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Missarova A, Dann E, Rosen L, Satija R, Marioni J. Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE. Genome Biol 2024; 25:189. [PMID: 39026254 PMCID: PMC11256449 DOI: 10.1186/s13059-024-03334-3] [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: 09/01/2023] [Accepted: 07/10/2024] [Indexed: 07/20/2024] Open
Abstract
Single-cell RNA-sequencing enables testing for differential expression (DE) between conditions at a cell type level. While powerful, one of the limitations of such approaches is that the sensitivity of DE testing is dictated by the sensitivity of clustering, which is often suboptimal. To overcome this, we present miloDE-a cluster-free framework for DE testing (available as an open-source R package). We illustrate the performance of miloDE on both simulated and real data. Using miloDE, we identify a transient hemogenic endothelia-like state in mouse embryos lacking Tal1 and detect distinct programs during macrophage activation in idiopathic pulmonary fibrosis.
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Affiliation(s)
- Alsu Missarova
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Leah Rosen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Rahul Satija
- Center for Genomics and Systems Biology, NYU, New York, USA.
- New York Genome Center, New York, USA.
| | - John Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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2
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Shwab EK, Gingerich DC, Man Z, Gamache J, Garrett ME, Crawford GE, Ashley-Koch AE, Serrano GE, Beach TG, Lutz MW, Chiba-Falek O. Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks. Acta Neuropathol Commun 2024; 12:111. [PMID: 38956662 PMCID: PMC11218415 DOI: 10.1186/s40478-024-01803-1] [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: 05/22/2024] [Accepted: 05/27/2024] [Indexed: 07/04/2024] Open
Abstract
The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.
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Affiliation(s)
- E Keats Shwab
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Daniel C Gingerich
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Zhaohui Man
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Julia Gamache
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Melanie E Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, 27701, USA
| | - Gregory E Crawford
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, 27708, USA
- Center for Advanced Genomic Technologies, Duke University Medical Center, Durham, NC, 27708, USA
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, 27701, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, 27708, USA
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Thomas G Beach
- Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Michael W Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA.
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA.
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3
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Islam MT, Liu Y, Hassan MM, Abraham PE, Merlet J, Townsend A, Jacobson D, Buell CR, Tuskan GA, Yang X. Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0029. [PMID: 38435807 PMCID: PMC10905259 DOI: 10.34133/bdr.0029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/28/2024] [Indexed: 03/05/2024] Open
Abstract
Plants are complex systems hierarchically organized and composed of various cell types. To understand the molecular underpinnings of complex plant systems, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity. Furthermore, scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology, which aims to modify plants genetically/epigenetically through genome editing, engineering, or re-writing based on rational design for increasing crop yield and quality, promoting the bioeconomy and enhancing environmental sustainability. In particular, data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects. To date, scRNA-seq has been demonstrated in a limited number of plant species, including model plants (e.g., Arabidopsis thaliana), agricultural crops (e.g., Oryza sativa), and bioenergy crops (e.g., Populus spp.). It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants. In this review, we summarize current technical advancements in plant scRNA-seq, including sample preparation, sequencing, and data analysis, to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples. We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research. Finally, we discuss the challenges and opportunities for the application of scRNA-seq in plants.
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Affiliation(s)
- Md Torikul Islam
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Yang Liu
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Md Mahmudul Hassan
- Department of Genetics and Plant Breeding,
Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jean Merlet
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Alice Townsend
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C. Robin Buell
- Center for Applied Genetic Technologies,
University of Georgia, Athens, GA 30602, USA
- Department of Crop and Soil Sciences,
University of Georgia, Athens, GA 30602, USA
- Institute of Plant Breeding, Genetics, and Genomics,
University of Georgia, Athens, GA 30602, USA
| | - Gerald A. Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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4
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Kim J, Lee J, Li X, Kunjravia N, Rambhia D, Cueto I, Kim K, Chaparala V, Ko Y, Garcet S, Zhou W, Cao J, Krueger JG. Multi-omics segregate different transcriptomic impacts of anti-IL-17A blockade on type 17 T-cells and regulatory immune cells in psoriasis skin. Front Immunol 2023; 14:1250504. [PMID: 37781383 PMCID: PMC10536146 DOI: 10.3389/fimmu.2023.1250504] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Durable psoriasis improvement has been reported in a subset of psoriasis patients after treatment withdrawal of biologics blocking IL-23/Type 17 T-cell (T17) autoimmune axis. However, it is not well understood if systemic blockade of the IL-23/T17 axis promotes immune tolerance in psoriasis skin. The purpose of the study was to find translational evidence that systemic IL-17A blockade promotes regulatory transcriptome modification in human psoriasis skin immune cell subsets. We analyzed human psoriasis lesional skin 6 mm punch biopsy tissues before and after systemic IL-17A blockade using the muti-genomics approach integrating immune cell-enriched scRNA-seq (n = 18), microarray (n = 61), and immunohistochemistry (n = 61) with repository normal control skin immune cell-enriched scRNA-seq (n = 10) and microarray (n = 8) data. For the T17 axis transcriptome, systemic IL-17A blockade depleted 100% of IL17A + T-cells and 95% of IL17F + T-cells in psoriasis skin. The expression of IL23A in DC subsets was also downregulated by IL-17A blockade. The expression of IL-17-driven inflammatory mediators (IL36G, S100A8, DEFB4A, and DEFB4B) in suprabasal keratinocytes was correlated with psoriasis severity and was downregulated by IL-17A blockade. For the regulatory DC transcriptome, the proportion of regulatory semimature DCs expressing regulatory DC markers of BDCA-3 (THBD) and DCIR (CLEC4A) was increased in posttreatment psoriasis lesional skin compared to pretreatment psoriasis lesional skin. In addition, IL-17A blockade induced higher expression of CD1C and CD14, which are markers of CD1c+ CD14+ dendritic cell (DC) subset that suppresses antigen-specific T-cell responses, in posttreatment regulatory semimature DCs compared to pretreatment regulatory semimature DCs. In conclusion, systemic IL-17A inhibition not only blocks the entire IL-23/T17 cell axis but also promotes regulatory gene expression in regulatory DCs in human psoriasis skin.
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Affiliation(s)
- Jaehwan Kim
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
- Dermatology Section, Veterans Affairs Northern California Health Care System, Mather, CA, United States
- Department of Dermatology, University of California, Davis, Sacramento, CA, United States
| | - Jongmi Lee
- Dermatology Section, Veterans Affairs Northern California Health Care System, Mather, CA, United States
| | - Xuan Li
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
| | - Norma Kunjravia
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
| | - Darshna Rambhia
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
| | - Inna Cueto
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
| | - Katherine Kim
- Dermatology Section, Veterans Affairs Northern California Health Care System, Mather, CA, United States
- Department of Dermatology, University of California, Davis, Sacramento, CA, United States
| | - Vasuma Chaparala
- Dermatology Section, Veterans Affairs Northern California Health Care System, Mather, CA, United States
| | - Younhee Ko
- Department of Dermatology, University of California, Davis, Sacramento, CA, United States
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Seoul, Republic of Korea
| | - Sandra Garcet
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
- Research Bioinformatics, Center for Clinical and Translational Science, The Rockefeller University, New York, NY, United States
| | - Wei Zhou
- Laboratory of Single-cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, United States
| | - Junyue Cao
- Laboratory of Single-cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, United States
| | - James G. Krueger
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, United States
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Li K, Sun YH, Ouyang Z, Negi S, Gao Z, Zhu J, Wang W, Chen Y, Piya S, Hu W, Zavodszky MI, Yalamanchili H, Cao S, Gehrke A, Sheehan M, Huh D, Casey F, Zhang X, Zhang B. scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing. BMC Genomics 2023; 24:228. [PMID: 37131143 PMCID: PMC10155351 DOI: 10.1186/s12864-023-09332-2] [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: 01/17/2023] [Accepted: 04/25/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.
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Affiliation(s)
- Kejie Li
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Yu H Sun
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | | | - Soumya Negi
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Zhen Gao
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Jing Zhu
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Wanli Wang
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Yirui Chen
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Sarbottam Piya
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Wenxing Hu
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Maria I Zavodszky
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Hima Yalamanchili
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Shaolong Cao
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Andrew Gehrke
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Mark Sheehan
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Dann Huh
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Fergal Casey
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Xinmin Zhang
- Data Science, BioInfoRx Inc., Madison, WI, 53719, USA
| | - Baohong Zhang
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA.
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Zhang S, Xie L, Cui Y, Carone BR, Chen Y. Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance. Biomolecules 2022; 12:biom12081130. [PMID: 36009024 PMCID: PMC9405875 DOI: 10.3390/biom12081130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/12/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
The detection of differentially expressed genes (DEGs) is one of most important computational challenges in the analysis of single-cell RNA sequencing (scRNA-seq) data. However, due to the high heterogeneity and dropout noise inherent in scRNAseq data, challenges in detecting DEGs exist when using a single distribution of gene expression levels, leaving much room to improve the precision and robustness of current DEG detection methods. Here, we propose the use of a new method, DEGman, which utilizes several possible diverse distributions in combination with Bhattacharyya distance. DEGman can automatically select the best-fitting distributions of gene expression levels, and then detect DEGs by permutation testing of Bhattacharyya distances of the selected distributions from two cell groups. Compared with several popular DEG analysis tools on both large-scale simulation data and real scRNA-seq data, DEGman shows an overall improvement in the balance of sensitivity and precision. We applied DEGman to scRNA-seq data of TRAP; Ai14 mouse neurons to detect fear-memory-related genes that are significantly differentially expressed in neurons with and without fear memory. DEGman detected well-known fear-memory-related genes and many novel candidates. Interestingly, we found 25 DEGs in common in five neuron clusters that are functionally enriched for synaptic vesicles, indicating that the coupled dynamics of synaptic vesicles across in neurons plays a critical role in remote memory formation. The proposed method leverages the advantage of the use of diverse distributions in DEG analysis, exhibiting better performance in analyzing composite scRNA-seq datasets in real applications.
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Affiliation(s)
- Shaoqiang Zhang
- Department of Computer Science, College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Linjuan Xie
- Department of Computer Science, College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Yaxuan Cui
- Department of Computer Science, College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Benjamin R. Carone
- Department of Biology and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
| | - Yong Chen
- Department of Biology and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
- Correspondence: ; Tel.: +1-856-256-4500
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