1
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Hu M, Chikina M. Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods. Genome Biol 2024; 25:169. [PMID: 38956606 PMCID: PMC11218230 DOI: 10.1186/s13059-024-03292-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/29/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.
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Affiliation(s)
- Mengying Hu
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA
- Joint Carnegie Mellon - University of Pittsburgh Computational Biology PhD Program, University of Pittsburgh, Pittsburgh, USA
| | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA.
- Joint Carnegie Mellon - University of Pittsburgh Computational Biology PhD Program, University of Pittsburgh, Pittsburgh, USA.
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2
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Tiong KL, Luzhbin D, Yeang CH. Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data. BMC Bioinformatics 2024; 25:209. [PMID: 38867193 PMCID: PMC11167951 DOI: 10.1186/s12859-024-05825-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/03/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Single-cell RNA sequencing (sc-RNASeq) data illuminate transcriptomic heterogeneity but also possess a high level of noise, abundant missing entries and sometimes inadequate or no cell type annotations at all. Bulk-level gene expression data lack direct information of cell population composition but are more robust and complete and often better annotated. We propose a modeling framework to integrate bulk-level and single-cell RNASeq data to address the deficiencies and leverage the mutual strengths of each type of data and enable a more comprehensive inference of their transcriptomic heterogeneity. Contrary to the standard approaches of factorizing the bulk-level data with one algorithm and (for some methods) treating single-cell RNASeq data as references to decompose bulk-level data, we employed multiple deconvolution algorithms to factorize the bulk-level data, constructed the probabilistic graphical models of cell-level gene expressions from the decomposition outcomes, and compared the log-likelihood scores of these models in single-cell data. We term this framework backward deconvolution as inference operates from coarse-grained bulk-level data to fine-grained single-cell data. As the abundant missing entries in sc-RNASeq data have a significant effect on log-likelihood scores, we also developed a criterion for inclusion or exclusion of zero entries in log-likelihood score computation. RESULTS We selected nine deconvolution algorithms and validated backward deconvolution in five datasets. In the in-silico mixtures of mouse sc-RNASeq data, the log-likelihood scores of the deconvolution algorithms were strongly anticorrelated with their errors of mixture coefficients and cell type specific gene expression signatures. In the true bulk-level mouse data, the sample mixture coefficients were unknown but the log-likelihood scores were strongly correlated with accuracy rates of inferred cell types. In the data of autism spectrum disorder (ASD) and normal controls, we found that ASD brains possessed higher fractions of astrocytes and lower fractions of NRGN-expressing neurons than normal controls. In datasets of breast cancer and low-grade gliomas (LGG), we compared the log-likelihood scores of three simple hypotheses about the gene expression patterns of the cell types underlying the tumor subtypes. The model that tumors of each subtype were dominated by one cell type persistently outperformed an alternative model that each cell type had elevated expression in one gene group and tumors were mixtures of those cell types. Superiority of the former model is also supported by comparing the real breast cancer sc-RNASeq clusters with those generated by simulated sc-RNASeq data. CONCLUSIONS The results indicate that backward deconvolution serves as a sensible model selection tool for deconvolution algorithms and facilitates discerning hypotheses about cell type compositions underlying heterogeneous specimens such as tumors.
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Affiliation(s)
- Khong-Loon Tiong
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Dmytro Luzhbin
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
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3
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Hoedjes KM, Grath S, Posnien N, Ritchie MG, Schlötterer C, Abbott JK, Almudi I, Coronado-Zamora M, Durmaz Mitchell E, Flatt T, Fricke C, Glaser-Schmitt A, González J, Holman L, Kankare M, Lenhart B, Orengo DJ, Snook RR, Yılmaz VM, Yusuf L. From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology. Mol Ecol 2024:e17382. [PMID: 38856653 DOI: 10.1111/mec.17382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/09/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
RNA sequencing (RNAseq) methodology has experienced a burst of technological developments in the last decade, which has opened up opportunities for studying the mechanisms of adaptation to environmental factors at both the organismal and cellular level. Selecting the most suitable experimental approach for specific research questions and model systems can, however, be a challenge and researchers in ecology and evolution are commonly faced with the choice of whether to study gene expression variation in whole bodies, specific tissues, and/or single cells. A wide range of sometimes polarised opinions exists over which approach is best. Here, we highlight the advantages and disadvantages of each of these approaches to provide a guide to help researchers make informed decisions and maximise the power of their study. Using illustrative examples of various ecological and evolutionary research questions, we guide the readers through the different RNAseq approaches and help them identify the most suitable design for their own projects.
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Affiliation(s)
- Katja M Hoedjes
- Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sonja Grath
- Division of Evolutionary Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Nico Posnien
- Department of Developmental Biology, Göttingen Center for Molecular Biosciences (GZMB), University of Göttingen, Göttingen, Germany
| | - Michael G Ritchie
- Centre for Biological Diversity, University of St Andrews, St Andrews, UK
| | | | | | - Isabel Almudi
- Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | | | - Esra Durmaz Mitchell
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Functional Genomics and Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Thomas Flatt
- Department of Biology, University of Fribourg, Fribourg, Switzerland
| | - Claudia Fricke
- Institute for Zoology/Animal Ecology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | | | - Josefa González
- Institute of Evolutionary Biology, CSIC, UPF, Barcelona, Spain
| | - Luke Holman
- School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK
| | - Maaria Kankare
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
| | - Benedict Lenhart
- Department of Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Dorcas J Orengo
- Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | - Rhonda R Snook
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Vera M Yılmaz
- Division of Evolutionary Biology, LMU Munich, Planegg-Martinsried, Germany
| | - Leeban Yusuf
- Centre for Biological Diversity, University of St Andrews, St Andrews, UK
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4
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Huang J, Du Y, Stucky A, Kelly KR, Zhong JF, Sun F. DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data. PATTERNS (NEW YORK, N.Y.) 2024; 5:100969. [PMID: 38800361 PMCID: PMC11117059 DOI: 10.1016/j.patter.2024.100969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/15/2024] [Accepted: 03/21/2024] [Indexed: 05/29/2024]
Abstract
Understanding the cellular composition of a disease-related tissue is important in disease diagnosis, prognosis, and downstream treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technique have allowed the measurement of gene expression profiles for individual cells. However, scRNA-seq is still too expensive to be used for large-scale population studies, and bulk RNA-seq is still widely used in such situations. An essential challenge is to deconvolve cellular composition for bulk RNA-seq data based on scRNA-seq data. Here, we present DeepDecon, a deep neural network model that leverages single-cell gene expression information to accurately predict the fraction of cancer cells in bulk tissues. It provides a refining strategy in which the cancer cell fraction is iteratively estimated by a set of trained models. When applied to simulated and real cancer data, DeepDecon exhibits superior performance compared to existing decomposition methods in terms of accuracy.
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Affiliation(s)
- Jiawei Huang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Yuxuan Du
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Andres Stucky
- Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA 92350, USA
| | - Kevin R. Kelly
- Division of Hematology, University of Southern California, Los Angeles, CA 90089, USA
| | - Jiang F. Zhong
- Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA 92350, USA
| | - Fengzhu Sun
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
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5
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O'Connell GC. Dataset including whole blood gene expression profiles and matched leukocyte counts with utility for benchmarking cellular deconvolution pipelines. BMC Genom Data 2024; 25:45. [PMID: 38714942 PMCID: PMC11077736 DOI: 10.1186/s12863-024-01223-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
OBJECTIVES Cellular deconvolution is a valuable computational process that can infer the cellular composition of heterogeneous tissue samples from bulk RNA-sequencing data. Benchmark testing is a crucial step in the development and evaluation of new cellular deconvolution algorithms, and also plays a key role in the process of building and optimizing deconvolution pipelines for specific experimental applications. However, few in vivo benchmarking datasets exist, particularly for whole blood, which is the single most profiled human tissue. Here, we describe a unique dataset containing whole blood gene expression profiles and matched circulating leukocyte counts from a large cohort of human donors with utility for benchmarking cellular deconvolution pipelines. DATA DESCRIPTION To produce this dataset, venous whole blood was sampled from 138 total donors recruited at an academic medical center. Genome-wide expression profiling was subsequently performed via next-generation RNA sequencing, and white blood cell differentials were collected in parallel using flow cytometry. The resultant final dataset contains donor-level expression data for over 45,000 protein coding and non-protein coding genes, as well as matched neutrophil, lymphocyte, monocyte, and eosinophil counts.
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Affiliation(s)
- Grant C O'Connell
- Molecular Biomarker Core, Case Western Reserve University, Cleveland, OH, USA.
- School of Nursing, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4904, USA.
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6
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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. Genome Med 2024; 16:65. [PMID: 38685057 PMCID: PMC11057104 DOI: 10.1186/s13073-024-01338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA.
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
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7
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Hsu YC, Chiu YC, Lu TP, Hsiao TH, Chen Y. Predicting drug response through tumor deconvolution by cancer cell lines. PATTERNS (NEW YORK, N.Y.) 2024; 5:100949. [PMID: 38645769 PMCID: PMC11026976 DOI: 10.1016/j.patter.2024.100949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 04/23/2024]
Abstract
Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing.
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Affiliation(s)
- Yu-Ching Hsu
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei 115, Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei 100, Taiwan
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yu-Chiao Chiu
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, USA
| | - Tzu-Pin Lu
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei 100, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Yidong Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
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8
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Ferro dos Santos MR, Giuili E, De Koker A, Everaert C, De Preter K. Computational deconvolution of DNA methylation data from mixed DNA samples. Brief Bioinform 2024; 25:bbae234. [PMID: 38762790 PMCID: PMC11102637 DOI: 10.1093/bib/bbae234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 05/20/2024] Open
Abstract
In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.
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Affiliation(s)
- Maísa R Ferro dos Santos
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Edoardo Giuili
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Andries De Koker
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Celine Everaert
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Katleen De Preter
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
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9
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Wu CT, Du D, Chen L, Dai R, Liu C, Yu G, Bhardwaj S, Parker SJ, Zhang Z, Clarke R, Herrington DM, Wang Y. CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution. Bioinformatics 2024; 40:btae107. [PMID: 38407991 PMCID: PMC10924278 DOI: 10.1093/bioinformatics/btae107] [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: 04/03/2023] [Revised: 01/13/2024] [Accepted: 02/25/2024] [Indexed: 02/28/2024] Open
Abstract
MOTIVATION Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.
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Affiliation(s)
- Chiung-Ting Wu
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
| | - Dongping Du
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
| | - Lulu Chen
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, United States
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, United States
| | - Guoqiang Yu
- Department of Automation, Tsinghua University, Beijing 100084, P. R. China
| | - Saurabh Bhardwaj
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering & Technology, Punjab 147004, India
| | - Sarah J Parker
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Zhen Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, United States
| | - Robert Clarke
- The Hormel Institute, University of Minnesota, Austin, MN 55912, United States
| | - David M Herrington
- Department of Internal Medicine, Wake Forest University, Winston-Salem, NC 27157, United States
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
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10
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Zhang H, Lu X, Lu B, Gullo G, Chen L. Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future. Future Oncol 2024; 20:1207-1220. [PMID: 38362731 DOI: 10.2217/fon-2023-0658] [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: 07/31/2023] [Accepted: 01/24/2024] [Indexed: 02/17/2024] Open
Abstract
Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.
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Affiliation(s)
- Han Zhang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Binfeng Lu
- Center for Discovery & Innovation, Hackensack Meridian Health, Nutley, NJ 07110, USA
| | - Giuseppe Gullo
- Department of Obstetrics & Gynecology, Villa Sofia Cervello Hospital, University of Palermo, 90146, Palermo, Italy
| | - Lujia Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
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11
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Mañanes D, Rivero-García I, Relaño C, Torres M, Sancho D, Jimenez-Carretero D, Torroja C, Sánchez-Cabo F. SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks. Bioinformatics 2024; 40:btae072. [PMID: 38366652 PMCID: PMC10881086 DOI: 10.1093/bioinformatics/btae072] [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: 07/25/2023] [Revised: 01/10/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
SUMMARY Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools. AVAILABILITY AND IMPLEMENTATION The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS.
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Affiliation(s)
- Diego Mañanes
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Inés Rivero-García
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
- Departamento de Ingeniería Biomédica, ETSI de Telecomunicaciones, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Carlos Relaño
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Miguel Torres
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - David Sancho
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | | | - Carlos Torroja
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Fátima Sánchez-Cabo
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
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12
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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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13
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Wang L, Hu Y, Gao L. Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics. Brief Bioinform 2024; 25:bbae063. [PMID: 38426323 PMCID: PMC10939420 DOI: 10.1093/bib/bbae063] [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: 11/02/2023] [Revised: 01/10/2024] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.
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Affiliation(s)
- Lanying Wang
- School of Computer Science and Technology, Xidian University, Xi’an 710100, China
| | - Yuxuan Hu
- School of Computer Science and Technology, Xidian University, Xi’an 710100, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi’an 710100, China
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14
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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15
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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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16
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Brotman SM, Oravilahti A, Rosen JD, Alvarez M, Heinonen S, van der Kolk BW, Fernandes Silva L, Perrin HJ, Vadlamudi S, Pylant C, Deochand S, Basta PV, Valone JM, Narain MN, Stringham HM, Boehnke M, Kuusisto J, Love MI, Pietiläinen KH, Pajukanta P, Laakso M, Mohlke KL. Cell-Type Composition Affects Adipose Gene Expression Associations With Cardiometabolic Traits. Diabetes 2023; 72:1707-1718. [PMID: 37647564 PMCID: PMC10588284 DOI: 10.2337/db23-0365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023]
Abstract
Understanding differences in adipose gene expression between individuals with different levels of clinical traits may reveal the genes and mechanisms leading to cardiometabolic diseases. However, adipose is a heterogeneous tissue. To account for cell-type heterogeneity, we estimated cell-type proportions in 859 subcutaneous adipose tissue samples with bulk RNA sequencing (RNA-seq) using a reference single-nuclear RNA-seq data set. Cell-type proportions were associated with cardiometabolic traits; for example, higher macrophage and adipocyte proportions were associated with higher and lower BMI, respectively. We evaluated cell-type proportions and BMI as covariates in tests of association between >25,000 gene expression levels and 22 cardiometabolic traits. For >95% of genes, the optimal, or best-fit, models included BMI as a covariate, and for 79% of associations, the optimal models also included cell type. After adjusting for the optimal covariates, we identified 2,664 significant associations (P ≤ 2e-6) for 1,252 genes and 14 traits. Among genes proposed to affect cardiometabolic traits based on colocalized genome-wide association study and adipose expression quantitative trait locus signals, 25 showed a corresponding association between trait and gene expression levels. Overall, these results suggest the importance of modeling cell-type proportion when identifying gene expression associations with cardiometabolic traits. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Sarah M. Brotman
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
| | - Anniina Oravilahti
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jonathan D. Rosen
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Birgitta W. van der Kolk
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Hannah J. Perrin
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
| | | | - Cortney Pylant
- Department of Epidemiology, The University of North Carolina, Chapel Hill, NC
| | - Sonia Deochand
- Department of Epidemiology, The University of North Carolina, Chapel Hill, NC
| | - Patricia V. Basta
- Department of Epidemiology, The University of North Carolina, Chapel Hill, NC
| | - Jordan M. Valone
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
- UNC Neuroscience Center, The University of North Carolina, Chapel Hill, NC
| | - Morgan N. Narain
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
- Curriculum of Toxicology and Environmental Medicine, The University of North Carolina, Chapel Hill, NC
| | - Heather M. Stringham
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Michael I. Love
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
- Department of Biostatistics, The University of North Carolina, Chapel Hill, NC
| | - Kirsi H. Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HealthyWeightHub, Endocrinology, Abdominal Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA
- Institute for Precision Health, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Karen L. Mohlke
- Department of Genetics, The University of North Carolina, Chapel Hill, NC
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17
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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.27.559579. [PMID: 37808714 PMCID: PMC10557724 DOI: 10.1101/2023.09.27.559579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R. Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, 38105, TN, USA
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
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18
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Yi H, Lin Y, Chang Q, Jin W. A fast and globally optimal solution for RNA-seq quantification. Brief Bioinform 2023; 24:bbad298. [PMID: 37595963 DOI: 10.1093/bib/bbad298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 08/20/2023] Open
Abstract
Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation-Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation ATAx = ATb. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at https://github.com/yhg926/TQSLE.
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Affiliation(s)
- Huiguang Yi
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Rd, Shenzhen, 518000, Guangdong, China
- School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, China
| | - Yanling Lin
- School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, China
| | - Qing Chang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Rd, Shenzhen, 518000, Guangdong, China
| | - Wenfei Jin
- School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, China
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19
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O'Neill NK, Stein TD, Hu J, Rehman H, Campbell JD, Yajima M, Zhang X, Farrer LA. Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM. BMC Bioinformatics 2023; 24:349. [PMID: 37726653 PMCID: PMC10507917 DOI: 10.1186/s12859-023-05476-w] [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: 12/06/2022] [Accepted: 09/12/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies. RESULTS We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data. CONCLUSION DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.
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Affiliation(s)
- Nicholas K O'Neill
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Thor D Stein
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Veterans Administration Medical Center, Bedford, MA, USA
| | - Junming Hu
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Habbiburr Rehman
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Joshua D Campbell
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Medicine (Computational Biomedicine), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Masanao Yajima
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Xiaoling Zhang
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
| | - Lindsay A Farrer
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Medicine (Biomedical Genetics), Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
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20
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Wang J, Lu L, Zheng S, Wang D, Jin L, Zhang Q, Li M, Zhang Z. DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301058. [PMID: 37515382 PMCID: PMC10520690 DOI: 10.1002/advs.202301058] [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: 02/15/2023] [Revised: 07/06/2023] [Indexed: 07/30/2023]
Abstract
Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome architecture. By applying deCOOC to in-house-generated bulk Hi-C data from visceral and subcutaneous adipose tissues, it is found that the characteristic chromatin features of M2 cells in the two anatomical loci are distinctively bound to different physiological functionalities. Taken together, deCOOC is both a reliable Hi-C data deconvolution method and a powerful tool for functional extraction of 3D genome architecture.
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Affiliation(s)
- Junmei Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Lu Lu
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Shiqi Zheng
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Danyang Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
- Sars‐Fang Centre & MOE Key Laboratory of Marine Genetics and BreedingCollege of Marine Life SciencesOcean University of ChinaQingdao266100China
| | - Long Jin
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Qing Zhang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
| | - Mingzhou Li
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Zhihua Zhang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
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21
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Sinning J, Funk ND, Soerensen-Zender I, Wulfmeyer VC, Liao CM, Haller H, Hinze C, Schmidt-Ott KM, Melk A, Schmitt R. The aging kidney is characterized by tubuloinflammaging, a phenotype associated with MHC-II gene expression. Front Immunol 2023; 14:1222339. [PMID: 37675124 PMCID: PMC10477980 DOI: 10.3389/fimmu.2023.1222339] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 08/01/2023] [Indexed: 09/08/2023] Open
Abstract
Introduction Even during physiologic aging, the kidney experiences a loss of mass and a progressive functional decline. This is clinically relevant as it leads to an increased risk of acute and chronic kidney disease. The kidney tubular system plays an important role in the underlying aging process, but the involved cellular mechanisms remain largely elusive. Methods Kidneys of 3-, 12- and 24-month-old male C57BL/6J mice were used for RNA sequencing, histological examination, immunostaining and RNA-in-situ-hybridization. Single cell RNA sequencing data of differentially aged murine and human kidneys was analyzed to identify age-dependent expression patterns in tubular epithelial cells. Senescent and non-senescent primary tubular epithelial cells from mouse kidney were used for in vitro experiments. Results During normal kidney aging, tubular cells adopt an inflammatory phenotype, characterized by the expression of MHC class II related genes. In our analysis of bulk and single cell transcriptional data we found that subsets of tubular cells show an age-related expression of Cd74, H2-Eb1 and H2-Ab1 in mice and CD74, HLA-DQB1 and HLADRB1 in humans. Expression of MHC class II related genes was associated with a phenotype of tubular cell senescence, and the selective elimination of senescent cells reversed the phenotype. Exposure to the Cd74 ligand MIF promoted a prosenescent phenotype in tubular cell cultures. Discussion Together, these data suggest that during normal renal aging tubular cells activate a program of 'tubuloinflammaging', which might contribute to age-related phenotypical changes and to increased disease susceptibility.
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Affiliation(s)
- Julius Sinning
- Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany
| | - Nils David Funk
- Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany
| | - Inga Soerensen-Zender
- Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany
| | | | - Chieh Ming Liao
- Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany
| | - Hermann Haller
- Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany
| | - Christian Hinze
- Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany
| | | | - Anette Melk
- Department of Pediatric Kidney, Liver and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | - Roland Schmitt
- Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany
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22
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Berson E, Sreenivas A, Phongpreecha T, Perna A, Grandi FC, Xue L, Ravindra NG, Payrovnaziri N, Mataraso S, Kim Y, Espinosa C, Chang AL, Becker M, Montine KS, Fox EJ, Chang HY, Corces MR, Aghaeepour N, Montine TJ. Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature. Nat Commun 2023; 14:4947. [PMID: 37587197 PMCID: PMC10432546 DOI: 10.1038/s41467-023-40611-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.
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Affiliation(s)
- Eloise Berson
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| | - Anjali Sreenivas
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Amalia Perna
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Fiorella C Grandi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Lei Xue
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Neal G Ravindra
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Neelufar Payrovnaziri
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Samson Mataraso
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Yeasul Kim
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Alan L Chang
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Edward J Fox
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
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23
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Ghaffari S, Bouchonville KJ, Saleh E, Schmidt RE, Offer SM, Sinha S. BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures. Genome Biol 2023; 24:178. [PMID: 37537644 PMCID: PMC10399072 DOI: 10.1186/s13059-023-03007-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.
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Affiliation(s)
- Saba Ghaffari
- Department of Computer Science, University of Illinois at Urbana-Champaign, Thomas M. Siebel Center, 201 N. Goodwin Ave., Urbana, IL, USA
| | - Kelly J Bouchonville
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Gonda 19-476, 200 First St. SW, Rochester, MN, 55905, USA
| | - Ehsan Saleh
- Department of Computer Science, University of Illinois at Urbana-Champaign, Thomas M. Siebel Center, 201 N. Goodwin Ave., Urbana, IL, USA
| | - Remington E Schmidt
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Gonda 19-476, 200 First St. SW, Rochester, MN, 55905, USA
| | - Steven M Offer
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Gonda 19-476, 200 First St. SW, Rochester, MN, 55905, USA.
| | - Saurabh Sinha
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Georgia Institute of Technology, 3108 U.A. Whitaker Bldg., 313 Ferst Drive, Atlanta, GA, 30332, USA.
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24
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Hedman ÅK, Winter E, Yoosuf N, Benita Y, Berg L, Brynedal B, Folkersen L, Klareskog L, Maciejewski M, Sirota-Madi A, Spector Y, Ziemek D, Padyukov L, Shen-Orr SS, Jelinsky SA. Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs. Sci Rep 2023; 13:10058. [PMID: 37344505 DOI: 10.1038/s41598-023-36999-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/14/2023] [Indexed: 06/23/2023] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation and is mediated by multiple immune cell types. In this work, we aimed to determine the relevance of changes in cell proportions in peripheral blood mononuclear cells (PBMCs) during the development of disease and following treatment. Samples from healthy blood donors, newly diagnosed RA patients, and established RA patients that had an inadequate response to MTX and were about to start tumor necrosis factor inhibitors (TNFi) treatment were collected before and after 3 months of treatment. We used in parallel a computational deconvolution approach based on RNA expression and flow cytometry to determine the relative cell-type frequencies. Cell-type frequencies from deconvolution of gene expression indicate that monocytes (both classical and non-classical) and CD4+ cells (Th1 and Th2) were increased in RA patients compared to controls, while NK cells and B cells (naïve and mature) were significantly decreased in RA patients. Treatment with MTX caused a decrease in B cells (memory and plasma cell), and a decrease in CD4 Th cells (Th1 and Th17), while treatment with TNFi resulted in a significant increase in the population of B cells. Characterization of the RNA expression patterns found that most of the differentially expressed genes in RA subjects after treatment can be explained by changes in cell frequencies (98% and 74% respectively for MTX and TNFi).
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Affiliation(s)
- Åsa K Hedman
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Department of Inflammation and Immunology, Pfizer, 1 Portland Street, Cambridge, MA, 02139, USA
| | | | - Niyaz Yoosuf
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | | | - Louise Berg
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Boel Brynedal
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Lasse Folkersen
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mateusz Maciejewski
- Department of Inflammation and Immunology, Pfizer, 1 Portland Street, Cambridge, MA, 02139, USA
| | | | | | - Daniel Ziemek
- Department of Inflammation and Immunology, Pfizer, Berlin, Germany
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Shai S Shen-Orr
- CytoReason, Tel-Aviv, Israel
- Technion-Israel Institute of Technology, Haifa, Israel
| | - Scott A Jelinsky
- Department of Inflammation and Immunology, Pfizer, 1 Portland Street, Cambridge, MA, 02139, USA.
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25
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Wu H, Eckhardt CM, Baccarelli AA. Molecular mechanisms of environmental exposures and human disease. Nat Rev Genet 2023; 24:332-344. [PMID: 36717624 PMCID: PMC10562207 DOI: 10.1038/s41576-022-00569-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2022] [Indexed: 02/01/2023]
Abstract
A substantial proportion of disease risk for common complex disorders is attributable to environmental exposures and pollutants. An appreciation of how environmental pollutants act on our cells to produce deleterious health effects has led to advances in our understanding of the molecular mechanisms underlying the pathogenesis of chronic diseases, including cancer and cardiovascular, neurodegenerative and respiratory diseases. Here, we discuss emerging research on the interplay of environmental pollutants with the human genome and epigenome. We review evidence showing the environmental impact on gene expression through epigenetic modifications, including DNA methylation, histone modification and non-coding RNAs. We also highlight recent studies that evaluate recently discovered molecular processes through which the environment can exert its effects, including extracellular vesicles, the epitranscriptome and the mitochondrial genome. Finally, we discuss current challenges when studying the exposome - the cumulative measure of environmental influences over the lifespan - and its integration into future environmental health research.
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Affiliation(s)
- Haotian Wu
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Christina M Eckhardt
- Department of Pulmonary, Allergy and Critical Care Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
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26
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Hicks EM, Seah C, Cote A, Marchese S, Brennand KJ, Nestler EJ, Girgenti MJ, Huckins LM. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Transl Psychiatry 2023; 13:129. [PMID: 37076454 PMCID: PMC10115809 DOI: 10.1038/s41398-023-02412-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023] Open
Abstract
Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.
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Affiliation(s)
- Emily M Hicks
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Alanna Cote
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Shelby Marchese
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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27
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Revkov E, Kulshrestha T, Sung KWK, Skanderup AJ. PUREE: accurate pan-cancer tumor purity estimation from gene expression data. Commun Biol 2023; 6:394. [PMID: 37041233 PMCID: PMC10090153 DOI: 10.1038/s42003-023-04764-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 03/27/2023] [Indexed: 04/13/2023] Open
Abstract
Tumors are complex masses composed of malignant and non-malignant cells. Variation in tumor purity (proportion of cancer cells in a sample) can both confound integrative analysis and enable studies of tumor heterogeneity. Here we developed PUREE, which uses a weakly supervised learning approach to infer tumor purity from a tumor gene expression profile. PUREE was trained on gene expression data and genomic consensus purity estimates from 7864 solid tumor samples. PUREE predicted purity with high accuracy across distinct solid tumor types and generalized to tumor samples from unseen tumor types and cohorts. Gene features of PUREE were further validated using single-cell RNA-seq data from distinct tumor types. In a comprehensive benchmark, PUREE outperformed existing transcriptome-based purity estimation approaches. Overall, PUREE is a highly accurate and versatile method for estimating tumor purity and interrogating tumor heterogeneity from bulk tumor gene expression data, which can complement genomics-based approaches or be used in settings where genomic data is unavailable.
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Affiliation(s)
- Egor Revkov
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Republic of Singapore
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Republic of Singapore
| | - Tanmay Kulshrestha
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Republic of Singapore
| | - Ken Wing-Kin Sung
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Republic of Singapore
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Republic of Singapore
| | - Anders Jacobsen Skanderup
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Republic of Singapore.
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Republic of Singapore.
- National Cancer Centre Singapore, Division of Medical Oncology, 30 Hospital Boulevard, Singapore, 168583, Republic of Singapore.
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28
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Wang S, Pan W, Mi WX, Wang SH. Sex-specific gene expression patterns in head and neck squamous cell carcinomas. Heliyon 2023; 9:e14890. [PMID: 37064442 PMCID: PMC10102211 DOI: 10.1016/j.heliyon.2023.e14890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/15/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
Objective The head and neck squamous cell carcinomas (HNSCCs) have higher incidence rates in men, but the reasons are still obscure. This study aimed to investigate the sex-specific gene expression patterns and predict the regulatory mechanisms. Design Data including clinical, survival, RNA-seq, miRNA, and methylation information were derived from The Cancer Genome Atlas (TCGA). A total of 131 paired male and female cases were included based on propensity score matching. We concentrated on the prognostic values of the sex-specific pathways enriched by differentially expressed genes (DEGs) and predicted the potential regulatory mechanisms from immune cell infiltration, ceRNA regulatory network, methylation, and differential coexpression analysis. Results Compared with females, males exhibited a lower activity of immune-related functions and higher activities of mitochondrial and ubiquitination functions. The pathway activities were associated with the prognosis of males but less relevant to females. We extracted eight pathways with sex-biased survival patterns, of which five were about down-regulated immune functions, and three were up-regulated pathways (GTP biosynthetic, DNA polymerase, and spliceosomal complex assembly). The five immune pathways were moderately or strongly correlated with the proportion of macrophages. We identified six over-expressed lncRNAs that might be involved in the regulation of the three up-regulated pathways. These lncRNAs exhibited a lower methylation density in males, which might account for their over-expression. Conclusions For HNSCCs, males were characterized by immunosuppression. It was a sign of unfavorable prognosis and might be associated the proportion of macrophages. LncRNAs and methylation might be involved in the regulation of these pathways.
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Affiliation(s)
- Shuo Wang
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - Wei Pan
- Institute for Macromolecular Chemistry, University of Freiburg, Stefan-Meier-Str. 31, 79104 Freiburg, Germany
| | - Wen-xiang Mi
- Department of Stomatology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China
| | - Shao-hai Wang
- Department of Stomatology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China
- Corresponding author. Department of Stomatology, Shanghai East Hospital, Tongji University School of Medicine, 150 Jimo Road, Shanghai 200120, China.
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29
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Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment. Biosci Rep 2023; 43:232194. [PMID: 36459212 PMCID: PMC9950536 DOI: 10.1042/bsr20221680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 12/03/2022] Open
Abstract
Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.
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30
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Aubin RG, Montelongo J, Hu R, Camara PG. Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.06.527318. [PMID: 36798206 PMCID: PMC9934539 DOI: 10.1101/2023.02.06.527318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in functionality and accuracy with respect to current deconvolution methods. Using ConDecon, we discover the implication of neurodegenerative microglial inflammatory pathways in the mesenchymal transformation of ependymoma, recapitulate spatial patterns of cell differentiation during zebrafish embryogenesis, and make temporal inferences from bulk ATAC-seq data. Overall, ConDecon significantly enhances our understanding of dynamic cellular processes within bulk tissue samples.
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Affiliation(s)
- Rachael G Aubin
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Javier Montelongo
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Robert Hu
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Pablo G Camara
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
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31
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Riggins TE, Whitsitt QA, Saxena A, Hunter E, Hunt B, Thompson CH, Moore MG, Purcell EK. Gene Expression Changes in Cultured Reactive Rat Astrocyte Models and Comparison to Device-Associated Effects in the Brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.06.522870. [PMID: 36712012 PMCID: PMC9881929 DOI: 10.1101/2023.01.06.522870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Implanted microelectrode arrays hold immense therapeutic potential for many neurodegenerative diseases. However, a foreign body response limits long-term device performance. Recent literature supports the role of astrocytes in the response to damage to the central nervous system (CNS) and suggests that reactive astrocytes exist on a spectrum of phenotypes, from beneficial to neurotoxic. The goal of our study was to gain insight into the subtypes of reactive astrocytes responding to electrodes implanted in the brain. In this study, we tested the transcriptomic profile of two reactive astrocyte culture models (cytokine cocktail or lipopolysaccharide, LPS) utilizing RNA sequencing, which we then compared to differential gene expression surrounding devices inserted into rat motor cortex via spatial transcriptomics. We interpreted changes in the genetic expression of the culture models to that of 24 hour, 1 week and 6 week rat tissue samples at multiple distances radiating from the injury site. We found overlapping expression of up to ∼250 genes between in vitro models and in vivo effects, depending on duration of implantation. Cytokine-induced cells shared more genes in common with chronically implanted tissue (≥1 week) in comparison to LPS-exposed cells. We revealed localized expression of a subset of these intersecting genes (e.g., Serping1, Chi3l1, and Cyp7b1) in regions of device-encapsulating, glial fibrillary acidic protein (GFAP)-expressing astrocytes identified with immunohistochemistry. We applied a factorization approach to assess the strength of the relationship between reactivity markers and the spatial distribution of GFAP-expressing astrocytes in vivo . We also provide lists of hundreds of differentially expressed genes between reactive culture models and untreated controls, and we observed 311 shared genes between the cytokine induced model and the LPS-reaction induced control model. Our results show that comparisons of reactive astrocyte culture models with spatial transcriptomics data can reveal new biomarkers of the foreign body response to implantable neurotechnology. These comparisons also provide a strategy to assess the development of in vitro models of the tissue response to implanted electrodes.
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32
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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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Luo Y, Fan R. Deconvolution analysis of cell-type expression from bulk tissues by integrating with single-cell expression reference. Genet Epidemiol 2022; 46:615-628. [PMID: 35788983 PMCID: PMC9669104 DOI: 10.1002/gepi.22494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/22/2022] [Accepted: 05/16/2022] [Indexed: 11/06/2022]
Abstract
To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell-type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell-type-specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk-profiled samples by using reference single-cell (sc) RNA-sequencing (RNA-seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed-effect models provide similar results as an elegant machine learning algorithm named cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well-known and reliable procedure to reconstruct cell-type abundances and cell-type-specific gene expression profiles. In real data analysis, the mixed-effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA-seq data, it is better to use mixed models with heterogeneous mean and variance-covariance. As a byproduct, the mixed models provide fractions of covariance between subject-specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA-seq data.
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Affiliation(s)
- Yutong Luo
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC 20057
| | - Ruzong Fan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC 20057
- Computational and Statistical Genomics Branch, National Human Genome Research Institute (NHGRI), National Institutes of Health (NIH), Baltimore, MD 21224
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Okereke LC, Bello AU, Onwukwe EA. Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells. Cells 2022; 11:cells11223604. [PMID: 36429031 PMCID: PMC9688486 DOI: 10.3390/cells11223604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022] Open
Abstract
Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.
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Affiliation(s)
- Lois Chinwendu Okereke
- Department of Pure and Applied Mathematics, Mathematics Institute (Emerging Regional Centre of Excellence (ERCE) of the European Mathematical Society (EMS)), African University of Science and Technology, Abuja 900107, Nigeria
- Correspondence:
| | - Abdulmalik Usman Bello
- Department of Pure and Applied Mathematics, Mathematics Institute (Emerging Regional Centre of Excellence (ERCE) of the European Mathematical Society (EMS)), African University of Science and Technology, Abuja 900107, Nigeria
- Department of Mathematics, Federal University Dutsin-Ma, Dutsin-Ma 821101, Nigeria
| | - Emmanuel Akwari Onwukwe
- Department of Theoretical and Applied Physics, African University of Science and Technology, Abuja 900107, Nigeria
- Inspired Innovative Sustainable (IIS) Projects & Solutions Limited, Abuja 900107, Nigeria
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Lam KHB, Diamandis P. Niche deconvolution of the glioblastoma proteome reveals a distinct infiltrative phenotype within the proneural transcriptomic subgroup. Sci Data 2022; 9:596. [PMID: 36182941 PMCID: PMC9526702 DOI: 10.1038/s41597-022-01716-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 09/07/2022] [Indexed: 11/23/2022] Open
Abstract
Glioblastoma is often subdivided into three transcriptional subtypes (classical, proneural, mesenchymal) based on bulk RNA signatures that correlate with distinct genetic and clinical features. Potential cellular-level differences of these subgroups, such as the relative proportions of glioblastoma’s hallmark histopathologic features (e.g. brain infiltration, microvascular proliferation), may provide insight into their distinct phenotypes but are, however, not well understood. Here we leverage machine learning and reference proteomic profiles derived from micro-dissected samples of these major histomorphologic glioblastoma features to deconvolute and estimate niche proportions in an independent proteogenomically-characterized cohort. This approach revealed a strong association of the proneural transcriptional subtype with a diffusely infiltrating phenotype. Similarly, enrichment of a microvascular proliferation proteomic signature was seen within the mesenchymal subtype. This study is the first to link differences in the cellular pathology signatures and transcriptional profiles of glioblastoma, providing potential new insights into the genetic drivers and poor treatment response of specific subsets of glioblastomas.
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Affiliation(s)
- K H Brian Lam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada. .,Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada. .,Laboratory Medicine Program, University Health Network, 200 Elizabeth Street, Toronto, ON, Toronto, Ontario, M5G 2C4, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.
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36
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Early transcriptional responses of bronchial epithelial cells to whole cigarette smoke mirror those of in-vivo exposed human bronchial mucosa. Respir Res 2022; 23:227. [PMID: 36056356 PMCID: PMC9440516 DOI: 10.1186/s12931-022-02150-2] [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: 03/14/2022] [Accepted: 08/16/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Despite the well-known detrimental effects of cigarette smoke (CS), little is known about the complex gene expression dynamics in the early stages after exposure. This study aims to investigate early transcriptomic responses following CS exposure of airway epithelial cells in culture and compare these to those found in human CS exposure studies. METHODS Primary bronchial epithelial cells (PBEC) were differentiated at the air-liquid interface (ALI) and exposed to whole CS. Bulk RNA-sequencing was performed at 1 h, 4 h, and 24 h hereafter, followed by differential gene expression analysis. Results were additionally compared to data retrieved from human CS studies. RESULTS ALI-PBEC gene expression in response to CS was most significantly changed at 4 h after exposure. Early transcriptomic changes (1 h, 4 h post CS exposure) were related to oxidative stress, xenobiotic metabolism, higher expression of immediate early genes and pro-inflammatory pathways (i.e., Nrf2, AP-1, AhR). At 24 h, ferroptosis-associated genes were significantly increased, whereas PRKN, involved in removing dysfunctional mitochondria, was downregulated. Importantly, the transcriptome dynamics of the current study mirrored in-vivo human studies of acute CS exposure, chronic smokers, and inversely mirrored smoking cessation. CONCLUSION These findings show that early after CS exposure xenobiotic metabolism and pro-inflammatory pathways were activated, followed by activation of the ferroptosis-related cell death pathway. Moreover, significant overlap between these transcriptomic responses in the in-vitro model and human in-vivo studies was found, with an early response of ciliated cells. These results provide validation for the use of ALI-PBEC cultures to study the human lung epithelial response to inhaled toxicants.
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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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38
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Bottomly D, Long N, Schultz AR, Kurtz SE, Tognon CE, Johnson K, Abel M, Agarwal A, Avaylon S, Benton E, Blucher A, Borate U, Braun TP, Brown J, Bryant J, Burke R, Carlos A, Chang BH, Cho HJ, Christy S, Coblentz C, Cohen AM, d'Almeida A, Cook R, Danilov A, Dao KHT, Degnin M, Dibb J, Eide CA, English I, Hagler S, Harrelson H, Henson R, Ho H, Joshi SK, Junio B, Kaempf A, Kosaka Y, Laderas T, Lawhead M, Lee H, Leonard JT, Lin C, Lind EF, Liu SQ, Lo P, Loriaux MM, Luty S, Maxson JE, Macey T, Martinez J, Minnier J, Monteblanco A, Mori M, Morrow Q, Nelson D, Ramsdill J, Rofelty A, Rogers A, Romine KA, Ryabinin P, Saultz JN, Sampson DA, Savage SL, Schuff R, Searles R, Smith RL, Spurgeon SE, Sweeney T, Swords RT, Thapa A, Thiel-Klare K, Traer E, Wagner J, Wilmot B, Wolf J, Wu G, Yates A, Zhang H, Cogle CR, Collins RH, Deininger MW, Hourigan CS, Jordan CT, Lin TL, Martinez ME, Pallapati RR, Pollyea DA, Pomicter AD, Watts JM, Weir SJ, Druker BJ, McWeeney SK, Tyner JW. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell 2022; 40:850-864.e9. [PMID: 35868306 PMCID: PMC9378589 DOI: 10.1016/j.ccell.2022.07.002] [Citation(s) in RCA: 93] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/30/2022] [Accepted: 06/30/2022] [Indexed: 12/17/2022]
Abstract
Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.
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Affiliation(s)
- Daniel Bottomly
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Nicola Long
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Anna Reister Schultz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Stephen E Kurtz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Cristina E Tognon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Kara Johnson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Melissa Abel
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Anupriya Agarwal
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sammantha Avaylon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Erik Benton
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Aurora Blucher
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Uma Borate
- Division of Hematology, Department of Internal Medicine, James Cancer Center, Ohio State University, Columbus, OH 43210, USA
| | - Theodore P Braun
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jordana Brown
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jade Bryant
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Russell Burke
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Amy Carlos
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA
| | - Bill H Chang
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology and Oncology, Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Hyun Jun Cho
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Stephen Christy
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Cody Coblentz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Aaron M Cohen
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Amanda d'Almeida
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rachel Cook
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alexey Danilov
- Department of Hematology and Hematopoietic Stem Cell Transplant, City of Hope National Medical Center, Duarte, CA 91010, USA
| | | | - Michie Degnin
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - James Dibb
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christopher A Eide
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Isabel English
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Stuart Hagler
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Heath Harrelson
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rachel Henson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA
| | - Hibery Ho
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sunil K Joshi
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Brian Junio
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Andy Kaempf
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Biostatistics Shared Resource, Oregon Health & Science University, Portland, OR 97239, USA
| | - Yoko Kosaka
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Matt Lawhead
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Hyunjung Lee
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jessica T Leonard
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Chenwei Lin
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA
| | - Evan F Lind
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Selina Qiuying Liu
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Pierrette Lo
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Marc M Loriaux
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Pathology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Samuel Luty
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Julia E Maxson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Tara Macey
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jacqueline Martinez
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jessica Minnier
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Biostatistics Shared Resource, Oregon Health & Science University, Portland, OR 97239, USA; OHSU-PSU School of Public Health, VA Portland Health Care System, Oregon Health & Science University, Portland, OR 97239, USA
| | - Andrea Monteblanco
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Motomi Mori
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Quinlan Morrow
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Dylan Nelson
- High-Throughput Screening Services Laboratory, Oregon State University, Corvallis, OR 97331, USA
| | - Justin Ramsdill
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Angela Rofelty
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alexandra Rogers
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Kyle A Romine
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Peter Ryabinin
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jennifer N Saultz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - David A Sampson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Samantha L Savage
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Robert Searles
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rebecca L Smith
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Stephen E Spurgeon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Tyler Sweeney
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Ronan T Swords
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Aashis Thapa
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Karina Thiel-Klare
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Elie Traer
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jake Wagner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Beth Wilmot
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joelle Wolf
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Amy Yates
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Haijiao Zhang
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christopher R Cogle
- Department of Medicine, Division of Hematology and Oncology, University of Florida, Gainesville, FL 32610, USA
| | - Robert H Collins
- Department of Internal Medicine/ Hematology Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390-8565, USA
| | - Michael W Deininger
- Division of Hematology & Hematologic Malignancies, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84112, USA; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher S Hourigan
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20814-1476, USA
| | - Craig T Jordan
- Division of Hematology, University of Colorado, Denver, CO 80045, USA
| | - Tara L Lin
- Division of Hematologic Malignancies & Cellular Therapeutics, University of Kansas, Kansas City, KS 66205, USA
| | - Micaela E Martinez
- Clinical Research Services, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL 33136, USA
| | - Rachel R Pallapati
- Clinical Research Services, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL 33136, USA
| | - Daniel A Pollyea
- Division of Hematology, University of Colorado, Denver, CO 80045, USA
| | - Anthony D Pomicter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Justin M Watts
- Division of Hematology, Department of Medicine, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL 33136, USA
| | - Scott J Weir
- Department of Cancer Biology, Division of Medical Oncology, Department of Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Brian J Druker
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA.
| | - Shannon K McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA.
| | - Jeffrey W Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA.
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Mizuno K, Beltran H. Future directions for precision oncology in prostate cancer. Prostate 2022; 82 Suppl 1:S86-S96. [PMID: 35657153 PMCID: PMC9942493 DOI: 10.1002/pros.24354] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/28/2022] [Indexed: 11/06/2022]
Abstract
Clinical genomic testing is becoming routine in prostate cancer, as biomarker-driven therapies such as poly-ADP ribose polymerase (PARP) inhibitors and anti-PD1 immunotherapy are now approved for select men with castration-resistant prostate cancer harboring alterations in DNA repair genes. Challenges for precision medicine in prostate cancer include an overall low prevalence of actionable genomic alterations and a still limited understanding of the impact of tumor heterogeneity and co-occurring alterations on treatment response and outcomes across diverse patient populations. Expanded tissue-based technologies such as whole-genome sequencing, transcriptome analysis, epigenetic analysis, and single-cell RNA sequencing have not yet entered the clinical realm and could potentially improve upon our understanding of how molecular features of tumors, intratumoral heterogeneity, and the tumor microenvironment impact therapy response and resistance. Blood-based technologies including cell-free DNA, circulating tumor cells (CTCs), and extracellular vesicles (EVs) are less invasive molecular profiling resources that could also help capture intraindividual tumor heterogeneity and track dynamic changes that occur in the context of specific therapies. Furthermore, molecular imaging is an important biomarker tool within the framework of prostate cancer precision medicine with a capability to detect heterogeneity across metastases and potential therapeutic targets less invasively. Here, we review recent technological advances that may help promote the future implementation and value of precision oncology testing for patients with advanced prostate cancer.
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Affiliation(s)
- Kei Mizuno
- Department of Medical Oncology, Dana Farber Cancer Institute
| | - Himisha Beltran
- Department of Medical Oncology, Dana Farber Cancer Institute
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40
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Whitsitt QA, Koo B, Celik ME, Evans BM, Weiland JD, Purcell EK. Spatial Transcriptomics as a Novel Approach to Redefine Electrical Stimulation Safety. Front Neurosci 2022; 16:937923. [PMID: 35928007 PMCID: PMC9344921 DOI: 10.3389/fnins.2022.937923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
Current standards for safe delivery of electrical stimulation to the central nervous system are based on foundational studies which examined post-mortem tissue for histological signs of damage. This set of observations and the subsequently proposed limits to safe stimulation, termed the “Shannon limits,” allow for a simple calculation (using charge per phase and charge density) to determine the intensity of electrical stimulation that can be delivered safely to brain tissue. In the three decades since the Shannon limits were reported, advances in molecular biology have allowed for more nuanced and detailed approaches to be used to expand current understanding of the physiological effects of stimulation. Here, we demonstrate the use of spatial transcriptomics (ST) in an exploratory investigation to assess the biological response to electrical stimulation in the brain. Electrical stimulation was delivered to the rat visual cortex with either acute or chronic electrode implantation procedures. To explore the influence of device type and stimulation parameters, we used carbon fiber ultramicroelectrode arrays (7 μm diameter) and microwire electrode arrays (50 μm diameter) delivering charge and charge density levels selected above and below reported tissue damage thresholds (range: 2–20 nC, 0.1–1 mC/cm2). Spatial transcriptomics was performed using Visium Spatial Gene Expression Slides (10x Genomics, Pleasanton, CA, United States), which enabled simultaneous immunohistochemistry and ST to directly compare traditional histological metrics to transcriptional profiles within each tissue sample. Our data give a first look at unique spatial patterns of gene expression that are related to cellular processes including inflammation, cell cycle progression, and neuronal plasticity. At the acute timepoint, an increase in inflammatory and plasticity related genes was observed surrounding a stimulating electrode compared to a craniotomy control. At the chronic timepoint, an increase in inflammatory and cell cycle progression related genes was observed both in the stimulating vs. non-stimulating microwire electrode comparison and in the stimulating microwire vs. carbon fiber comparison. Using the spatial aspect of this method as well as the within-sample link to traditional metrics of tissue damage, we demonstrate how these data may be analyzed and used to generate new hypotheses and inform safety standards for stimulation in cortex.
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Affiliation(s)
- Quentin A. Whitsitt
- Department of Biomedical Engineering, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Beomseo Koo
- Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States
| | - Mahmut Emin Celik
- Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey
| | - Blake M. Evans
- Department of Biomedical Engineering, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - James D. Weiland
- Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, United States
| | - Erin K. Purcell
- Department of Biomedical Engineering, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- *Correspondence: Erin K. Purcell,
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41
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Yao T, Liu Q, Tian W. Deconvolution of a Large Cohort of Placental Microarray Data Reveals Clinically Distinct Subtypes of Preeclampsia. Front Bioeng Biotechnol 2022; 10:917086. [PMID: 35910034 PMCID: PMC9326345 DOI: 10.3389/fbioe.2022.917086] [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: 04/10/2022] [Accepted: 06/13/2022] [Indexed: 11/28/2022] Open
Abstract
It has been well established that the dysfunctional placenta plays an important role in the pathogenesis of preeclampsia (PE), a hypertensive disorder in pregnancy. However, it is not well understood how individual cell types in the placenta are involved in placenta dysfunction because of limited single-cell studies of placenta with PE. Given that a high-resolution single-cell atlas in the placenta is now available, deconvolution of publicly available bulk PE transcriptome data may provide us with the opportunity to investigate the contribution of individual placental cell types to PE. Recent benchmark studies on deconvolution have provided suggestions on the strategy of marker gene selection and the choice of methodologies. In this study, we experimented with these suggestions by using real bulk data with known cell-type proportions and established a deconvolution pipeline using CIBERSORT. Applying the deconvolution pipeline to a large cohort of PE placental microarray data, we found that the proportions of trophoblast cells in the placenta were significantly different between PE and normal controls. We then predicted cell-type-level expression profiles for each sample using CIBERSORTx and found that the activities of several canonical PE-related pathways were significantly altered in specific subtypes of trophoblasts in PE. Finally, we constructed an integrated expression profile for each PE sample by combining the predicted cell-type-level expression profiles of several clinically relevant placental cell types and identified four clusters likely representing four PE subtypes with clinically distinct features. As such, our study showed that deconvolution of a large cohort of placental microarray provided new insights about the molecular mechanism of PE that would not be obtained by analyzing bulk expression profiles.
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Affiliation(s)
- Tian Yao
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Qiming Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Weidong Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
- Children’s Hospital of Fudan University, Shanghai, China
- Qilu Children’s Hospital of Shandong University, Jinan, China
- *Correspondence: Weidong Tian,
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Miedema SSM, Mol MO, Koopmans FTW, Hondius DC, van Nierop P, Menden K, de Veij Mestdagh CF, van Rooij J, Ganz AB, Paliukhovich I, Melhem S, Li KW, Holstege H, Rizzu P, van Kesteren RE, van Swieten JC, Heutink P, Smit AB. Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia. Acta Neuropathol Commun 2022; 10:100. [PMID: 35799292 PMCID: PMC9261008 DOI: 10.1186/s40478-022-01387-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/22/2022] [Indexed: 11/16/2022] Open
Abstract
Frontotemporal dementia is characterized by progressive atrophy of frontal and/or temporal cortices at an early age of onset. The disorder shows considerable clinical, pathological, and genetic heterogeneity. Here we investigated the proteomic signatures of frontal and temporal cortex from brains with frontotemporal dementia due to GRN and MAPT mutations to identify the key cell types and molecular pathways in their pathophysiology. We compared patients with mutations in the GRN gene (n = 9) or with mutations in the MAPT gene (n = 13) with non-demented controls (n = 11). Using quantitative proteomic analysis on laser-dissected tissues we identified brain region-specific protein signatures for both genetic subtypes. Using published single cell RNA expression data resources we deduced the involvement of major brain cell types in driving these different protein signatures. Subsequent gene ontology analysis identified distinct genetic subtype- and cell type-specific biological processes. For the GRN subtype, we observed a distinct role for immune processes related to endothelial cells and for mitochondrial dysregulation in neurons. For the MAPT subtype, we observed distinct involvement of dysregulated RNA processing, oligodendrocyte dysfunction, and axonal impairments. Comparison with an in-house protein signature of Alzheimer’s disease brains indicated that the observed alterations in RNA processing and oligodendrocyte function are distinct for the frontotemporal dementia MAPT subtype. Taken together, our results indicate the involvement of different brain cell types and biological mechanisms in genetic subtypes of frontotemporal dementia. Furthermore, we demonstrate that comparison of proteomic profiles of different disease entities can separate general neurodegenerative processes from disease-specific pathways, which may aid the development of disease subtype-specific treatment strategies.
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Affiliation(s)
- Suzanne S M Miedema
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands.
| | - Merel O Mol
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Frank T W Koopmans
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - David C Hondius
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - Pim van Nierop
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - Kevin Menden
- German Center for Neurodegenerative Diseases (DZNE)-Tübingen, Tübingen, Germany
| | - Christina F de Veij Mestdagh
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands.,Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, the Netherlands.,Alzheimer Center, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - Jeroen van Rooij
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Andrea B Ganz
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands.,Alzheimer Center, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - Iryna Paliukhovich
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - Shamiram Melhem
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ka Wan Li
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - Henne Holstege
- Alzheimer Center, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands.,Department of Clinical Genetics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - Patrizia Rizzu
- German Center for Neurodegenerative Diseases (DZNE)-Tübingen, Tübingen, Germany
| | - Ronald E van Kesteren
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Peter Heutink
- German Center for Neurodegenerative Diseases (DZNE)-Tübingen, Tübingen, Germany
| | - August B Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, W&N Building, C314. De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
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Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Background: Understanding the proportion of cell types in heterogeneous tissue samples is important in bioinformatics. It is a challenge to infer the proportion of tissues using bulk RNA sequencing data in bioinformatics because most traditional algorithms for predicting tissue cell ratios heavily rely on standardized specific cell-type gene expression profiles, and do not consider tissue heterogeneity. The prediction accuracy of algorithms is limited, and robustness is lacking. This means that new approaches are needed urgently. Methods: In this study, we introduced an algorithm that automatically predicts tissue cell ratios named Autoptcr. The algorithm uses the data simulated by single-cell RNA sequencing (ScRNA-Seq) for model training, using convolutional neural networks (CNNs) to extract intrinsic relationships between genes and predict the cell proportions of tissues. Results: We trained the algorithm using simulated bulk samples and made predictions using real bulk PBMC data. Comparing Autoptcr with existing advanced algorithms, the Pearson correlation coefficient between the actual value of Autoptcr and the predicted value was the highest, reaching 0.903. Tested on a bulk sample, the correlation coefficient of Lin was 41% higher than that of CSx. The algorithm can infer tissue cell proportions directly from tissue gene expression data. Conclusions: The Autoptcr algorithm uses simulated ScRNA-Seq data for training to solve the problem of specific cell-type gene expression profiles. It also has high prediction accuracy and strong noise resistance for the tissue cell ratio. This work is expected to provide new research ideas for the prediction of tissue cell proportions.
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Liu Q, Hu P. A novel integrative computational framework for breast cancer radiogenomic biomarker discovery. Comput Struct Biotechnol J 2022; 20:2484-2494. [PMID: 35664228 PMCID: PMC9136270 DOI: 10.1016/j.csbj.2022.05.031] [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: 02/14/2022] [Revised: 05/14/2022] [Accepted: 05/15/2022] [Indexed: 12/22/2022] Open
Abstract
Bayesian tensor factorization is used to integrate multiomics data for radiogenomics analysis. A regression framework is proposed to handle the unmatched data issue in radiogenomics analysis. Deep learning is used to identify prognostic meaningful radiogenomic biomarkers for cancer.
In precise medicine, it is with great value to develop computational frameworks for identifying prognostic biomarkers which can capture both multi-genomic and phenotypic heterogeneity of breast cancer (BC). Radiogenomics is a field where medical images and genomic measurements are integrated and mined to solve challenging clinical problems. Previous radiogenomic studies suffered from data incompleteness, feature subjectivity and low interpretability. For example, the majority of the radiogenomic studies miss one or two of medical imaging data, genomic data, and clinical outcome data, which results in the data incomplete issue. Feature subjectivity issue comes from the extraction of imaging features with significant human involvement. Thus, there is an urgent need to address above-mentioned limitations so that fully automatic and transparent radiogenomic prognostic biomarkers could be identified for BC. We proposed a novel framework for BC prognostic radiogenomic biomarker identification. This framework involves an explainable DL model for image feature extraction, a Bayesian tensor factorization (BTF) processing for multi-genomic feature extraction, a leverage strategy to utilize unpaired imaging, genomic, and survival outcome data, and a mediation analysis to provide further interpretation for identified biomarkers. This work provided a new perspective for conducting a comprehensive radiogenomic study when only limited resources are given. Compared with baseline traditional radiogenomic biomarkers, the 23 biomarkers identified by the proposed framework performed better in indicating patients’ survival outcome. And their interpretability is guaranteed by different levels of build-in and follow-up analyses.
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Affiliation(s)
- Qian Liu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba R3E 0W3, Canada
- Corresponding author at: Department of Biochemistry and Medical Genetics, Room 308 - Basic Medical Sciences Building, 745 Bannatyne Avenue, University of Manitoba, Winnipeg, Manitoba R3E 0J9, Canada.
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Lario S, Ramírez-Lázaro MJ, Brunet-Vega A, Vila-Casadesús M, Aransay AM, Lozano JJ, Calvet X. Coding and non-coding co-expression network analysis identifies key modules and driver genes associated with precursor lesions of gastric cancer. Genomics 2022; 114:110370. [PMID: 35430283 DOI: 10.1016/j.ygeno.2022.110370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/23/2022] [Accepted: 04/11/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Helicobacter pylori infection is the most important risk factor for gastric cancer (GC). Human gastric adenocarcinoma develops after long-term H. pylori infection via the Correa cascade. This carcinogenic pathway describes the progression from gastritis to atrophy, intestinal metaplasia (IM), dysplasia and GC. Patients with atrophy and intestinal metaplasia are considered to have precancerous lesions of GC (PLGC). H. pylori eradication and endoscopy surveillance are currently the only interventions for preventing GC. Better knowledge of the biology of human PLGC may help find stratification markers and contribute to better understanding of biological mechanisms. One way to achieve this is by using co-expression network analysis. Weighted gene co-expression network analysis (WGCNA) is often used to identify modules from co-expression networks and relate them to clinical traits. It also allows identification of driver genes that may be critical for PLGC. AIM The purpose of this study was to identify co-expression modules and differential gene expression in dyspeptic patients at different stages of the Correa pathway. METHODS We studied 96 gastric biopsies from 78 patients that were clinically classified as: non-active (n = 10) and chronic-active gastritis (n = 20), atrophy (n = 12), and IM (n = 36). Gene expression of coding RNAs was determined by microarrays and non-coding RNAs by RNA-seq. The WGCNA package was used for network construction, module detection, module preservation and hub and driver gene selection. RESULTS WGCNA identified 20 modules for coding RNAs and 4 for each miRNA and small RNA class. Modules were associated with antrum and corpus gastric locations, chronic gastritis and IM. Notably, coding RNA modules correlated with the Correa cascade. One was associated with the presence of H. pylori. In three modules, the module eigengene (ME) gradually increased in the stages toward IM, while in three others the inverse relationship was found. One miRNA module was negatively correlated to IM and was used for a mRNA-miRNA integration analysis. WGCNA also uncovered driver genes. Driver genes show both high connectivity within a module and are significantly associated with clinical traits. Some of those genes have been previously involved in H. pylori carcinogenesis, but others are new. Lastly, using similar external transcriptomic data, we confirmed that the discovered mRNA modules were highly preserved. CONCLUSION Our analysis captured co-expression modules that provide valuable information to understand the pathogenesis of the progression of PLGC.
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Affiliation(s)
- Sergio Lario
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Digestive Diseases Unit, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain.
| | - María J Ramírez-Lázaro
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Digestive Diseases Unit, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Anna Brunet-Vega
- Oncology Unit, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Maria Vila-Casadesús
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Bioinformatics Platform, CIBEREHD, Barcelona, Spain
| | - Ana M Aransay
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Genome Analysis Platform, CIC bioGUNE, Bizkaia Technology Park, Derio, Bizkaia, Spain
| | - Juan J Lozano
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Bioinformatics Platform, CIBEREHD, Barcelona, Spain
| | - Xavier Calvet
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Madrid, Spain; Digestive Diseases Unit, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain; Departament de Medicina, UAB, Sabadell, Spain
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46
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Liu Y. scDeconv: an R package to deconvolve bulk DNA methylation data with scRNA-seq data and paired bulk RNA-DNA methylation data. Brief Bioinform 2022; 23:6572659. [PMID: 35453146 PMCID: PMC9271220 DOI: 10.1093/bib/bbac150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/26/2022] [Accepted: 04/04/2022] [Indexed: 11/14/2022] Open
Abstract
Many DNA methylation (DNAm) data are from tissues composed of various cell types, and hence cell deconvolution methods are needed to infer their cell compositions accurately. However, a bottleneck for DNAm data is the lack of cell-type-specific DNAm references. On the other hand, scRNA-seq data are being accumulated rapidly with various cell-type transcriptomic signatures characterized, and also, many paired bulk RNA-DNAm data are publicly available currently. Hence, we developed the R package scDeconv to use these resources to solve the reference deficiency problem of DNAm data and deconvolve them from scRNA-seq data in a trans-omics manner. It assumes that paired samples have similar cell compositions. So the cell content information deconvolved from the scRNA-seq and paired RNA data can be transferred to the paired DNAm samples. Then an ensemble model is trained to fit these cell contents with DNAm features and adjust the paired RNA deconvolution in a co-training manner. Finally, the model can be used on other bulk DNAm data to predict their relative cell-type abundances. The effectiveness of this method is proved by its accurate deconvolution on the three testing datasets here, and if given an appropriate paired dataset, scDeconv can also deconvolve other omics, such as ATAC-seq data. Furthermore, the package also contains other functions, such as identifying cell-type-specific inter-group differential features from bulk DNAm data. scDeconv is available at: https://github.com/yuabrahamliu/scDeconv.
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Affiliation(s)
- Yu Liu
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
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47
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Feng C, Li T, Xiao J, Wang J, Meng X, Niu H, Jiang B, Huang L, Deng X, Yan X, Wu D, Fang Y, Lin Y, Chen F, Wu X, Zhao X, Feng J. Tumor Microenvironment Profiling Identifies Prognostic Signatures and Suggests Immunotherapeutic Benefits in Neuroblastoma. Front Cell Dev Biol 2022; 10:814836. [PMID: 35493068 PMCID: PMC9047956 DOI: 10.3389/fcell.2022.814836] [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: 11/14/2021] [Accepted: 01/27/2022] [Indexed: 12/14/2022] Open
Abstract
The tumor microenvironment (TME) influences disease initiation and progression. Cross-talks of cells within TME can affect the efficacy of immunotherapies. However, a precise, concise, and comprehensive TME landscape in neuroblastoma (NB) has not been established. Here, we profiled the TME landscape of 498 NB-related patients on a self-curated gene list and identified three prognostic TMEsubgroups. The differentially expressed genes in these three TMEsubgroups were used to construct a genetic signature of the TME landscape and characterize three GeneSubgroups. The subgroup with the worst overall survival prognosis, the TMEsubgroup/GeneSubgroup3, lacked immune cell infiltration and received the highest scores of MYCN- and ALK-related signatures and lowest scores of immune pathways. Additionally, we found that the GeneSubgroup3 might be benefited from anti-GD2 instead of anti-PD-1 therapy. We further created a 48-gene signature, the TMEscore, to infer prognosis and validated it in three independent NB cohorts and a pan-cancer cohort of 9,460 patients. We did RNA-seq on 16 samples and verified that TMEscore was higher in patients with stage 3/4 than stage 1/2 diseases. The TMEscore could also predict responses for several immunotherapies. After adding clinical features, we found that the nomogram-based score system, the TMEIndex, surpassed the current risk system at predicting survivals. Our analysis explained TME at the transcriptome level and paved the way for immunotherapies in NB.
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Affiliation(s)
- Chenzhao Feng
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Li
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Xiao
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Wang
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xinyao Meng
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huizhong Niu
- Department of General Surgery, Children’s Hospital of Hebei Province, Shijiazhuang, China
| | - Bin Jiang
- Department of General Surgery, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Huang
- Department of General Surgery, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaogeng Deng
- Department of Pediatric Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xueqiang Yan
- Department of Pediatric Surgery, Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dianming Wu
- Department of Pediatric Surgery, Fujian Provincial Maternity and Children’s Hospital, Fuzhou, China
| | - Yifan Fang
- Department of Pediatric Surgery, Fujian Provincial Maternity and Children’s Hospital, Fuzhou, China
| | - Yu Lin
- Department of Pediatric Surgery, Fujian Provincial Maternity and Children’s Hospital, Fuzhou, China
| | - Feng Chen
- Department of Pediatric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- *Correspondence: Feng Chen, ; Xiaojuan Wu, ; Xiang Zhao, ; Jiexiong Feng,
| | - Xiaojuan Wu
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Feng Chen, ; Xiaojuan Wu, ; Xiang Zhao, ; Jiexiong Feng,
| | - Xiang Zhao
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Feng Chen, ; Xiaojuan Wu, ; Xiang Zhao, ; Jiexiong Feng,
| | - Jiexiong Feng
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Feng Chen, ; Xiaojuan Wu, ; Xiang Zhao, ; Jiexiong Feng,
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Abou Khouzam R, Zaarour RF, Brodaczewska K, Azakir B, Venkatesh GH, Thiery J, Terry S, Chouaib S. The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes? Front Immunol 2022; 13:828875. [PMID: 35211123 PMCID: PMC8861358 DOI: 10.3389/fimmu.2022.828875] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 01/19/2022] [Indexed: 12/15/2022] Open
Abstract
Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.
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Affiliation(s)
- Raefa Abou Khouzam
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
| | - Rania Faouzi Zaarour
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
| | - Klaudia Brodaczewska
- Laboratory of Molecular Oncology and Innovative Therapies, Military Institute of Medicine, Warsaw, Poland
| | - Bilal Azakir
- Faculty of Medicine, Beirut Arab University, Beirut, Lebanon
| | - Goutham Hassan Venkatesh
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
| | - Jerome Thiery
- INSERM U1186, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France.,Faculty of Medicine, University Paris Sud, Le Kremlin Bicêtre, France
| | - Stéphane Terry
- INSERM U1186, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France.,Faculty of Medicine, University Paris Sud, Le Kremlin Bicêtre, France.,Research Department, Inovarion, Paris, France
| | - Salem Chouaib
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates.,INSERM U1186, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
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49
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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: 36] [Impact Index Per Article: 18.0] [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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50
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Marquez-Galera A, de la Prida LM, Lopez-Atalaya JP. A protocol to extract cell-type-specific signatures from differentially expressed genes in bulk-tissue RNA-seq. STAR Protoc 2022; 3:101121. [PMID: 35118429 PMCID: PMC8792262 DOI: 10.1016/j.xpro.2022.101121] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Bulk-tissue RNA-seq is widely used to dissect variation in gene expression levels across tissues and under different experimental conditions. Here, we introduce a protocol that leverages existing single-cell expression data to deconvolve patterns of cell-type-specific gene expression in differentially expressed gene lists from highly heterogeneous tissue. We apply this protocol to interrogate cell-type-specific gene expression and variation in cell type composition between the distinct sublayers of the hippocampal CA1 region of the brain in a rodent model of epilepsy. For complete details on the use and execution of this protocol, please refer to Cid et al. (2021). A protocol to explore gene signatures from bulk RNA-seq at the cell-type-specific level Deconvolution of complex gene signatures from highly heterogeneous tissues Publicly available single-cell gene expression dataset is retrieved and curated Gene signatures across brain regions and disease states are surveyed in scRNA-seq data
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