searchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human bone marrow mononuclear cells (BMMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.
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Affiliation(s)
- Manqi Zhou
- Department of Computational Biology, Cornell University
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine
| | - Hao Zhang
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
| | - Zilong Baii
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
| | | | - Fei Wang
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
| | - Yue Li
- Quantitative Life Science, McGill University
- School of Computer Science, McGill University
- Mila - Quebec AI Institute
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23
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Kalatskaya I, Giovannoni G, Leist T, Cerra J, Boschert U, Rolfe PA. Revealing the immune cell subtype reconstitution profile in patients from the CLARITY study using deconvolution algorithms after cladribine tablets treatment. Sci Rep 2023; 13:8067. [PMID: 37202447 DOI: 10.1038/s41598-023-34384-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 04/28/2023] [Indexed: 05/20/2023] Open
Abstract
Immune Cell Deconvolution methods utilizing gene expression profiling to quantify immune cells in tissues and blood are an appealing alternative to flow cytometry. Our objective was to investigate the applicability of deconvolution approaches in clinical trial settings to better investigate the mode of action of drugs for autoimmune diseases. Popular deconvolution methods CIBERSORT and xCell were validated using gene expression from the publicly available GSE93777 dataset that has comprehensive matching flow cytometry. As shown in the online tool, ~ 50% of signatures show strong correlation (r > 0.5) with the remainder showing moderate correlation, or in a few cases, no correlation. Deconvolution methods were then applied to gene expression data from the phase III CLARITY study (NCT00213135) to evaluate the immune cell profile of relapsing multiple sclerosis patients treated with cladribine tablets. At 96 weeks after treatment, deconvolution scores showed the following changes vs placebo: naïve, mature, memory CD4+ and CD8+ T cells, non-class switched, and class switched memory B cells and plasmablasts were significantly reduced, naïve B cells and M2 macrophages were more abundant. Results confirm previously described changes in immune cell composition following cladribine tablets treatment and reveal immune homeostasis of pro- vs anti-inflammatory immune cell subtypes, potentially supporting long-term efficacy.
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Affiliation(s)
- Irina Kalatskaya
- EMD Serono Research & Development Institute, Inc. (an affiliate of Merck KGaA), 45 Middlesex Turnpike, Billerica, MA, 01821, USA.
| | - Gavin Giovannoni
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Thomas Leist
- Division of Clinical Neuroimmunology, Jefferson University, Comprehensive MS Center, Philadelphia, PA, USA
| | - Joseph Cerra
- EMD Serono Research & Development Institute, Inc. (an affiliate of Merck KGaA), 45 Middlesex Turnpike, Billerica, MA, 01821, USA
- BISC Global, Boston, MA, USA
| | - Ursula Boschert
- Ares Trading S.A. (an affiliate of Merck KGaA), Eysins, Switzerland
| | - P Alexander Rolfe
- EMD Serono Research & Development Institute, Inc. (an affiliate of Merck KGaA), 45 Middlesex Turnpike, Billerica, MA, 01821, USA
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24
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Merchant JP, Zhu K, Henrion MYR, Zaidi SSA, Lau B, Moein S, Alamprese ML, Pearse RV, Bennett DA, Ertekin-Taner N, Young-Pearse TL, Chang R. Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease. Commun Biol 2023; 6:503. [PMID: 37188718 PMCID: PMC10185548 DOI: 10.1038/s42003-023-04791-5] [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: 12/30/2021] [Accepted: 03/31/2023] [Indexed: 05/17/2023] Open
Abstract
Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.
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Affiliation(s)
- Julie P Merchant
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Neuroscience Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kuixi Zhu
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Marc Y R Henrion
- Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, Pembroke Place, L3 5QA, UK
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, PO Box 30096, Blantyre, Malawi
| | - Syed S A Zaidi
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Branden Lau
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
- Arizona Research Labs, Genetics Core, University of Arizona, Tucson, AZ, USA
| | - Sara Moein
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Melissa L Alamprese
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Tracy L Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Harvard Stem Cell Institute, Harvard University, Boston, MA, USA.
| | - Rui Chang
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA.
- Department of Neurology, University of Arizona, Tucson, AZ, USA.
- INTelico Therapeutics LLC, Tucson, AZ, USA.
- PATH Biotech LLC, Tucson, AZ, USA.
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25
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Meyran D, Zhu JJ, Butler J, Tantalo D, MacDonald S, Nguyen TN, Wang M, Thio N, D'Souza C, Qin VM, Slaney C, Harrison A, Sek K, Petrone P, Thia K, Giuffrida L, Scott AM, Terry RL, Tran B, Desai J, Prince HM, Harrison SJ, Beavis PA, Kershaw MH, Solomon B, Ekert PG, Trapani JA, Darcy PK, Neeson PJ. T STEM-like CAR-T cells exhibit improved persistence and tumor control compared with conventional CAR-T cells in preclinical models. Sci Transl Med 2023; 15:eabk1900. [PMID: 37018415 DOI: 10.1126/scitranslmed.abk1900] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Patients who receive chimeric antigen receptor (CAR)-T cells that are enriched in memory T cells exhibit better disease control as a result of increased expansion and persistence of the CAR-T cells. Human memory T cells include stem-like CD8+ memory T cell progenitors that can become either functional stem-like T (TSTEM) cells or dysfunctional T progenitor exhausted (TPEX) cells. To that end, we demonstrated that TSTEM cells were less abundant in infused CAR-T cell products in a phase 1 clinical trial testing Lewis Y-CAR-T cells (NCT03851146), and the infused CAR-T cells displayed poor persistence in patients. To address this issue, we developed a production protocol to generate TSTEM-like CAR-T cells enriched for expression of genes in cell replication pathways. Compared with conventional CAR-T cells, TSTEM-like CAR-T cells had enhanced proliferative capacity and increased cytokine secretion after CAR stimulation, including after chronic CAR stimulation in vitro. These responses were dependent on the presence of CD4+ T cells during TSTEM-like CAR-T cell production. Adoptive transfer of TSTEM-like CAR-T cells induced better control of established tumors and resistance to tumor rechallenge in preclinical models. These more favorable outcomes were associated with increased persistence of TSTEM-like CAR-T cells and an increased memory T cell pool. Last, TSTEM-like CAR-T cells and anti-programmed cell death protein 1 (PD-1) treatment eradicated established tumors, and this was associated with increased tumor-infiltrating CD8+CAR+ T cells producing interferon-γ. In conclusion, our CAR-T cell protocol generated TSTEM-like CAR-T cells with enhanced therapeutic efficacy, resulting in increased proliferative capacity and persistence in vivo.
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Affiliation(s)
- Deborah Meyran
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Université de Paris, Inserm, U976 HIPI Unit, Institut de Recherche Saint-Louis, Paris F-75010, France
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Joe Jiang Zhu
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Jeanne Butler
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Daniela Tantalo
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Sean MacDonald
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Thu Ngoc Nguyen
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Minyu Wang
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Niko Thio
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Criselle D'Souza
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Vicky Mengfei Qin
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Clare Slaney
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Aaron Harrison
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Kevin Sek
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Pasquale Petrone
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Kevin Thia
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Lauren Giuffrida
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
| | - Andrew M Scott
- Tumor Targeting Laboratory, Olivia Newton-John Cancer Research Institute, Austin Health, Heidelberg, VIC 3084, Australia
- School of Cancer Medicine, La Trobe University, Melbourne, VIC 3086, Australia
| | - Rachael L Terry
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW 1466, Australia
| | - Ben Tran
- Division of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Jayesh Desai
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
- Division of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - H Miles Prince
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Division of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Simon J Harrison
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
- Division of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Paul A Beavis
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Michael H Kershaw
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Ben Solomon
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
- Division of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Paul G Ekert
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW 1466, Australia
- School of Women's and Children's Health, UNSW Sydney, Sydney, NSW 1466, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW 2031, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3052, Australia
| | - Joseph A Trapani
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Phillip K Darcy
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
| | - Paul J Neeson
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne 3010, Australia
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26
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Dunlap G, Wagner A, Meednu N, Zhang F, Jonsson AH, Wei K, Sakaue S, Nathan A, Bykerk VP, Donlin LT, Goodman SM, Firestein GS, Boyle DL, Holers VM, Moreland LW, Tabechian D, Pitzalis C, Filer A, Raychaudhuri S, Brenner MB, McDavid A, Rao DA, Anolik JH. Clonal associations of lymphocyte subsets and functional states revealed by single cell antigen receptor profiling of T and B cells in rheumatoid arthritis synovium. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.18.533282. [PMID: 36993527 PMCID: PMC10055242 DOI: 10.1101/2023.03.18.533282] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease initiated by antigen-specific T cells and B cells, which promote synovial inflammation through a complex set of interactions with innate immune and stromal cells. To better understand the phenotypes and clonal relationships of synovial T and B cells, we performed single-cell RNA and repertoire sequencing on paired synovial tissue and peripheral blood samples from 12 donors with seropositive RA ranging from early to chronic disease. Paired transcriptomic-repertoire analyses highlighted 3 clonally distinct CD4 T cells populations that were enriched in RA synovium: T peripheral helper (Tph) and T follicular helper (Tfh) cells, CCL5+ T cells, and T regulatory cells (Tregs). Among these cells, Tph cells showed a unique transcriptomic signature of recent T cell receptor (TCR) activation, and clonally expanded Tph cells expressed an elevated transcriptomic effector signature compared to non-expanded Tph cells. CD8 T cells showed higher oligoclonality than CD4 T cells, and the largest CD8 T cell clones in synovium were highly enriched in GZMK+ cells. TCR analyses revealed CD8 T cells with likely viral-reactive TCRs distributed across transcriptomic clusters and definitively identified MAIT cells in synovium, which showed transcriptomic features of TCR activation. Among B cells, non-naive B cells including age-associated B cells (ABC), NR4A1+ activated B cells, and plasma cells, were enriched in synovium and had higher somatic hypermutation rates compared to blood B cells. Synovial B cells demonstrated substantial clonal expansion, with ABC, memory, and activated B cells clonally linked to synovial plasma cells. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate RA synovium.
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Affiliation(s)
- Garrett Dunlap
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
| | - Aaron Wagner
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry; Rochester, NY, USA
| | - Nida Meednu
- Division of Allergy, Immunology and Rheumatology, University of Rochester Medical Center; Rochester, NY, USA
| | - Fan Zhang
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital; Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital; Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School; Boston, MA, USA
- Broad Institute of MIT and Harvard; Cambridge, MA, USA
- Division of Rheumatology and the Center for Health Artificial Intelligence, University of Colorado School of Medicine; Aurora, CO, USA
| | - A Helena Jonsson
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
| | - Saori Sakaue
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital; Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital; Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School; Boston, MA, USA
- Broad Institute of MIT and Harvard; Cambridge, MA, USA
| | - Aparna Nathan
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital; Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital; Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School; Boston, MA, USA
- Broad Institute of MIT and Harvard; Cambridge, MA, USA
| | - Vivian P Bykerk
- Hospital for Special Surgery; New York, NY, USA
- Weill Cornell Medicine; New York, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery; New York, NY, USA
- Weill Cornell Medicine; New York, NY, USA
| | - Susan M Goodman
- Hospital for Special Surgery; New York, NY, USA
- Weill Cornell Medicine; New York, NY, USA
| | - Gary S Firestein
- Division of Rheumatology, Allergy, and Immunology, University of California, San Diego; La Jolla, CA, USA
| | - David L Boyle
- Division of Rheumatology, Allergy, and Immunology, University of California, San Diego; La Jolla, CA, USA
| | - V Michael Holers
- Division of Rheumatology, University of Colorado School of Medicine; Aurora, CO, USA
| | - Larry W Moreland
- Division of Rheumatology, University of Colorado School of Medicine; Aurora, CO, USA
- Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine; Pittsburgh, PA, USA
| | - Darren Tabechian
- Division of Allergy, Immunology and Rheumatology, University of Rochester Medical Center; Rochester, NY, USA
| | - Costantino Pitzalis
- Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute, Queen Mary University of London; London, UK
| | - Andrew Filer
- Rheumatology Research Group, Institute for Inflammation and Ageing, University of Birmingham, NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital; Birmingham, UK
| | - Soumya Raychaudhuri
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital; Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital; Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School; Boston, MA, USA
- Broad Institute of MIT and Harvard; Cambridge, MA, USA
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester; Manchester, UK
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry; Rochester, NY, USA
| | - Deepak A Rao
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School; Boston, MA, USA
| | - Jennifer H Anolik
- Division of Allergy, Immunology and Rheumatology, University of Rochester Medical Center; Rochester, NY, USA
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Chen L, Li Z, Wu H. CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data. Genome Biol 2023; 24:37. [PMID: 36855165 PMCID: PMC9972684 DOI: 10.1186/s13059-023-02857-5] [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: 02/07/2022] [Accepted: 01/17/2023] [Indexed: 03/02/2023] Open
Abstract
Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.
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Affiliation(s)
- Luxiao Chen
- Department of Biostatistics and Bioinformatics, Emory University, GA 30322 Atlanta, USA
| | - Ziyi Li
- Department of Biostatistics, The University of MD Anderson Cancer Center, 77030 Houston, TX, USA
| | - Hao Wu
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055 P.R. China
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28
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Kelly DE, Ramdas S, Ma R, Rawlings-Goss RA, Grant GR, Ranciaro A, Hirbo JB, Beggs W, Yeager M, Chanock S, Nyambo TB, Omar SA, Woldemeskel D, Belay G, Li H, Brown CD, Tishkoff SA. The genetic and evolutionary basis of gene expression variation in East Africans. Genome Biol 2023; 24:35. [PMID: 36829244 PMCID: PMC9951478 DOI: 10.1186/s13059-023-02874-4] [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: 03/03/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Mapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases, though most studies have focused on individuals of European descent. While important progress has been made to study a greater diversity of human populations, many groups remain unstudied, particularly among indigenous populations within Africa. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations. RESULTS We find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine-mapping of causal variation. Integrating our QTL scans with signatures of natural selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation. CONCLUSION Extending QTL mapping studies beyond European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.
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Affiliation(s)
- Derek E Kelly
- Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Shweta Ramdas
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rong Ma
- Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - Jibril B Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - William Beggs
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Meredith Yeager
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Institutes of Health, Rockville, MD, USA
| | - Thomas B Nyambo
- Department of Biochemistry, Kampala International University in Tanzania, Dar Es Salaam, Tanzania
| | - Sabah A Omar
- Center for Biotechnology Research and Development, Kenya Medical Research Institute, Nairobi, Kenya
| | - Dawit Woldemeskel
- Microbial Cellular and Molecular Biology Department, Addis Ababa University, Addis Ababa, Ethiopia
| | - Gurja Belay
- Microbial Cellular and Molecular Biology Department, Addis Ababa University, Addis Ababa, Ethiopia
| | - Hongzhe Li
- Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D Brown
- Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah A Tishkoff
- Genetics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Biology, University of Pennsylvania, Philadelphia, USA.
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29
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Zagorulya M, Yim L, Morgan DM, Edwards A, Torres-Mejia E, Momin N, McCreery CV, Zamora IL, Horton BL, Fox JG, Wittrup KD, Love JC, Spranger S. Tissue-specific abundance of interferon-gamma drives regulatory T cells to restrain DC1-mediated priming of cytotoxic T cells against lung cancer. Immunity 2023; 56:386-405.e10. [PMID: 36736322 PMCID: PMC10880816 DOI: 10.1016/j.immuni.2023.01.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 12/27/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023]
Abstract
Local environmental factors influence CD8+ T cell priming in lymph nodes (LNs). Here, we sought to understand how factors unique to the tumor-draining mediastinal LN (mLN) impact CD8+ T cell responses toward lung cancer. Type 1 conventional dendritic cells (DC1s) showed a mLN-specific failure to induce robust cytotoxic T cells responses. Using regulatory T (Treg) cell depletion strategies, we found that Treg cells suppressed DC1s in a spatially coordinated manner within tissue-specific microniches within the mLN. Treg cell suppression required MHC II-dependent contact between DC1s and Treg cells. Elevated levels of IFN-γ drove differentiation Treg cells into Th1-like effector Treg cells in the mLN. In patients with cancer, Treg cell Th1 polarization, but not CD8+/Treg cell ratios, correlated with poor responses to checkpoint blockade immunotherapy. Thus, IFN-γ in the mLN skews Treg cells to be Th1-like effector Treg cells, driving their close interaction with DC1s and subsequent suppression of cytotoxic T cell responses.
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Affiliation(s)
- Maria Zagorulya
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA; Department of Biology, MIT, Cambridge, MA 02139, USA
| | - Leon Yim
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA
| | - Duncan M Morgan
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA; Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Austin Edwards
- Biological Imaging Development CoLab, UCSF, San Francisco, CA 94143, USA
| | - Elen Torres-Mejia
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA
| | - Noor Momin
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA
| | - Chloe V McCreery
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA
| | - Izabella L Zamora
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
| | - Brendan L Horton
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA
| | - James G Fox
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Division of Comparative Medicine, MIT, Cambridge, MA 02139, USA
| | - K Dane Wittrup
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA; Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA
| | - J Christopher Love
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA; Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Stefani Spranger
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA; Department of Biology, MIT, Cambridge, MA 02139, USA; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA.
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30
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Teh RQ, Liu GS, Wang JH. Bioinformatics Tools for Bulk Gene Expression Deconvolution in Diabetic Retinopathy. Methods Mol Biol 2023; 2678:107-115. [PMID: 37326707 DOI: 10.1007/978-1-0716-3255-0_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Retinal neovascularization is one of the leading causes of vision loss and a hallmark of proliferative diabetic retinopathy (PDR). The immune system is observed to be involved in the pathogenesis of diabetic retinopathy (DR). The specific immune cell type that contributes to retinal neovascularization can be identified via a bioinformatics analysis of RNA sequencing (RNA-seq) data, known as deconvolution analysis. Previous study has identified the infiltration of macrophages in the retina of rats with hypoxia-induced retinal neovascularization and patients with PDR through a deconvolution algorithm, known as CIBERSORTx. Here, we describe the protocols of using CIBERSORTx to perform the deconvolution analysis and downstream analysis of RNA-seq data.
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Affiliation(s)
- Ru Qi Teh
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
| | - Guei-Sheung Liu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
- Ophthalmology, Department of Surgery, University of Melbourne, East Melbourne, VIC, Australia.
| | - Jiang-Hui Wang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.
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31
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Xiao B, Li M, Cui M, Yin C, Zhang B. A large-scale screening and functional sorting of tumour microenvironment prognostic genes for breast cancer patients. Front Endocrinol (Lausanne) 2023; 14:1131525. [PMID: 36936167 PMCID: PMC10014861 DOI: 10.3389/fendo.2023.1131525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
PURPOSE The aim of this study was to systematically establish a comprehensive tumour microenvironment (TME)-relevant prognostic gene and target miRNA network for breast cancer patients. METHODS Based on a large-scale screening of TME-relevant prognostic genes (760 genes) for breast cancer patients, the prognostic model was established. The primary TME prognostic genes were selected from the constructing database and verified in the testing database. The internal relationships between the potential TME prognostic genes and the prognosis of breast cancer patients were explored in depth. The associated miRNAs for the TME prognostic genes were generated, and the functions of each primary TME member were investigated in the breast cancer cell line. RESULTS Compared with sibling controls, breast cancer patients showed 55 differentially expressed TME prognostic genes, of which 31 were considered as protective genes, while the remaining 24 genes were considered as risk genes. According to the lambda values of the LASSO Cox analysis, the 15 potential TME prognostic genes were as follows: ENPEP, CCDC102B, FEZ1, NOS2, SCG2, RPLP2, RELB, RGS3, EMP1, PDLIM4, EPHA3, PCDH9, VIM, GFI1, and IRF1. Among these, there was a remarkable linear internal relationship for CCDC102B but non-linear relationships for others with breast cancer patient prognosis. Using the siRNA technique, we silenced the expression of each TME prognostic gene. Seven of the 15 TME prognostic genes (NOS2, SCG2, RGS3, EMP1, PDLIM4, PCDH9, and GFI1) were involved in enhancing cell proliferation, destroying cell apoptosis, promoting cell invasion, or migration in breast cancer. Six of them (CCDC102B, RPLP2, RELB, EPHA3, VIM, and IRF1) were favourable for maintaining cell invasion or migration. Only two of them (ENPEP and FEZ1) were favourable for the processes of cell proliferation and apoptosis. CONCLUSIONS This integrated study hypothesised an innovative TME-associated genetic functional network for breast cancer patients. The external relationships between these TME prognostic genes and the disease were measured. Meanwhile, the internal molecular mechanisms were also investigated.
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Affiliation(s)
- Bo Xiao
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Cellular and Molecular Immunology, Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Mingwei Li
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Cellular and Molecular Immunology, Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Mingxuan Cui
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Cellular and Molecular Immunology, Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China
- *Correspondence: Bo Zhang, ; Chengliang Yin,
| | - Bo Zhang
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Cellular and Molecular Immunology, Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- *Correspondence: Bo Zhang, ; Chengliang Yin,
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32
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Vanhaverbeke M, Attard R, Bartekova M, Ben-Aicha S, Brandenburger T, de Gonzalo-Calvo D, Emanueli C, Farrugia R, Grillari J, Hackl M, Kalocayova B, Martelli F, Scholz M, Wettinger SB, Devaux Y. Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129. Cardiovasc Res 2022; 118:3183-3197. [PMID: 34648023 PMCID: PMC9799060 DOI: 10.1093/cvr/cvab327] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 10/06/2021] [Accepted: 10/12/2021] [Indexed: 01/25/2023] Open
Abstract
Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.
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Affiliation(s)
- Maarten Vanhaverbeke
- Department of Cardiovascular Medicine, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Ritienne Attard
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida MSD 2080, Malta
| | - Monika Bartekova
- Institute for Heart Research, Centre of Experimental Medicine, Slovak Academy of Sciences, Dúbravská cesta 9, 84104 Bratislava, Slovakia
- Faculty of Medicine, Institute of Physiology, Comenius University, Sasinkova 2, 81372 Bratislava, Slovakia
| | - Soumaya Ben-Aicha
- Faculty of Medicine, Imperial College London, ICTEM Building, Du Cane Road, London W12 0NN, UK
| | - Timo Brandenburger
- Department of Anesthesiology, University Hospital Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - David de Gonzalo-Calvo
- Translational Research in Respiratory Medicine, IRBLleida, University Hospital Arnau de Vilanova and Santa Maria, Av. Alcalde Rovira Roure 80, 25198, Lleida, Spain
- CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Av. de Monforte de Lemos, 28029, Madrid, Spain
| | - Costanza Emanueli
- Faculty of Medicine, Imperial College London, ICTEM Building, Du Cane Road, London W12 0NN, UK
| | - Rosienne Farrugia
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida MSD 2080, Malta
| | - Johannes Grillari
- Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, AUVA Research Center, Donaueschingenstraße 13, 1200, Vienna, Austria
- Institute of Molecular Biotechnology, BOKU - University of Natural Resources and Life Sciences, Gregor-Mendel-Straße 33, 1180 Vienna, Austria
| | | | - Barbora Kalocayova
- Institute for Heart Research, Centre of Experimental Medicine, Slovak Academy of Sciences, Dúbravská cesta 9, 84104 Bratislava, Slovakia
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Milan 20097, Italy
| | - Markus Scholz
- Institute of Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany
| | - Stephanie Bezzina Wettinger
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida MSD 2080, Malta
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, 1A-B rue Edison, L-1445 Strassen, Luxembourg
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Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature. Cell Syst 2022; 13:989-1001.e8. [PMID: 36549275 DOI: 10.1016/j.cels.2022.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/05/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022]
Abstract
The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.
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34
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Hasanaj E, Alavi A, Gupta A, Póczos B, Bar-Joseph Z. Multiset multicover methods for discriminative marker selection. CELL REPORTS METHODS 2022; 2:100332. [PMID: 36452867 PMCID: PMC9701606 DOI: 10.1016/j.crmeth.2022.100332] [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] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/12/2022] [Accepted: 10/18/2022] [Indexed: 06/17/2023]
Abstract
Markers are increasingly being used for several high-throughput data analysis and experimental design tasks. Examples include the use of markers for assigning cell types in scRNA-seq studies, for deconvolving bulk gene expression data, and for selecting marker proteins in single-cell spatial proteomics studies. Most marker selection methods focus on differential expression (DE) analysis. Although such methods work well for data with a few non-overlapping marker sets, they are not appropriate for large atlas-size datasets where several cell types and tissues are considered. To address this, we define the phenotype cover (PC) problem for marker selection and present algorithms that can improve the discriminative power of marker sets. Analysis of these sets on several marker-selection tasks suggests that these methods can lead to solutions that accurately distinguish different phenotypes in the data.
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Affiliation(s)
- Euxhen Hasanaj
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Amir Alavi
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Anupam Gupta
- Computer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Barnabás Póczos
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Ziv Bar-Joseph
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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35
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Huang X, Jiang L, Wen Z, Yuan M, Zhong Y. Knockdown of TTC9 inhibits the proliferation, migration and invasion, but induces the apoptosis of lung adenocarcinoma cells. Heliyon 2022; 8:e11254. [PMID: 36339754 PMCID: PMC9634374 DOI: 10.1016/j.heliyon.2022.e11254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/23/2022] [Accepted: 10/20/2022] [Indexed: 11/19/2022] Open
Abstract
Lung adenocarcinoma (LUAD) is one of the most commonly diagnosed subtypes of lung cancer, and one of the deadliest cancers. Tetratricopeptide repeat domain 9A (TTC9) is upregulated and has played an oncogenic role in some malignant tumors. However, the expression and role of TTC9 has not yet been elucidated in LUAD. Here, we investigated the expression profiles, biological functions and potential molecular mechanism of the TTC9 gene in LUAD. TTC9 expression was significantly overexpressed in LUAD tissues compared with that in normal lung tissues. TTC9 expression was closely correlated with gender, lymph node metastasis, and survival status in the TCGA-LUAD cohort. Subsequent cellular function assays demonstrated that knockdown of TTC9 promoted PC9 cell apoptosis and inhibited cell proliferation, migration and invasion, leading to cell cycle arrest in G2 phase. Moreover, inhibition of TTC9 suppressed the tumorigenicity of PC9 cells in nude mice. TTC9 might serve as oncogene in LUAD through cancer-related signaling pathways including p38 MAPK pathway. The expression of TTC9 gene might be modulated by DNA copy number variant and DNA methylation. TTC9 was significantly associated with tumor immune infiltration patterns. Accordingly, TTC9 may be a novel therapeutic target for the treatment of LUAD.
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Affiliation(s)
- Xiaoyue Huang
- Medical College, Guangxi University, Nanning 530021, PR China
- Department of Thoracic Surgery, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning 530021, PR China
| | - Lingyu Jiang
- The First Affiliated Hospital, Jinan University, Guangzhou 510006, PR China
- Intensive Care Unit, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning 530021, PR China
| | - Zhaoke Wen
- Department of Thoracic Surgery, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning 530021, PR China
| | - Mingqing Yuan
- Medical College, Guangxi University, Nanning 530021, PR China
- Corresponding author.
| | - Yonglong Zhong
- Department of Thoracic Surgery, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning 530021, PR China
- Corresponding author.
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Tiwari A, Trivedi R, Lin SY. Tumor microenvironment: barrier or opportunity towards effective cancer therapy. J Biomed Sci 2022; 29:83. [PMID: 36253762 PMCID: PMC9575280 DOI: 10.1186/s12929-022-00866-3] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 10/01/2022] [Indexed: 12/24/2022] Open
Abstract
Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.
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Affiliation(s)
- Aadhya Tiwari
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Rakesh Trivedi
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shiaw-Yih Lin
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Chen D, Li S, Wang X. GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA. FOUNDATIONS OF DATA SCIENCE (SPRINGFIELD, MO.) 2022; 4:441-466. [PMID: 38250319 PMCID: PMC10798655 DOI: 10.3934/fods.2022013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Complete deconvolution analysis for bulk RNA-seq data is important and helpful to distinguish whether the differences of disease-associated GEPs (gene expression profiles) in tissues of patients and normal controls are due to changes in cellular composition of tissue samples, or due to GEPs changes in specific cells. One of the major techniques to perform complete deconvolution is nonnegative matrix factorization (NMF), which also has a wide-range of applications in the machine learning community. However, the NMF is a well-known strongly ill-posed problem, so a direct application of NMF to RNA-seq data will suffer severe difficulties in the interpretability of solutions. In this paper, we develop an NMF-based mathematical model and corresponding computational algorithms to improve the solution identifiability of deconvoluting bulk RNA-seq data. In our approach, we combine the biological concept of marker genes with the solvability conditions of the NMF theories, and develop a geometric structures guided optimization model. In this strategy, the geometric structure of bulk tissue data is first explored by the spectral clustering technique. Then, the identified information of marker genes is integrated as solvability constraints, while the overall correlation graph is used as manifold regularization. Both synthetic and biological data are used to validate the proposed model and algorithms, from which solution interpretability and accuracy are significantly improved.
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Affiliation(s)
- Duan Chen
- Department of Mathematics and Statistics School of Data Science University of North Carolina at Charlotte, USA
| | - Shaoyu Li
- Department of Mathematics and Statistics University of North Carolina at Charlotte, USA
| | - Xue Wang
- Department of Quantitative Health Sciences Mayo Clinic, Florida, 32224, USA
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van Doorn CLR, Eckold C, Ronacher K, Ruslami R, van Veen S, Lee JS, Kumar V, Kerry-Barnard S, Malherbe ST, Kleynhans L, Stanley K, Hill PC, Joosten SA, van Crevel R, Wijmenga C, Critchley JA, Walzl G, Alisjahbana B, Haks MC, Dockrell HM, Ottenhoff THM, Vianello E, Cliff JM. Transcriptional profiles predict treatment outcome in patients with tuberculosis and diabetes at diagnosis and at two weeks after initiation of anti-tuberculosis treatment. EBioMedicine 2022; 82:104173. [PMID: 35841871 PMCID: PMC9297076 DOI: 10.1016/j.ebiom.2022.104173] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Globally, the tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients. Treatment success is lower among people with diabetes mellitus (DM). Predicting treatment outcome early after diagnosis, especially in TB-DM patients, would allow early treatment adaptation for individuals and may improve global TB control. METHODS Samples were collected in a longitudinal cohort study of adult TB patients from South Africa (n = 94) and Indonesia (n = 81), who had concomitant DM (n = 59), intermediate hyperglycaemia (n = 79) or normal glycaemia/no DM (n = 37). Treatment outcome was monitored, and patients were categorized as having a good (cured) or poor (failed, recurrence, died) outcome during treatment and 12 months follow-up. Whole blood transcriptional profiles before, during and at the end of TB treatment were characterized using unbiased RNA-Seq and targeted gene dcRT-MLPA. FINDINGS We report differences in whole blood transcriptome profiles, which were observed before initiation of treatment and throughout treatment, between patients with a good versus poor TB treatment outcome. An eight-gene and a 22-gene blood transcriptional signature distinguished patients with a good TB treatment outcome from patients with a poor TB treatment outcome at diagnosis (AUC = 0·815) or two weeks (AUC = 0·834) after initiation of TB treatment, respectively. High accuracy was obtained by cross-validating this signature in an external cohort (AUC = 0·749). INTERPRETATION These findings suggest that transcriptional profiles can be used as a prognostic biomarker for treatment failure and success, even in patients with concomitant DM. FUNDING The research leading to these results, as part of the TANDEM Consortium, received funding from the European Community's Seventh Framework Programme (FP7/2007-2013 Grant Agreement No. 305279) and the Netherlands Organization for Scientific Research (NWO-TOP Grant Agreement No. 91214038). The research leading to the results presented in the Indian validation cohort was supported by Research Council of Norway Global Health and Vaccination Research (GLOBVAC) projects: RCN 179342, 192534, and 248042, the University of Bergen (Norway).
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Affiliation(s)
- Cassandra L R van Doorn
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, the Netherlands
| | - Clare Eckold
- Dept of Infection Biology and TB Centre, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, United Kingdom
| | - Katharina Ronacher
- SA MRC Centre for TB Research, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa; Mater Research Institute - The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Rovina Ruslami
- TB-HIV Research Center, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia; Hasan Sadikin General Hospital, Bandung, Indonesia
| | - Suzanne van Veen
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, the Netherlands
| | - Ji-Sook Lee
- Dept of Infection Biology and TB Centre, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, United Kingdom
| | - Vinod Kumar
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Sarah Kerry-Barnard
- Population Health Research Institute, St George's Hospital Medical School, University of London
| | - Stephanus T Malherbe
- SA MRC Centre for TB Research, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Léanie Kleynhans
- SA MRC Centre for TB Research, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Kim Stanley
- SA MRC Centre for TB Research, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Philip C Hill
- Centre for International Health, Division of Health Sciences, University of Otago, Dunedin, New Zealand
| | - Simone A Joosten
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, the Netherlands
| | - Reinout van Crevel
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Cisca Wijmenga
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Julia A Critchley
- Population Health Research Institute, St George's Hospital Medical School, University of London
| | - Gerhard Walzl
- SA MRC Centre for TB Research, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Bachti Alisjahbana
- TB-HIV Research Center, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia; Hasan Sadikin General Hospital, Bandung, Indonesia
| | - Mariëlle C Haks
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, the Netherlands
| | - Hazel M Dockrell
- Dept of Infection Biology and TB Centre, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, United Kingdom
| | - Tom H M Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, the Netherlands
| | - Eleonora Vianello
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jacqueline M Cliff
- Dept of Infection Biology and TB Centre, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, United Kingdom; Department of Life Sciences, Brunel University London, United Kingdom
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Karimnezhad A. More accurate estimation of cell composition in bulk expression through robust integration of single-cell information. BIOINFORMATICS ADVANCES 2022; 2:vbac049. [PMID: 36699374 PMCID: PMC9710693 DOI: 10.1093/bioadv/vbac049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 06/24/2022] [Accepted: 07/25/2022] [Indexed: 02/01/2023]
Abstract
Motivation The rapid single-cell transcriptomic technology developments have led to an increasing interest in cellular heterogeneity within cell populations. Although cell-type proportions can be obtained directly from single-cell RNA sequencing (scRNA-seq), it is costly and not feasible in every study. Alternatively, with fewer experimental complications, cell-type compositions are characterized from bulk RNA-seq data. Many computational tools have been developed and reported in the literature. However, they fail to appropriately incorporate the covariance structures in both scRNA-seq and bulk RNA-seq datasets in use. Results We present a covariance-based single-cell decomposition (CSCD) method that estimates cell-type proportions in bulk data through building a reference expression profile based on a single-cell data, and learning gene-specific bulk expression transformations using a constrained linear inverse model. The approach is similar to Bisque, a cell-type decomposition method that was recently developed. Bisque is limited to a univariate model, thus unable to incorporate gene-gene correlations into the analysis. We introduce a more advanced model that successfully incorporates the covariance structures in both scRNA-seq and bulk RNA-seq datasets into the analysis, and fixes the collinearity issue by utilizing a linear shrinkage estimation of the corresponding covariance matrices. We applied CSCD to several publicly available datasets and measured the performance of CSCD, Bisque and six other common methods in the literature. Our results indicate that CSCD is more accurate and comprehensive than most of the existing methods. Availability and implementation The R package is available on https://github.com/empiricalbayes/CSCDRNA.
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40
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Long H, Jia Q, Wang L, Fang W, Wang Z, Jiang T, Zhou F, Jin Z, Huang J, Zhou L, Hu C, Wang X, Zhang J, Ba Y, Gong Y, Zeng X, Zeng D, Su X, Alexander PB, Wang L, Wang L, Wan YY, Wang XF, Zhang L, Li QJ, Zhu B. Tumor-induced erythroid precursor-differentiated myeloid cells mediate immunosuppression and curtail anti-PD-1/PD-L1 treatment efficacy. Cancer Cell 2022; 40:674-693.e7. [PMID: 35594863 DOI: 10.1016/j.ccell.2022.04.018] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 02/10/2022] [Accepted: 04/29/2022] [Indexed: 12/25/2022]
Abstract
Despite the unprecedented success of immune checkpoint inhibitors (ICIs) as anti-cancer therapy, it remains a prevailing clinical need to identify additional mechanisms underlying ICI therapeutic efficacy and potential drug resistance. Here, using lineage tracking in cancer patients and tumor-bearing mice, we demonstrate that erythroid progenitor cells lose their developmental potential and switch to the myeloid lineage. Single-cell transcriptome analyses reveal that, notwithstanding quantitative differences in erythroid gene expression, erythroid differentiated myeloid cells (EDMCs) are transcriptionally indistinguishable from their myeloid-originated counterparts. EDMCs possess multifaceted machinery to curtail T cell-mediated anti-tumor responses. Consequently, EDMC content within tumor tissues is negatively associated with T cell inflammation for the majority of solid cancers; moreover, EDMC enrichment, in accordance with anemia manifestation, is predictive of poor prognosis in various cohorts of patients undergoing ICI therapy. Together, our findings reveal a feedforward mechanism by which tumors exploit anemia-triggered erythropoiesis for myeloid transdifferentiation and immunosuppression.
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Affiliation(s)
- Haixia Long
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Qingzhu Jia
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Liuyang Wang
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC, USA
| | - Wenfeng Fang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhongyu Wang
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Tao Jiang
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, China
| | - Fei Zhou
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, China
| | - Zheng Jin
- Research Institute, GloriousMed Clinical Laboratory (Shanghai) Co., Ltd, Shanghai, China
| | - Jiani Huang
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Li Zhou
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Chunyan Hu
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Xinxin Wang
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Jin Zhang
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Yujie Ba
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; School of Life Science, Chongqing University, Chongqing, China
| | - Yujie Gong
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China
| | - Xianghua Zeng
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Dong Zeng
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | - Xingxing Su
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China
| | | | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Limei Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Yisong Y Wan
- Department of Microbiology and Immunology, Lineberger Comprehensive Cancer Centre, University of North Carolina, Chapel Hill, NC, USA
| | - Xiao-Fan Wang
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, USA
| | - Li Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Qi-Jing Li
- Department of Immunology, Duke University Medical Center, Durham, NC, USA.
| | - Bo Zhu
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, China; Chongqing Key Laboratory of Immunotherapy, Chongqing, China.
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Abstract
There are many experimental methods for characterizing immune profiles of tumors, such as flow and mass cytometry. However, these approaches are time and resource intensive. Thus, several "digital cytometry" methods have been developed to extract cell frequencies from RNA-seq data. Here, we introduce TumorDecon, named for its potential to deconvolve the distribution of cells from the gene expression levels of a bulk of cells, such as a tumor. The Python package provides an accessible way of applying these methods. It includes four deconvolution methods as well as several gene sets, signature matrices, and functions for generating custom signature matrices.
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42
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Detection of Cell Separation-Induced Gene Expression Through a Penalized Deconvolution Approach. STATISTICS IN BIOSCIENCES 2022. [DOI: 10.1007/s12561-022-09344-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Suñer C, Sibilio A, Martín J, Castellazzi CL, Reina O, Dotu I, Caballé A, Rivas E, Calderone V, Díez J, Nebreda AR, Méndez R. Macrophage inflammation resolution requires CPEB4-directed offsetting of mRNA degradation. eLife 2022; 11:75873. [PMID: 35442882 PMCID: PMC9094754 DOI: 10.7554/elife.75873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 04/17/2022] [Indexed: 11/17/2022] Open
Abstract
Chronic inflammation is a major cause of disease. Inflammation resolution is in part directed by the differential stability of mRNAs encoding pro-inflammatory and anti-inflammatory factors. In particular, tristetraprolin (TTP)-directed mRNA deadenylation destabilizes AU-rich element (ARE)-containing mRNAs. However, this mechanism alone cannot explain the variety of mRNA expression kinetics that are required to uncouple degradation of pro-inflammatory mRNAs from the sustained expression of anti-inflammatory mRNAs. Here, we show that the RNA-binding protein CPEB4 acts in an opposing manner to TTP in macrophages: it helps to stabilize anti-inflammatory transcripts harboring cytoplasmic polyadenylation elements (CPEs) and AREs in their 3′-UTRs, and it is required for the resolution of the lipopolysaccharide (LPS)-triggered inflammatory response. Coordination of CPEB4 and TTP activities is sequentially regulated through MAPK signaling. Accordingly, CPEB4 depletion in macrophages impairs inflammation resolution in an LPS-induced sepsis model. We propose that the counterbalancing actions of CPEB4 and TTP, as well as the distribution of CPEs and AREs in their target mRNAs, define transcript-specific decay patterns required for inflammation resolution. Thus, these two opposing mechanisms provide a fine-tuning control of inflammatory transcript destabilization while maintaining the expression of the negative feedback loops required for efficient inflammation resolution; disruption of this balance can lead to disease.
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Affiliation(s)
- Clara Suñer
- Institute for Research in Biomedicine (IRB), Barcelona, Spain
| | | | - Judit Martín
- Institute for Research in Biomedicine (IRB), Barcelona, Spain
| | | | - Oscar Reina
- Institute for Research in Biomedicine (IRB), Barcelona, Spain
| | - Ivan Dotu
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Adrià Caballé
- Institute for Research in Biomedicine (IRB), Barcelona, Spain
| | - Elisa Rivas
- Institute for Research in Biomedicine (IRB), Barcelona, Spain
| | | | - Juana Díez
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Angel R Nebreda
- Institute for Research in Biomedicine (IRB), Barcelona, Spain
| | - Raúl Méndez
- Institute for Research in Biomedicine (IRB), Barcelona, Spain
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Anene CA, Taggart E, Harwood CA, Pennington DJ, Wang J. Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods. Front Genet 2022; 13:802838. [PMID: 35432466 PMCID: PMC9011041 DOI: 10.3389/fgene.2022.802838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 03/04/2022] [Indexed: 12/26/2022] Open
Abstract
The assessment of the cellular heterogeneity and abundance in bulk tissue samples is essential for characterising cellular and organismal states. Computational approaches to estimate cellular abundance from bulk RNA-Seq datasets have variable performances, often requiring benchmarking matrices to select the best performing methods for individual studies. However, such benchmarking investigations are difficult to perform and assess in typical applications because of the absence of gold standard/ground-truth cellular measurements. Here we describe Decosus, an R package that integrates seven methods and signatures for deconvoluting cell types from gene expression profiles (GEP). Benchmark analysis on a range of datasets with ground-truth measurements revealed that our integrated estimates consistently exhibited stable performances across datasets than individual methods and signatures. We further applied Decosus to characterise the immune compartment of skin samples in different settings, confirming the well-established Th1 and Th2 polarisation in psoriasis and atopic dermatitis, respectively. Secondly, we revealed immune system-related UV-induced changes in sun-exposed skin. Furthermore, a significant motivation in the design of Decosus is flexibility and the ability for the user to include new gene signatures, algorithms, and integration methods at run time.
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Affiliation(s)
- Chinedu A. Anene
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
- Centre for Cancer Biology and Therapy, School of Applied Science, London South Bank University, London, United Kingdom
- *Correspondence: Chinedu A. Anene,
| | - Emma Taggart
- Centre for Immunobiology, Barts and the London School of Medicine, Blizard Institute, Queen Mary University of London, London, United Kingdom
| | - Catherine A. Harwood
- Centre for Cell Biology and Cutaneous Research, Barts and The London School of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London, United Kingdom
- Department of Dermatology, The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Daniel J. Pennington
- Centre for Immunobiology, Barts and the London School of Medicine, Blizard Institute, Queen Mary University of London, London, United Kingdom
| | - Jun Wang
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
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Comprehensive evaluation of deconvolution methods for human brain gene expression. Nat Commun 2022; 13:1358. [PMID: 35292647 PMCID: PMC8924248 DOI: 10.1038/s41467-022-28655-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/28/2022] [Indexed: 11/08/2022] Open
Abstract
Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing. Transcriptome deconvolution aims to estimate cellular composition based on gene expression data. Here the authors evaluate deconvolution methods for human brain transcriptome and conclude that partial deconvolution algorithms work best, but that appropriate cell-type signatures are also important.
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Bunis DG, Wang W, Vallvé-Juanico J, Houshdaran S, Sen S, Ben Soltane I, Kosti I, Vo KC, Irwin JC, Giudice LC, Sirota M. Whole-Tissue Deconvolution and scRNAseq Analysis Identify Altered Endometrial Cellular Compositions and Functionality Associated With Endometriosis. Front Immunol 2022; 12:788315. [PMID: 35069565 PMCID: PMC8766492 DOI: 10.3389/fimmu.2021.788315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/09/2021] [Indexed: 12/13/2022] Open
Abstract
The uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single-cell RNAseq, we comprehensively characterized immune and nonimmune cell types in the endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single-cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue.
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Affiliation(s)
- Daniel G Bunis
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Wanxin Wang
- Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Júlia Vallvé-Juanico
- Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Sahar Houshdaran
- Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Sushmita Sen
- Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Isam Ben Soltane
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Idit Kosti
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Kim Chi Vo
- Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Juan C Irwin
- Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Linda C Giudice
- Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.,Department of Pediatrics, Division of Neonatology, University of California, San Francisco, San Francisco, CA, United States
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47
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Boldina G, Fogel P, Rocher C, Bettembourg C, Luta G, Augé F. A2Sign: Agnostic Algorithms for Signatures-a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution. Bioinformatics 2022; 38:1015-1021. [PMID: 34788798 DOI: 10.1093/bioinformatics/btab773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 09/17/2021] [Accepted: 11/09/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Molecular signatures are critical for inferring the proportions of cell types from bulk transcriptomics data. However, the identification of these signatures is based on a methodology that relies on prior biological knowledge of the cell types being studied. When working with less known biological material, a data-driven approach is required to uncover the underlying classes and generate ad hoc signatures from healthy or pathogenic tissue. RESULTS We present a new approach, A2Sign: Agnostic Algorithms for Signatures, based on a non-negative tensor factorization (NTF) strategy that allows us to identify cell-type-specific molecular signatures, greatly reduce collinearities and also account for inter-individual variability. We propose a global framework that can be applied to uncover molecular signatures for cell-type deconvolution in arbitrary tissues using bulk transcriptome data. We also present two new molecular signatures for deconvolution of up to 16 immune cell types using microarray or RNA-seq data. AVAILABILITY AND IMPLEMENTATION All steps of our analysis were implemented in annotated Python notebooks (https://github.com/paulfogel/A2SIGN). To perform NTF, we used the NMTF package, which can be downloaded using Python pip install. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Galina Boldina
- Sanofi, R&D Translational Sciences France, Bioinformatics, Sanofi, F-91385 Chilly-Mazarin Cedex, France
| | - Paul Fogel
- Consultant, F-75006 Paris, France.,Advestis, F-75008 Paris, France.,Quinten, F-75017 Paris, France
| | - Corinne Rocher
- Sanofi, R&D Translational Sciences France, Bioinformatics, Sanofi, F-91385 Chilly-Mazarin Cedex, France
| | - Charles Bettembourg
- Sanofi, R&D Translational Sciences France, Bioinformatics, Sanofi, F-91385 Chilly-Mazarin Cedex, France
| | - George Luta
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC 20057, USA
| | - Franck Augé
- Sanofi, R&D Translational Sciences France, Bioinformatics, Sanofi, F-91385 Chilly-Mazarin Cedex, France
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48
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Ahmed MM, Zaki A, Alhazmi A, Alsharif KF, Bagabir HA, Haque S, Manda K, Ahmad S, Ali SM, Ishrat R. Identification and Validation of Pathogenic Genes in Sepsis and Associated Diseases by Integrated Bioinformatics Approach. Genes (Basel) 2022; 13:genes13020209. [PMID: 35205254 PMCID: PMC8872348 DOI: 10.3390/genes13020209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 12/14/2022] Open
Abstract
Sepsis is a clinical syndrome with high mortality and morbidity rates. In sepsis, the abrupt release of cytokines by the innate immune system may cause multiorgan failure, leading to septic shock and associated complications. In the presence of a number of systemic disorders, such as sepsis, infections, diabetes, and systemic lupus erythematosus (SLE), cardiorenal syndrome (CRS) type 5 is defined by concomitant cardiac and renal dysfunctions Thus, our study suggests that certain mRNAs and unexplored pathways may pave a way to unravel critical therapeutic targets in three debilitating and interrelated illnesses, namely, sepsis, SLE, and CRS. Sepsis, SLE, and CRS are closely interrelated complex diseases likely sharing an overlapping pathogenesis caused by erroneous gene network activities. We sought to identify the shared gene networks and the key genes for sepsis, SLE, and CRS by completing an integrative analysis. Initially, 868 DEGs were identified in 16 GSE datasets. Based on degree centrality, 27 hub genes were revealed. The gProfiler webtool was used to perform functional annotations and enriched molecular pathway analyses. Finally, core hub genes (EGR1, MMP9, and CD44) were validated using RT-PCR analysis. Our comprehensive multiplex network approach to hub gene discovery is effective, as evidenced by the findings. This work provides a novel research path for a new research direction in multi-omics biological data analysis.
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Affiliation(s)
- Mohd Murshad Ahmed
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India;
| | - Almaz Zaki
- Translational Research Lab, Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India; (A.Z.); (S.A.)
| | - Alaa Alhazmi
- Medical Laboratory Technology Department, SMIRES for Consultation in Specialized, Jazan University, Jazan 45142, Saudi Arabia;
| | - Khalaf F. Alsharif
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia;
| | - Hala Abubaker Bagabir
- Department of Medical Physiology, Faculty of Medicine, King Abdulaziz University, Rabigh 21589, Saudi Arabia;
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan 45142, Saudi Arabia;
| | - Kailash Manda
- Institute of Nuclear Medicine and Applied Sciences, Defense Research Development Organization, New Delhi 110054, India;
| | - Shaniya Ahmad
- Translational Research Lab, Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India; (A.Z.); (S.A.)
| | - Syed Mansoor Ali
- Translational Research Lab, Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India; (A.Z.); (S.A.)
- Correspondence: (S.M.A.); (R.I.)
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India;
- Correspondence: (S.M.A.); (R.I.)
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49
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Mädler SC, Julien-Laferriere A, Wyss L, Phan M, Sonrel A, Kang ASW, Ulrich E, Schmucki R, Zhang JD, Ebeling M, Badi L, Kam-Thong T, Schwalie PC, Hatje K. Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research. NAR Genom Bioinform 2021; 3:lqab102. [PMID: 34761219 PMCID: PMC8573822 DOI: 10.1093/nargab/lqab102] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 02/07/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.
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Affiliation(s)
- Sophia Clara Mädler
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Alice Julien-Laferriere
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Luis Wyss
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Miroslav Phan
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Anthony Sonrel
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Albert S W Kang
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Eric Ulrich
- Roche Pharma Research and Early Development, I2O Disease Translational Area, Roche Innovation Center Basel, Basel, Switzerland
| | - Roland Schmucki
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Jitao David Zhang
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Martin Ebeling
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Laura Badi
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Tony Kam-Thong
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Petra C Schwalie
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Klas Hatje
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
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50
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Fischer S, Gillis J. How many markers are needed to robustly determine a cell's type? iScience 2021; 24:103292. [PMID: 34765918 PMCID: PMC8571500 DOI: 10.1016/j.isci.2021.103292] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 09/16/2021] [Accepted: 10/13/2021] [Indexed: 12/30/2022] Open
Abstract
Our understanding of cell types has advanced considerably with the publication of single-cell atlases. Marker genes play an essential role for experimental validation and computational analyses such as physiological characterization, annotation, and deconvolution. However, a framework for quantifying marker replicability and selecting replicable markers is currently lacking. Here, using high-quality data from the Brain Initiative Cell Census Network (BICCN), we systematically investigate marker replicability for 85 neuronal cell types. We show that, due to dataset-specific noise, we need to combine 5 datasets to obtain robust differentially expressed (DE) genes, particularly for rare populations and lowly expressed genes. We estimate that 10 to 200 meta-analytic markers provide optimal downstream performance and make available replicable marker lists for the 85 BICCN cell types. Replicable marker lists condense interpretable and generalizable information about cell types, opening avenues for downstream applications, including cell type annotation, selection of gene panels, and bulk data deconvolution.
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Affiliation(s)
- Stephan Fischer
- Cold Spring Harbor Laboratory, Stanley Institute for Cognitive Genomics, Cold Spring Harbor, NY 11724, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Stanley Institute for Cognitive Genomics, Cold Spring Harbor, NY 11724, USA
- Cold Spring Harbor Laboratory, Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA
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