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Dong W, Zhu C, Xie D, Zhang Y, Tao S, Tian C. Image restoration for ring-array photoacoustic tomography system based on blind spatially rotational deconvolution. Photoacoustics 2024; 38:100607. [PMID: 38665365 PMCID: PMC11044036 DOI: 10.1016/j.pacs.2024.100607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/17/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024]
Abstract
Ring-array photoacoustic tomography (PAT) system has been widely used in noninvasive biomedical imaging. However, the reconstructed image usually suffers from spatially rotational blur and streak artifacts due to the non-ideal imaging conditions. To improve the reconstructed image towards higher quality, we propose a concept of spatially rotational convolution to formulate the image blur process, then we build a regularized restoration problem model accordingly and design an alternating minimization algorithm which is called blind spatially rotational deconvolution to achieve the restored image. Besides, we also present an image preprocessing method based on the proposed algorithm to remove the streak artifacts. We take experiments on phantoms and in vivo biological tissues for evaluation, the results show that our approach can significantly enhance the resolution of the image obtained from ring-array PAT system and remove the streak artifacts effectively.
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Affiliation(s)
- Wende Dong
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China
- Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing, Jiangsu 211106, China
| | - Chenlong Zhu
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China
- Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing, Jiangsu 211106, China
| | - Dan Xie
- School of Engineering Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yanli Zhang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China
- Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing, Jiangsu 211106, China
| | - Shuyin Tao
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
| | - Chao Tian
- School of Engineering Science, University of Science and Technology of China, Hefei, Anhui 230026, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230088, China
- Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230088, China
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Li L, Zhang Q, Yuan X, Yang H, Qin S, Hong L, Pu L, Li L, Zhang P, Zhang J. Study of the molecular structure of proteins in fermented Maize-Soybean meal-based rations based on FTIR spectroscopy. Food Chem 2024; 441:138310. [PMID: 38218143 DOI: 10.1016/j.foodchem.2023.138310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/03/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024]
Abstract
This research investigates the dynamic alterations that occur in protein molecular structure during the fermentation process of feed. Fourier transform infrared spectroscopy (FTIR), coupled with deconvolution, second derivative and curve-fitting methodologies, was employed to comparatively analyse the protein molecular structures in fermented feed. At the 48-h fermentation mark, the α-helix and β-sheet contents reached their peaks, while the random coil and β-turn contents were at their lowest. Simultaneously, the β-sheet/α-helix ratio was minimized. FTIR spectroscopy emerged as a comprehensive tool, revealing the nuanced changes in molecular structure throughout the fermentation process of corn-soybean meal feed. When integrated with spectral quantitative analysis, it provides a novel perspective for evaluating the nutritional value of fermented feed.
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Affiliation(s)
- Long Li
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China; Hotan Vocational and Technical College,Xinjiang, Hotan 848000, China
| | - Qingnan Zhang
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China
| | - Xuefeng Yuan
- Tianjin Key Laboratory of Green Ecological Feed, Tianjin, Bao Di 301800, China
| | - Hua Yang
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China
| | - Shunyi Qin
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China
| | - Liang Hong
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China
| | - Lei Pu
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China
| | - Liuan Li
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China
| | - Pengyue Zhang
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China
| | - Jianbin Zhang
- Tianjin Key Laboratory of Agricultural Animal Breeding and Healthy Husbandry, College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Huuki-Myers LA, Montgomery KD, Kwon SH, Cinquemani S, Eagles NJ, Gonzalez-Padilla D, Maden SK, Kleinman JE, Hyde TM, Hicks SC, Maynard KR, Collado-Torres L. Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex. bioRxiv 2024:2024.02.09.579665. [PMID: 38405805 PMCID: PMC10888823 DOI: 10.1101/2024.02.09.579665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. Conclusions Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.
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Affiliation(s)
- Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Kelsey D. Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Sophia Cinquemani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | | | - Sean K. Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
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Castrogiovanni P, Sanfilippo C, Imbesi R, Lazzarino G, Li Volti G, Tibullo D, Vicario N, Parenti R, Giuseppe L, Barbagallo I, Alanazi AM, Vecchio M, Cappello F, Musumeci G, Di Rosa M. Skeletal muscle of young females under resistance exercise exhibits a unique innate immune cell infiltration profile compared to males and elderly individuals. J Muscle Res Cell Motil 2024:10.1007/s10974-024-09668-6. [PMID: 38578562 DOI: 10.1007/s10974-024-09668-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/12/2024] [Indexed: 04/06/2024]
Abstract
Muscle damage resulting from physical activities such as exercise triggers an immune response crucial for tissue repair and recovery. This study investigates the immune cell profiles in muscle biopsies of individuals engaged in resistance exercise (RE) and explores the impact of age and sex on the immune response following exercise-induced muscle damage. Microarray datasets from muscle biopsies of young and old subjects were analyzed, focusing on the gene expression patterns associated with immune cell activation. Genes were compared with immune cell signatures to reveal the cellular landscape during exercise. Results show that the most significant modulated gene after RE was Folliculin Interacting Protein 2 (FNIP2) a crucial regulator in cellular homeostasis. Moreover, the transcriptome was stratified based on the expression of FNIP2 and the 203 genes common to the groups obtained based on sex and age. Gene ontology analysis highlighted the FLCN-FNIP1-FNIP2 complex, which exerts as a negative feedback loop to Pi3k-Akt-mTORC1 pathway. Furthermore, we highlighted that the young females exhibit a distinct innate immune cell activation signature compared to males after a RE session. Specifically, young females demonstrate a notable overlap with dendritic cells (DCs), M1 macrophages, M2 macrophages, and neutrophils, while young males overlap with M1 macrophages, M2 macrophages, and motor neurons. Interestingly, in elderly subjects, both sexes display M1 macrophage activation signatures. Comparison of young and elderly signatures reveals an increased M1 macrophage percentage in young subjects. Additionally, common genes were identified in both sexes across different age groups, elucidating biological functions related to cell remodeling and immune activation. This study underscores the intricate interplay between sex, age, and the immune response in muscle tissue following RE, offering potential directions for future research. Nevertheless, there is a need for further studies to delve deeper and confirm the dynamics of immune cells in response to exercise-induced muscle damage.
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Affiliation(s)
- Paola Castrogiovanni
- Department of Biomedical and Biotechnological Sciences, Anatomy, Histology and Movement Sciences Section, School of Medicine, University of Catania, Catania, 95125, Italy
| | - Cristina Sanfilippo
- Neurologic Unit, Department of Medical, Surgical Sciences and Advanced Technologies, AOU "Policlinico-San Marco", University of Catania, Via Santa Sofia n.78, Sicily, GF, Ingrassia, Catania, 95100, Italy
| | - Rosa Imbesi
- Department of Biomedical and Biotechnological Sciences, Anatomy, Histology and Movement Sciences Section, School of Medicine, University of Catania, Catania, 95125, Italy
| | - Giacomo Lazzarino
- UniCamillus-Saint Camillus International University of Health Sciences, Via di Sant'Alessandro 8, Rome, 00131, Italy
| | - Giovanni Li Volti
- Department of Biomedical and Biotechnological Sciences, Section of Biochemistry, University of Catania, Catania, 95123, Italy
| | - Daniele Tibullo
- Department of Biomedical and Biotechnological Sciences, Section of Biochemistry, University of Catania, Catania, 95123, Italy
| | - Nunzio Vicario
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, 95123, Italy
| | - Rosalba Parenti
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, 95123, Italy
| | - Lazzarino Giuseppe
- Department of Biomedical and Biotechnological Sciences, Section of Biochemistry, University of Catania, Catania, 95123, Italy
| | - Ignazio Barbagallo
- Department of Biomedical and Biotechnological Sciences, Section of Biochemistry, University of Catania, Catania, 95123, Italy
| | - Amer M Alanazi
- Pharmaceutical Biotechnology Laboratory, Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Michele Vecchio
- Section of Pharmacology, Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, 95124, Italy
| | - Francesco Cappello
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University of Palermo, Palermo, 90127, Italy
- Euro-Mediterranean Institute of Science and Technology (IEMEST), Palermo, 90139, Italy
| | - Giuseppe Musumeci
- Department of Biomedical and Biotechnological Sciences, Anatomy, Histology and Movement Sciences Section, School of Medicine, University of Catania, Catania, 95125, Italy
| | - Michelino Di Rosa
- Department of Biomedical and Biotechnological Sciences, Anatomy, Histology and Movement Sciences Section, School of Medicine, University of Catania, Catania, 95125, Italy.
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Hannon ER, Marsit CJ, Dent AE, Embury P, Ogolla S, Midem D, Williams SM, Kazura JW. Transcriptome- and DNA methylation-based cell-type deconvolutions produce similar estimates of differential gene expression and differential methylation. Res Sq 2024:rs.3.rs-3992113. [PMID: 38645047 PMCID: PMC11030537 DOI: 10.21203/rs.3.rs-3992113/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Background Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs). Methods Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children (n = 8) before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modeling with Cibersortx or IDOL, respectively. Results Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60-0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils. Conclusion Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.
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Ma C, Liang C, Jiang Z, Zhang K, Xu Y. A novel time-frequency slice extraction method for target recognition and local enhancement of non-stationary signal features. ISA Trans 2024; 146:319-335. [PMID: 38220542 DOI: 10.1016/j.isatra.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 01/16/2024]
Abstract
Blind deconvolution can remove the effects of complex paths and extraneous disturbances, thus recovering simple features of the original fault source, and is used extensively in the field of fault diagnosis. However, it can only identify and extract the statistical mean of the fault impact features in a single domain and is unable to simultaneously highlight the local features of the signal in the time-frequency domain. Therefore, the extraction effect of weak fault signals is generally not ideal. In this paper, a new time-frequency slice extraction method is proposed. The method first computes a high temporal resolution spectrum of the signal by short-time Fourier transform to obtain multiple frequency slices with distinct temporal waveforms. Subsequently, the constructed harmonic spectral feature index is used to quantify and target the intensity of feature information in each frequency slice and enhance their fault characteristics using maximum correlation kurtosis deconvolution. Enhancing the local features of selected frequency slice clusters can reduce noise interference and obtain signal components with more obvious fault signatures. Finally, the validity of the method was confirmed by a simulated signal and fault diagnosis of the rolling bearing outer and inner rings was accomplished sequentially. Compared with other common deconvolution methods, the proposed method obtains more accurate and effective results in identifying fault messages.
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Affiliation(s)
- Chaoyong Ma
- Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Chen Liang
- Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Zuhua Jiang
- Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Kun Zhang
- Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
| | - Yonggang Xu
- Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
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Nishikawa T, Lee M, Amau M. New generative methods for single-cell transcriptome data in bulk RNA sequence deconvolution. Sci Rep 2024; 14:4156. [PMID: 38378978 PMCID: PMC10879528 DOI: 10.1038/s41598-024-54798-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 02/16/2024] [Indexed: 02/22/2024] Open
Abstract
Numerous methods for bulk RNA sequence deconvolution have been developed to identify cellular targets of diseases by understanding the composition of cell types in disease-related tissues. However, issues of heterogeneity in gene expression between subjects and the shortage of reference single-cell RNA sequence data remain to achieve accurate bulk deconvolution. In our study, we investigated whether a new data generative method named sc-CMGAN and benchmarking generative methods (Copula, CTGAN and TVAE) could solve these issues and improve the bulk deconvolutions. We also evaluated the robustness of sc-CMGAN using three deconvolution methods and four public datasets. In almost all conditions, the generative methods contributed to improved deconvolution. Notably, sc-CMGAN outperformed the benchmarking methods and demonstrated higher robustness. This study is the first to examine the impact of data augmentation on bulk deconvolution. The new generative method, sc-CMGAN, is expected to become one of the powerful tools for the preprocessing of bulk deconvolution.
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Affiliation(s)
- Toui Nishikawa
- Faculty of Medicine, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan.
| | - Masatoshi Lee
- Faculty of Medicine, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan
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Li S, Li Y, Sun Y, Feng G, Yang Z, Yan X, Gao X, Jiang Y, Du Y, Zhao S, Zhao H, Chen ZJ. Deconvolution at the single-cell level reveals ovarian cell-type-specific transcriptomic changes in PCOS. Reprod Biol Endocrinol 2024; 22:24. [PMID: 38373962 PMCID: PMC10875798 DOI: 10.1186/s12958-024-01195-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Polycystic ovary syndrome (PCOS) is one of the most common reproductive endocrine disorders in females of childbearing age. Various types of ovarian cells work together to maintain normal reproductive function, whose discordance often takes part in the development and progression of PCOS. Understanding the cellular heterogeneity and compositions of ovarian cells would provide insight into PCOS pathogenesis, but are, however, not well understood. Transcriptomic characterization of cells isolated from PCOS cases have been assessed using bulk RNA-seq but cells isolated contain a mixture of many ovarian cell types. METHODS Here we utilized the reference scRNA-seq data from human adult ovaries to deconvolute and estimate cell proportions and dysfunction of ovarian cells in PCOS, by integrating various granulosa cells(GCs) transcriptomic data. RESULTS We successfully defined 22 distinct cell clusters of human ovarian cells. Then after transcriptome integration, we obtained a gene expression matrix with 13,904 genes within 30 samples (15 control vs. 15 PCOS). Subsequent deconvolution analysis revealed decreased proportion of small antral GCs and increased proportion of KRT8high mural GCs, HTRA1high cumulus cells in PCOS, especially increased differentiation from small antral GCs to KRT8high mural GCs. For theca cells, the abundance of internal theca cells (TCs) and external TCs was both increased. Less TCF21high stroma cells (SCs) and more STARhigh SCs were observed. The proportions of NK cells and monocytes were decreased, and T cells occupied more in PCOS and communicated stronger with inTCs and exTCs. In the end, we predicted the candidate drugs which could be used to correct the proportion of ovarian cells in patients with PCOS. CONCLUSIONS Taken together, this study provides insights into the molecular alterations and cellular compositions in PCOS ovarian tissue. The findings might contribute to our understanding of PCOS pathophysiology and offer resource for PCOS basic research.
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Affiliation(s)
- Shumin Li
- Department of Reproductive Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, People's Republic of China
| | - Yimeng Li
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, People's Republic of China
- Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, Shandong, People's Republic of China
- Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, Shandong, People's Republic of China
| | - Yu Sun
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, People's Republic of China
- Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, Shandong, People's Republic of China
- Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, Shandong, People's Republic of China
| | - Gengchen Feng
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, People's Republic of China
- Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, Shandong, People's Republic of China
- Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, Shandong, People's Republic of China
| | - Ziyi Yang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, People's Republic of China
- Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, Shandong, People's Republic of China
- Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, Shandong, People's Republic of China
| | - Xueqi Yan
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, People's Republic of China
- Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, Shandong, People's Republic of China
- Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, Shandong, People's Republic of China
| | - Xueying Gao
- Department of Reproductive Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, People's Republic of China
| | - Yonghui Jiang
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China
| | - Yanzhi Du
- Department of Reproductive Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, People's Republic of China.
| | - Shigang Zhao
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China.
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, People's Republic of China.
- Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, Shandong, People's Republic of China.
- Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, Shandong, People's Republic of China.
- Shandong Key Laboratory of Reproductive Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.
| | - Han Zhao
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China.
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, People's Republic of China.
- Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, Shandong, People's Republic of China.
- Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, Shandong, People's Republic of China.
- Shandong Key Laboratory of Reproductive Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.
| | - Zi-Jiang Chen
- Department of Reproductive Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, People's Republic of China.
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, People's Republic of China.
- Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, Shandong, People's Republic of China.
- Research Unit of Gametogenesis and Health of ART-Offspring, Chinese Academy of Medical Sciences (No.2021RU001), Jinan, Shandong, People's Republic of China.
- Shandong Key Laboratory of Reproductive Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, People's Republic of China.
- Gusu School, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.
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11
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Zhang L, Fan M, Li L. Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer. J Imaging Inform Med 2024; 37:13-24. [PMID: 38343210 DOI: 10.1007/s10278-023-00915-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 03/02/2024]
Abstract
Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration-time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.
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Affiliation(s)
- Liangliang Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- School of Computer and Information, Anqing Normal University, Anqing, 246133, China
| | - Ming Fan
- Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Lihua Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
- Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
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12
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Li X, Zhang X, Fan C, Chen Y, Zheng J, Gao J, Shen Y. Deconvolution based on sparsity and continuity improves the quality of ultrasound image. Comput Biol Med 2024; 169:107860. [PMID: 38159397 DOI: 10.1016/j.compbiomed.2023.107860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 01/03/2024]
Abstract
The application of ultrasound (US) image has been limited by its limited resolution, inherent speckle noise, and the impact of clutter and artifacts, especially in the miniaturized devices with restricted hardware conditions. In order to solve these problems, many researchers have explored a number of hardware modifications as well as algorithmic improvements, but further improvements in resolution, signal-to-noise ratio (SNR) and contrast are still needed. In this paper, a deconvolution algorithm based on sparsity and continuity (DBSC) is proposed to obtain the higher resolution, SNR, and, contrast. The algorithm begins with a relatively bold Wiener filtering for initial enhancement of image resolution in preprocessing, but it also introduces ringing noise and compromises the SNR. In further processing, the noise is suppressed based on the characteristic that the adjacent pixels of the US image are continuous as long as Nyquist sampling criterion is met, and the extraction of high-frequency information is balanced by using relatively sparse. Subsequently, the theory and experiments demonstrate that relative sparsity and continuity are general properties of US images. DBSC is compared with other deconvolution strategies through simulations and experiments, and US imaging under different transmission channels is also investigated. The final results show that the proposed method can greatly improve the resolution, as well as provide significant advantages in terms of contrast and SNR, and is also feasible in applications to devices with limited hardware.
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Affiliation(s)
- Xiangyu Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Xin Zhang
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
| | - Chaolin Fan
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Yifei Chen
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Jie Zheng
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Jie Gao
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Yi Shen
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
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13
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Drexler R, Khatri R, Schüller U, Eckhardt A, Ryba A, Sauvigny T, Dührsen L, Mohme M, Ricklefs T, Bode H, Hausmann F, Huber TB, Bonn S, Voß H, Neumann JE, Silverbush D, Hovestadt V, Suvà ML, Lamszus K, Gempt J, Westphal M, Heiland DH, Hänzelmann S, Ricklefs FL. Temporal change of DNA methylation subclasses between matched newly diagnosed and recurrent glioblastoma. Acta Neuropathol 2024; 147:21. [PMID: 38244080 PMCID: PMC10799798 DOI: 10.1007/s00401-023-02677-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/08/2023] [Accepted: 12/24/2023] [Indexed: 01/22/2024]
Abstract
The longitudinal transition of phenotypes is pivotal in glioblastoma treatment resistance and DNA methylation emerged as an important tool for classifying glioblastoma phenotypes. We aimed to characterize DNA methylation subclass heterogeneity during progression and assess its clinical impact. Matched tissues from 47 glioblastoma patients were subjected to DNA methylation profiling, including CpG-site alterations, tissue and serum deconvolution, mass spectrometry, and immunoassay. Effects of clinical characteristics on temporal changes and outcomes were studied. Among 47 patients, 8 (17.0%) had non-matching classifications at recurrence. In the remaining 39 cases, 28.2% showed dominant DNA methylation subclass transitions, with 72.7% being a mesenchymal subclass. In general, glioblastomas with a subclass transition showed upregulated metabolic processes. Newly diagnosed glioblastomas with mesenchymal transition displayed increased stem cell-like states and decreased immune components at diagnosis and exhibited elevated immune signatures and cytokine levels in serum. In contrast, tissue of recurrent glioblastomas with mesenchymal transition showed increased immune components but decreased stem cell-like states. Survival analyses revealed comparable outcomes for patients with and without subclass transitions. This study demonstrates a temporal heterogeneity of DNA methylation subclasses in 28.2% of glioblastomas, not impacting patient survival. Changes in cell state composition associated with subclass transition may be crucial for recurrent glioblastoma targeted therapies.
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Affiliation(s)
- Richard Drexler
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Robin Khatri
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ulrich Schüller
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Pediatric Hematology and Oncology, Research Institute Children's Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Alicia Eckhardt
- Department of Pediatric Hematology and Oncology, Research Institute Children's Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
- Department of Radiation Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alice Ryba
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Thomas Sauvigny
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Lasse Dührsen
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Malte Mohme
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Tammo Ricklefs
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Helena Bode
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Fabian Hausmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias B Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hannah Voß
- Section of Mass Spectrometric Proteomics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Julia E Neumann
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dana Silverbush
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Volker Hovestadt
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mario L Suvà
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Katrin Lamszus
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Jens Gempt
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Manfred Westphal
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Dieter H Heiland
- Department of Neurosurgery, Medical Center University of Freiburg, Freiburg, Germany
| | - Sonja Hänzelmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Franz L Ricklefs
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
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14
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Thong A, Basri N, Chew W. Comparison of untargeted gas chromatography-mass spectrometry analysis algorithms with implications to the interpretation and putative identification of volatile aroma compositions. J Chromatogr A 2024; 1713:464519. [PMID: 38039625 DOI: 10.1016/j.chroma.2023.464519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/31/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023]
Abstract
The aroma profiling process requires the identification of the volatile compounds in a sample or its headspace. Typically, the identification of compounds relies on automated feature finding and matching algorithms to (putatively) identify and report compounds based on retention index and mass spectra matching against a compound library. We investigated the use of five different workflows and proposed three metrics (target accuracy A, identification percentage I, uniqueness U) to quantify their impact on generated aroma profiles of a mixture of fragrance standards and a commercial grade essential oil. All workflows accurately identified target compounds (100% in standards, >90% in samples) and reported similar compound identities for major GC-MS features, but beyond that could differ by up to 40-50%. Despite the variances, different workflows did not report conflicting compound identities. Aroma compositions primarily contained unreported or extra (putatively) identified compounds due to variations in mass spectral elucidations within the various workflows. Considering these differences, we show how the proposed metrics, I and U, could be modified to help the analyst interpret and evaluate reported volatile aroma compositions of unknown materials.
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Affiliation(s)
- Aaron Thong
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Nanos, 31 Biopolis Way, #01-02, 138669, Singapore
| | - Nurhidayah Basri
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Nanos, 31 Biopolis Way, #01-02, 138669, Singapore
| | - Wee Chew
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Nanos, 31 Biopolis Way, #01-02, 138669, Singapore.
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15
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Reynolds SR, Zhang Z, Salas LA, Christensen BC. Tumor microenvironment deconvolution identifies cell-type-independent aberrant DNA methylation and gene expression in prostate cancer. Clin Epigenetics 2024; 16:5. [PMID: 38173042 PMCID: PMC10765773 DOI: 10.1186/s13148-023-01609-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/25/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Among men, prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer death. Etiologic factors associated with both prostate carcinogenesis and somatic alterations in tumors are incompletely understood. While genetic variants associated with PCa have been identified, epigenetic alterations in PCa are relatively understudied. To date, DNA methylation (DNAm) and gene expression (GE) in PCa have been investigated; however, these studies did not correct for cell-type proportions of the tumor microenvironment (TME), which could confound results. METHODS The data (GSE183040) consisted of DNAm and GE data from both tumor and adjacent non-tumor prostate tissue of 56 patients who underwent radical prostatectomies prior to any treatment. This study builds upon previous studies that examined methylation patterns and GE in PCa patients by using a novel tumor deconvolution approach to identify and correct for cell-type proportions of the TME in its epigenome-wide association study (EWAS) and differential expression analysis (DEA). RESULTS The inclusion of cell-type proportions in EWASs and DEAs reduced the scope of significant alterations associated with PCa. We identified 2,093 significantly differentially methylated CpGs (DMC), and 51 genes associated with PCa, including PCA3, SPINK1, and AMACR. CONCLUSIONS This work illustrates the importance of correcting for cell types of the TME when performing EWASs and DEAs on PCa samples, and establishes a more confounding-adverse methodology. We identified a more tumor-cell-specific set of altered genes and epigenetic marks that can be further investigated as potential biomarkers of disease or potential therapeutic targets.
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Affiliation(s)
- Samuel R Reynolds
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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16
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Desale H, Herrera C, Dumonteil E. Trypanosoma cruzi amastigote transcriptome analysis reveals heterogenous populations with replicating and dormant parasites. Microbes Infect 2024; 26:105240. [PMID: 37866547 DOI: 10.1016/j.micinf.2023.105240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/13/2023] [Accepted: 10/17/2023] [Indexed: 10/24/2023]
Abstract
Trypanosoma cruzi is a protozoan parasite causing Chagas disease, with a complex life cycle involving different stages in insect vectors and mammalian hosts. Amastigotes are an intracellular form that replicates in the cytoplasm of host cells, and recent studies suggested that dormant forms may be contributing to parasite persistence, suggesting cellular heterogeneity among amastigotes. We investigated here if a transcriptomic approach could identify some heterogeneity in intracellular amastigotes and identify a dormant population. We used gene expression data derived from bulk RNA-sequencing of T. cruzi infection of human fibroblasts for deconvolution using CDSeq, which allows to simultaneously estimate amastigote cell-type proportions and cell-type-specific expression profiles. Six amastigote subpopulations were identified, confirming intracellular amastigotes heterogeneity, and one population presented characteristics of non-replicative dormant parasites, based on replication markers and TcRAD51 expression. Transcriptomic approaches appear to be powerful to understand T. cruzi cell differentiation and expansion of these studies could provide further insight on the role different cell types in parasite persistence and Chagas disease pathogenesis.
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Affiliation(s)
- Hans Desale
- Department of Tropical Medicine, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, New Orleans, LA, USA
| | - Claudia Herrera
- Department of Tropical Medicine, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, New Orleans, LA, USA
| | - Eric Dumonteil
- Department of Tropical Medicine, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, New Orleans, LA, USA.
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17
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Lee JM, Park SU, Lee SD, Lee HY. Application of array-based age prediction models to post-mortem tissue samples. Forensic Sci Int Genet 2024; 68:102940. [PMID: 37857127 DOI: 10.1016/j.fsigen.2023.102940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/03/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023]
Abstract
Since DNA methylation at specific CpG sites exhibits a strong age association, researchers have developed numerous age prediction models based on the methylation BeadChip array. These models harness epigenetic clocks that hold the potential to narrow down the search range for unknown suspects and unidentified victims. This study collected 180 post-mortem tissue samples comprising nine tissue types (blood, brain, heart, lung, liver, kidney, muscle, epidermis, and dermis) from autopsies of 20 Koreans aged 18-78. Subsequently, DNA methylation profiling was conducted using the Infinium MethylationEPIC array. We tested several array-based age prediction models using the data obtained from various tissues. The pan-tissue clock exhibited a moderately accurate prediction across all nine tissue types (MAE = 8.7 years, r = 0.88). Notably, the DNAm ages of the Hannum clock, the skin & blood clock, and the Zhang clock strongly correlated with the actual age in blood samples (MAE < approximately 5 years, r > 0.9). PhenoAge yielded an MAE of 10.1 years and an r-value of 0.92. The muscle-specific epigenetic clock, the MEAT package, demonstrated high prediction accuracy in muscle samples (MAE = 4.7 years, r = 0.93). Those previously reported array-based age prediction models were mainly constructed in Europeans but performed well in Koreans. In addition, tests involving various quantities of DNA and fragmented DNA have shown that DNA quantity and quality affected methylation measurements and age prediction results. However, robust age prediction models exist under low amounts of DNA and fragmented DNA conditions.
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Affiliation(s)
- Jeong Min Lee
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Sang Un Park
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Soong Deok Lee
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, Korea; Institute of Forensic and Anthropological Science, Seoul National University College of Medicine, Seoul, Korea
| | - Hwan Young Lee
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, Korea; Institute of Forensic and Anthropological Science, Seoul National University College of Medicine, Seoul, Korea.
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18
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Zhang H, Li B. A novel computational tool for tracking cancer energy hijacking from immune cells. Clin Transl Med 2024; 14:e1533. [PMID: 38193607 PMCID: PMC10775179 DOI: 10.1002/ctm2.1533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 12/23/2023] [Indexed: 01/10/2024] Open
Abstract
Recent studies revealed a new biological process that malignant cancer cells hijack mitochondria from nearby T cells, providing another potential mechanism for immune evasion. We further confirmed this process at the single-cell genomic level through MERCI, a novel algorithm for tracking mitochondrial (MT) transfer. Applied to human cancer samples, MERCI identified a new cancer phenotype linked to MT hijacking, correlating with rapid tumour proliferation and poor patient survival. This discovery offers insights into the limitations of current cancer immunotherapies and suggests new therapeutic avenues targeting MT transfer to enhance cancer treatment efficacy.
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Affiliation(s)
- Hongyi Zhang
- Center for Computational and Genomic MedicineThe Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Bo Li
- Center for Computational and Genomic MedicineThe Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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19
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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20
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Yi M, Shi J, Tan X, Zhang X, Tao D, Yang Y, Liu Y. Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma. J Cancer Res Clin Oncol 2023; 149:16441-16460. [PMID: 37710052 DOI: 10.1007/s00432-023-05403-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE This study aims to establish a risk prediction model based on prognosis-related genes (PRGs) and clinicopathological factors, and investigate the biological activities of PRGs in lung adenocarcinoma (LUAD). METHODS Risk score signatures were developed by employing multiple algorithms and their amalgamations. A predictive model for overall survival was established through the integration of risk score signatures and several clinicopathological parameters. A comprehensive single-cell atlas, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to investigate the biological activities of prognosis-related genes in LUAD. RESULTS A risk prediction model was established based on 16 PRGs, exhibiting robust performance in predicting overall survival. The single-cell analysis revealed that epithelial cells were primarily associated with worse survival of LUAD, and PRGs were predominantly enriched in malignant epithelial cells and influenced epithelial cell growth and progression. Furthermore, GSEA and GSVA analysis showed that PRGs were involved in tumor pathways such as epithelial-mesenchymal transition, hypoxia and KRAS_UP, and high GSVA scores are correlated with worse outcome in LUAD patients. CONCLUSIONS The constructed risk prediction model in this study offers clinicians a valuable tool for tailoring treatment strategies of LUAD and provides a comprehensive interpretation on the biological activities of PRGs in LUAD.
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Affiliation(s)
- Ming Yi
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jiaying Shi
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaolan Tan
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xinyue Zhang
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Dachang Tao
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuan Yang
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yunqiang Liu
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
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21
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Stride B, Dykes C, Abolfathi S, Jimoh M, Bending GD, Pearson J. Microplastic transport dynamics in surcharging and overflowing manholes. Sci Total Environ 2023; 899:165683. [PMID: 37478932 DOI: 10.1016/j.scitotenv.2023.165683] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 07/23/2023]
Abstract
The transport of microplastics within urban water systems remains poorly understood, with little prior research on their behaviour within manhole configurations. This study represents the first to measure and model the transport dynamics of microplastics within circular and square manholes under different hydraulic scenarios. The transport and fate of polyethylene (PE) was quantified and compared to solutes (Rhodamine WT dye) using energy losses, residence time distributions (RTDs), and mixing models within surcharging and overflowing manholes. The bulk mass of solute and PE concentrations followed similar flow paths across all conditions except for 17.3 ± 7.9 % of PE mass that was immobilized in a dead zone above the inlet pipe for manholes with a surcharge to pipe diameter ratio ≥2. Consequently, these microplastics only exit after a significant change in hydraulic regime occurs, causing microplastics to be at risk of being contaminated over a prolonged duration. No significant mixing differences for PE and solutes were found between manhole geometries. The deconvolution method outperformed the ADZ model with goodness of fit (Rt2) values of 0.99 (0.60) and 1.00 (0.89) for PE and solute mixing, respectively. This establishes the deconvolution method as the most accurate and appropriate model to accurately predict microplastic mixing in manholes and urban drainage systems.
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Affiliation(s)
- Ben Stride
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
| | - Charlotte Dykes
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | | | - Modupe Jimoh
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Gary D Bending
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| | - Jonathan Pearson
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
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22
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Calanca N, Francisco ALN, Bizinelli D, Kuasne H, Barros Filho MC, Flores BCT, Pinto CAL, Rainho CA, Soares MBP, Marchi FA, Kowalski LP, Rogatto SR. DNA methylation-based depiction of the immune microenvironment and immune-associated long non-coding RNAs in oral cavity squamous cell carcinomas. Biomed Pharmacother 2023; 167:115559. [PMID: 37742611 DOI: 10.1016/j.biopha.2023.115559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 09/26/2023] Open
Abstract
Oral cavity squamous cell carcinoma (OSCC) is a complex and dynamic disease characterized by clinicopathological and molecular heterogeneity. Spatial and temporal heterogeneity of cell subpopulations has been associated with cancer progression and implicated in the prognosis and therapy response. Emerging evidence indicates that aberrant epigenetic profiles in OSCC may foster an immunosuppressive tumor microenvironment by modulating the expression of immune-related long non-coding RNAs (lncRNAs). DNA methylation analysis was performed in 46 matched OSCC and normal adjacent tissue samples using a genome-wide platform (Infinium HumanMethylation450 BeadChip). Reference-based computational deconvolution (MethylCIBERSORT) was applied to infer the immune cell composition of the bulk samples. The expression levels of genes encoding immune markers and differentially methylated lncRNAs were investigated using The Cancer Genome Atlas dataset. OSCC specimens presented distinct immune cell composition, including the enrichment of monocyte lineage cells, natural killer cells, cytotoxic T-lymphocytes, regulatory T-lymphocytes, and neutrophils. In contrast, B-lymphocytes, effector T-lymphocytes, and fibroblasts were diminished in tumor samples. The hypomethylation of three immune-associated lncRNAs (MEG3, MIR155HG, and WFDC21P) at individual CpG sites was confirmed by bisulfite-pyrosequencing. Also, the upregulation of a set of immune markers (FOXP3, GZMB, IL10, IL2RA, TGFB, IFNG, TDO2, IDO1, and HIF1A) was detected. The immune cell composition, immune markers alteration, and dysregulation of immune-associated lncRNAs reinforce the impact of the immune microenvironment in OSCC. These concurrent factors contribute to tumor heterogeneity, suggesting that epi-immunotherapy could be an efficient alternative to treat OSCC.
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Affiliation(s)
- Naiade Calanca
- Department of Clinical Genetics, University Hospital of Southern Denmark-Vejle, Institute of Regional Health Research, University of Southern Denmark, Odense 5000, Denmark; Department of Chemical and Biological Sciences, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil
| | - Ana Lucia Noronha Francisco
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C.Camargo Cancer Center, São Paulo 01509-001, SP, Brazil
| | - Daniela Bizinelli
- International Research Center (CIPE), A.C.Camargo Cancer Center, São Paulo 01508-010, SP, Brazil
| | - Hellen Kuasne
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal H3A1A3, QC, Canada
| | | | - Bianca Campos Troncarelli Flores
- Department of Clinical Genetics, University Hospital of Southern Denmark-Vejle, Institute of Regional Health Research, University of Southern Denmark, Odense 5000, Denmark
| | | | - Claudia Aparecida Rainho
- Department of Chemical and Biological Sciences, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil
| | - Milena Botelho Pereira Soares
- Health Technology Institute, SENAI CIMATEC, Salvador 41650-010, BA, Brazil; Gonçalo Moniz Institute, FIOCRUZ, Salvador 40296-710, BA, Brazil
| | - Fabio Albuquerque Marchi
- Department of Head and Neck Surgery, University of São Paulo Medical School, São Paulo 05402-000, SP, Brazil
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C.Camargo Cancer Center, São Paulo 01509-001, SP, Brazil; Department of Head and Neck Surgery, University of São Paulo Medical School, São Paulo 05402-000, SP, Brazil
| | - Silvia Regina Rogatto
- Department of Clinical Genetics, University Hospital of Southern Denmark-Vejle, Institute of Regional Health Research, University of Southern Denmark, Odense 5000, Denmark.
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23
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Yang P, Hubert SM, Futreal PA, Song X, Zhang J, Lee JJ, Wistuba I, Yuan Y, Zhang J, Li Z. A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing. bioRxiv 2023:2023.10.24.563820. [PMID: 37961165 PMCID: PMC10634795 DOI: 10.1101/2023.10.24.563820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multi-region gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.
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Affiliation(s)
- Peng Yang
- Department of Statistics, Rice University, Houston, Texas 77005, U.S.A
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030, U.S.A
| | - Shawna M. Hubert
- Department of Thoracic Head Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - P. Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xingzhi Song
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030, U.S.A
| | - Ignacio Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030, U.S.A
| | - Jianjun Zhang
- Department of Thoracic Head Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030, U.S.A
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24
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Hippen AA, Omran DK, Weber LM, Jung E, Drapkin R, Doherty JA, Hicks SC, Greene CS. Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors. Genome Biol 2023; 24:239. [PMID: 37864274 PMCID: PMC10588129 DOI: 10.1186/s13059-023-03077-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 09/29/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay. RESULTS We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method. CONCLUSIONS Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.
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Affiliation(s)
- Ariel A Hippen
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dalia K Omran
- Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lukas M Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Euihye Jung
- Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ronny Drapkin
- Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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25
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Huuki-Myers LA, Montgomery KD, Kwon SH, Page SC, Hicks SC, Maynard KR, Collado-Torres L. Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue. Genome Biol 2023; 24:233. [PMID: 37845779 PMCID: PMC10578035 DOI: 10.1186/s13059-023-03066-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/20/2023] [Indexed: 10/18/2023] Open
Abstract
We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions.
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Affiliation(s)
- Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Kelsey D Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 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
| | - Stephanie C Page
- 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
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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26
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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27
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Handin N, Yuan D, Ölander M, Wegler C, Karlsson C, Jansson-Löfmark R, Hjelmesæth J, Åsberg A, Lauschke VM, Artursson P. Proteome deconvolution of liver biopsies reveals hepatic cell composition as an important marker of fibrosis. Comput Struct Biotechnol J 2023; 21:4361-4369. [PMID: 37711184 PMCID: PMC10498185 DOI: 10.1016/j.csbj.2023.08.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/31/2023] [Accepted: 08/31/2023] [Indexed: 09/16/2023] Open
Abstract
Human liver tissue is composed of heterogeneous mixtures of different cell types and their cellular stoichiometry can provide information on hepatic physiology and disease progression. Deconvolution algorithms for the identification of cell types and their proportions have recently been developed for transcriptomic data. However, no method for the deconvolution of bulk proteomics data has been presented to date. Here, we show that proteomes, which usually contain less data than transcriptomes, can provide useful information for cell type deconvolution using different algorithms. We demonstrate that proteomes from defined mixtures of cell lines, isolated primary liver cells, and human liver biopsies can be deconvoluted with high accuracy. In contrast to transcriptome-based deconvolution, liver tissue proteomes also provided information about extracellular compartments. Using deconvolution of proteomics data from liver biopsies of 56 patients undergoing Roux-en-Y gastric bypass surgery we show that proportions of immune and stellate cells correlate with inflammatory markers and altered composition of extracellular matrix proteins characteristic of early-stage fibrosis. Our results thus demonstrate that proteome deconvolution can be used as a molecular microscope for investigations of the composition of cell types, extracellular compartments, and for exploring cell-type specific pathological events. We anticipate that these findings will allow the refinement of retrospective analyses of the growing number of proteome datasets from various liver disease states and pave the way for AI-supported clinical and preclinical diagnostics.
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Affiliation(s)
- Niklas Handin
- Department of Pharmacy, Uppsala University, SE-75123 Uppsala, Sweden
| | - Di Yuan
- Department of Information Technology, Uppsala University, SE-75123 Uppsala, Sweden
| | - Magnus Ölander
- Department of Pharmacy, Uppsala University, SE-75123 Uppsala, Sweden
| | - Christine Wegler
- Department of Pharmacy, Uppsala University, SE-75123 Uppsala, Sweden
| | - Cecilia Karlsson
- Late-stage Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, SE- 41345, Sweden
| | - Rasmus Jansson-Löfmark
- DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43153, Sweden
| | - Jøran Hjelmesæth
- Morbid Obesity Centre, Department of Medi cine, Vestfold Hospital Trust, NO-3103 Tønsberg, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Institute of Clinical Medicine, University of Oslo, NO-0318 Oslo, Norway
| | - Anders Åsberg
- Department of Pharmacy, University of Oslo, NO-0316 Oslo, Norway
- Department of Transplanation Medicin, Oslo University Hospital-Rikshospitalet, NO-0424 Oslo, Norway
| | - Volker M. Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Per Artursson
- Department of Pharmacy, Uppsala University, SE-75123 Uppsala, Sweden
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28
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Wang X, Bao Q, Wang R, Kwok O, Maurus K, Wang Y, Qin B, Burgess DJ. In situ forming risperidone implants: Effect of PLGA attributes on product performance. J Control Release 2023; 361:777-791. [PMID: 37591464 DOI: 10.1016/j.jconrel.2023.08.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
Despite the unique advantages of injectable, long-acting in situ forming implant formulations based on poly(lactide-co-glycolide) (PLGA) and N-Methyl-2-Pyrrolidone (NMP), only six products are commercially available. A better understanding of PLGA will aid in the development of more in situ forming implant innovator and generic products. This article investigates the impact of slight changes in PLGA attributes, i.e., molecular weight (MW), lactide:glycolide (L/G) ratio, blockiness, and end group, on the in vitro and in vivo performance of PLGA-based in situ forming implant formulations. Perseris (risperidone) for extended-release injectable suspension was selected as the reference listed drug (RLD). A previously developed adapter-based USP 2 method was used for the in vitro release testing of various risperidone implant formulations. A rabbit model was used to determine the in vivo pharmacokinetic profiles of the formulations (subcutaneous administration) and deconvolution (Loo-Riegelman method) was conducted to obtain the in vivo release profiles. The results showed that a 5 KDa difference in the MW (19.2, 24.2, 29.2 KDa), a 5% variation in the L/G ratio (85/15, 80/20, 75/25) and the end-cap (acid vs ester) all significantly impacted the formulation behavior both in vitro and in vivo. Higher MW, higher L/G ratio and ester end-cap PLGA all resulted in longer release durations. The formulations prepared with polymers with different blockiness values (within the blockiness range tested) did not show differences in in vitro and in vivo release. An in vitro-in vivo correlation (IVIVC) was not developed due to the different in vitro and in vivo phase separation rates, swelling tendencies and consequent significantly different release profiles. This is the first report evaluating the impact of PLGA property variation (over a narrow range) on the performance of in situ forming implants. The knowledge gained will provide a better understanding of the mechanisms underlying risperidone in situ forming implant performance and will aid the development of future products.
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Affiliation(s)
- Xiaoyi Wang
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Quanying Bao
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Ruifeng Wang
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Owen Kwok
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Kellen Maurus
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Yan Wang
- U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Bin Qin
- U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Diane J Burgess
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA.
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Kirchweger P, Mullick D, Swain PP, Wolf SG, Elbaum M. Correlating cryo-super resolution radial fluctuations and dual-axis cryo-scanning transmission electron tomography to bridge the light-electron resolution gap. J Struct Biol 2023; 215:107982. [PMID: 37268154 DOI: 10.1016/j.jsb.2023.107982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/05/2023] [Accepted: 05/24/2023] [Indexed: 06/04/2023]
Abstract
Visualization of organelles and their interactions with other features in the native cell remains a challenge in modern biology. We have introduced cryo-scanning transmission electron tomography (CSTET), which can access 3D volumes on the scale of 1 micron with a resolution of nanometers, making it ideal for this task. Here we introduce two relevant advances: (a) we demonstrate the utility of multi-color super-resolution radial fluctuation light microscopy under cryogenic conditions (cryo-SRRF), and (b) we extend the use of deconvolution processing for dual-axis CSTET data. We show that cryo-SRRF nanoscopy is able to reach resolutions in the range of 100 nm, using commonly available fluorophores and a conventional widefield microscope for cryo-correlative light-electron microscopy. Such resolution aids in precisely identifying regions of interest before tomographic acquisition and enhances precision in localizing features of interest within the 3D reconstruction. Dual-axis CSTET tilt series data and application of entropy regularized deconvolution during post-processing results in close-to-isotropic resolution in the reconstruction without averaging. The integration of cryo-SRRF with deconvolved dual-axis CSTET provides a versatile workflow for studying unique objects in a cell.
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Affiliation(s)
- Peter Kirchweger
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Debakshi Mullick
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Prabhu Prasad Swain
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel; School of Physical Sciences, UM-DAE Centre for Excellence in Basic Sciences, Mumbai 400098, India
| | - Sharon G Wolf
- Department of Chemical Research Support, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Michael Elbaum
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel.
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Niezen LE, Bos TS, Schoenmakers PJ, Somsen GW, Pirok BWJ. Capacitively coupled contactless conductivity detection to account for system-induced gradient deformation in liquid chromatography. Anal Chim Acta 2023; 1271:341466. [PMID: 37328247 DOI: 10.1016/j.aca.2023.341466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/12/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023]
Abstract
The time required for method development in gradient-elution liquid chromatography (LC) may be reduced by using an empirical modelling approach to describe and predict analyte retention and peak width. However, prediction accuracy is impaired by system-induced gradient deformation, which can be especially prominent for steep gradients. As the deformation is unique to each LC instrument, it needs to be corrected for if retention modelling for optimization and method transfer is to become generally applicable. Such a correction requires knowledge of the actual gradient profile. The latter has been measured using capacitively coupled "contactless" conductivity detection (C4D), featuring a low detection volume (approximately 0.05 μL) and compatibility with very high pressures (80 MPa or more). Several different solvent gradients, from water to acetonitrile, water to methanol, and acetonitrile to tetrahydrofuran, could be measured directly without the addition of a tracer component to the mobile phase, exemplifying the universal nature of the approach. Gradient profiles were found to be unique for each solvent combination, flowrate, and gradient duration. The profiles could be described by convoluting the programmed gradient with a weighted sum of two distribution functions. Knowledge of the exact profiles was used to improve the inter-system transferability of retention models for toluene, anthracene, phenol, emodin, sudan-I and several polystyrene standards.
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Affiliation(s)
- Leon E Niezen
- Analytical-Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands
| | - Tijmen S Bos
- Centre for Analytical Sciences Amsterdam (CASA), the Netherlands; Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands
| | - Peter J Schoenmakers
- Analytical-Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands
| | - Govert W Somsen
- Centre for Analytical Sciences Amsterdam (CASA), the Netherlands; Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands
| | - Bob W J Pirok
- Analytical-Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.
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Feng H, Meng G, Lin T, Parikh H, Pan Y, Li Z, Krischer J, Li Q. ISLET: individual-specific reference panel recovery improves cell-type-specific inference. Genome Biol 2023; 24:174. [PMID: 37496087 PMCID: PMC10373385 DOI: 10.1186/s13059-023-03014-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 07/12/2023] [Indexed: 07/28/2023] Open
Abstract
We propose a statistical framework ISLET to infer individual-specific and cell-type-specific transcriptome reference panels. ISLET models the repeatedly measured bulk gene expression data, to optimize the usage of shared information within each subject. ISLET is the first available method to achieve individual-specific reference estimation in repeated samples. Using simulation studies, we show outstanding performance of ISLET in the reference estimation and downstream cell-type-specific differentially expressed genes testing. We apply ISLET to longitudinal transcriptomes profiled from blood samples in a large observational study of young children and confirm the cell-type-specific gene signatures for pancreatic islet autoantibody. ISLET is available at https://bioconductor.org/packages/ISLET .
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Affiliation(s)
- Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Tong Lin
- Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA
| | - Hemang Parikh
- Health Informatics Institute, University of South Florida, Tampa, FL, 33620, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, FL, 33620, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
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Maié T, Schmidt M, Erz M, Wagner W, G Costa I. CimpleG: finding simple CpG methylation signatures. Genome Biol 2023; 24:161. [PMID: 37430364 DOI: 10.1186/s13059-023-03000-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 06/28/2023] [Indexed: 07/12/2023] Open
Abstract
DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. Here, we propose a computational framework named CimpleG for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. We show that CimpleG is both time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution.
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Affiliation(s)
- Tiago Maié
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Pauwelsstr. 19, Aachen, 52074, NRW, Germany.
| | - Marco Schmidt
- Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 19, Aachen, 52074, NRW, Germany
- Institute for Stem Cell Biology, RWTH Aachen University Medical School, Pauwelsstr. 19, Aachen, 52074, NRW, Germany
| | - Myriam Erz
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Pauwelsstr. 19, Aachen, 52074, NRW, Germany
| | - Wolfgang Wagner
- Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 19, Aachen, 52074, NRW, Germany
- Institute for Stem Cell Biology, RWTH Aachen University Medical School, Pauwelsstr. 19, Aachen, 52074, NRW, Germany
| | - Ivan G Costa
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Pauwelsstr. 19, Aachen, 52074, NRW, Germany.
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Canlet C, Deborde C, Cahoreau E, Da Costa G, Gautier R, Jacob D, Jousse C, Lacaze M, Le Mao I, Martineau E, Peyriga L, Richard T, Silvestre V, Traïkia M, Moing A, Giraudeau P. NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows. Metabolomics 2023; 19:65. [PMID: 37418094 DOI: 10.1007/s11306-023-02028-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
INTRODUCTION Absolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling. OBJECTIVES An inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision. METHODS A synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool. RESULTS For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables. CONCLUSION External calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools.
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Affiliation(s)
- Cécile Canlet
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Catherine Deborde
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France
| | - Edern Cahoreau
- TBI, Université de Toulouse, CNRS, INRAE, INSA, MetaboHUB - MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Grégory Da Costa
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | - Roselyne Gautier
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Daniel Jacob
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France
| | - Cyril Jousse
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut de Chimie de Clermont-Ferrand. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France
| | - Mélia Lacaze
- Toxalim (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, UPS, MetaToul-AXIOM Platform, National Infrastructure of Metabolomics and Fluxomics: MetaboHUB, INRAE, 31027, Toulouse, France
| | - Inès Le Mao
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | - Estelle Martineau
- Nantes Université, CNRS, CEISAM UMR 6230, 44000, Nantes, France
- CAPACITES SAS, 44200, Nantes, France
| | - Lindsay Peyriga
- TBI, Université de Toulouse, CNRS, INRAE, INSA, MetaboHUB - MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Tristan Richard
- Univ. Bordeaux, Bordeaux INP, INRAE, OENO, UMR 1366, ISVV, Bordeaux Metabolome - MetaboHUB, 33140, Villenave d'Ornon, France
| | | | - Mounir Traïkia
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut de Chimie de Clermont-Ferrand. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France
| | - Annick Moing
- INRAE, Univ. Bordeaux, Biologie du Fruit et Pathologie, UMR1332, Bordeaux Metabolome - MetaboHUB, Centre INRAE de Nouvelle-Aquitaine Bordeaux, 33140, Villenave d'Ornon, France.
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Akthar M, Nair N, Carter LM, Vital EM, Sutton E, McHugh N, Bruce IN, Reynolds JA. Deconvolution of whole blood transcriptomics identifies changes in immune cell composition in patients with systemic lupus erythematosus (SLE) treated with mycophenolate mofetil. Arthritis Res Ther 2023; 25:111. [PMID: 37391799 PMCID: PMC10311871 DOI: 10.1186/s13075-023-03089-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/09/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND Systemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease. We explored whether the deconvolution of whole blood transcriptomic data could identify differences in predicted immune cell frequency between active SLE patients, and whether these differences are associated with clinical features and/or medication use. METHODS Patients with active SLE (BILAG-2004 Index) enrolled in the BILAG-Biologics Registry (BILAG-BR), prior to change in therapy, were studied as part of the MASTERPLANS Stratified Medicine consortium. Whole blood RNA-sequencing (RNA-seq) was conducted at enrolment into the registry. Data were deconvoluted using CIBERSORTx. Predicted immune cell frequencies were compared between active and inactive disease in the nine BILAG-2004 domains and according to immunosuppressant use (current and past). RESULTS Predicted cell frequency varied between 109 patients. Patients currently, or previously, exposed to mycophenolate mofetil (MMF) had fewer inactivated macrophages (0.435% vs 1.391%, p = 0.001), naïve CD4 T cells (0.961% vs 2.251%, p = 0.002), and regulatory T cells (1.858% vs 3.574%, p = 0.007), as well as a higher proportion of memory activated CD4 T cells (1.826% vs 1.113%, p = 0.015), compared to patients never exposed to MMF. These differences remained statistically significant after adjusting for age, gender, ethnicity, disease duration, renal disease, and corticosteroid use. There were 2607 differentially expressed genes (DEGs) in patients exposed to MMF with over-representation of pathways relating to eosinophil function and erythrocyte development and function. Within CD4 + T cells, there were fewer predicted DEGs related to MMF exposure. No significant differences were observed for the other conventional immunosuppressants nor between patients according disease activity in any of the nine organ domains. CONCLUSION MMF has a significant and persisting effect on the whole blood transcriptomic signature in patients with SLE. This highlights the need to adequately adjust for background medication use in future studies using whole blood transcriptomics.
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Affiliation(s)
- Mumina Akthar
- Rheumatology Department, Sandwell and West Birmingham NHS Trust, Birmingham, UK
| | - Nisha Nair
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Lucy M Carter
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Edward M Vital
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Emily Sutton
- Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal & Dermatological Sciences, The University of Manchester, Manchester, UK
| | - Neil McHugh
- Department of Pharmacy and Pharmacology, University of Bath, Bath, UK
| | - Ian N Bruce
- Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal & Dermatological Sciences, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - John A Reynolds
- Rheumatology Department, Sandwell and West Birmingham NHS Trust, Birmingham, UK.
- Rheumatology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
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Fang C, Luo Y, Naidu R. Super-resolution imaging of micro- and nanoplastics using confocal Raman with Gaussian surface fitting and deconvolution. Talanta 2023; 265:124886. [PMID: 37392706 DOI: 10.1016/j.talanta.2023.124886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/14/2023] [Accepted: 06/25/2023] [Indexed: 07/03/2023]
Abstract
Confocal Raman imaging can directly identify and visualise microplastics and even nanoplastics. However, due to diffraction, the excitation laser spot has a size, which defines the image resolution. Consequently, it is difficult to image nanoplastic that is smaller than the diffraction limit. Within the laser spot, fortunately, the excitation energy density behaves an axially transcended distribution, or a 2D Gaussian distribution. By mapping the emission intensity of Raman signal, the imaged nanoplastic pattern is axially transcended as well and can be fitted as a 2D Gaussian surface via deconvolution, to re-construct the Raman image. The image re-construction can intentionally and selectively pick up the weak signal of nanoplastics, average the background noise/the variation of the Raman intensity, smoothen the image surface and re-focus the mapped pattern towards signal enhancement. Using this approach, along with nanoplastics models with known size for validation, real samples are also tested to image microplastics and nanoplastics released from the bushfire-burned face masks and water tanks. Even the bushfire-deviated surface group can be visualised as well, to monitor the different degrees of burning by visualising micro- and nanoplastics. Overall, this approach can effectively image regular shape of micro- and nanoplastics, capture nanoplastics smaller than the diffraction limit, and realise super-resolution imaging via confocal Raman.
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Affiliation(s)
- Cheng Fang
- Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, NSW, 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), University of Newcastle, Callaghan, NSW, 2308, Australia.
| | - Yunlong Luo
- Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, NSW, 2308, Australia
| | - Ravi Naidu
- Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, NSW, 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), University of Newcastle, Callaghan, NSW, 2308, Australia
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Merotto L, Zopoglou M, Zackl C, Finotello F. Next-generation deconvolution of transcriptomic data to investigate the tumor microenvironment. Int Rev Cell Mol Biol 2023; 382:103-143. [PMID: 38225101 DOI: 10.1016/bs.ircmb.2023.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Methods for in silico deconvolution of bulk transcriptomics can characterize the cellular composition of the tumor microenvironment, quantifying the abundance of cell types associated with patients' prognosis and response to therapy. While first-generation deconvolution methods rely on precomputed, transcriptional signatures of a handful of cell types, second-generation methods can be trained with single-cell data to disentangle more fine-grained cell phenotypes and states. These novel approaches can also be applied to spatial transcriptomic data to reveal the spatial organization of tumors. In this review, we describe state-of-the-art deconvolution methods (first-generation, second-generation, and spatial) which can be used to investigate the tumor microenvironment, discussing their strengths and limitations. We conclude with an outlook on the challenges that need to be overcome to unlock the full potential of next-generation deconvolution for oncology and the life sciences.
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Affiliation(s)
- Lorenzo Merotto
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Maria Zopoglou
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Constantin Zackl
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria
| | - Francesca Finotello
- Universität Innsbruck, Department of Molecular Biology, Digital Science Center (DiSC), Innsbruck, Austria.
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Toker L, Nido GS, Tzoulis C. Not every estimate counts - evaluation of cell composition estimation approaches in brain bulk tissue data. Genome Med 2023; 15:41. [PMID: 37287013 DOI: 10.1186/s13073-023-01195-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Variation in cell composition can dramatically impact analyses in bulk tissue samples. A commonly employed approach to mitigate this issue is to adjust statistical models using estimates of cell abundance derived directly from omics data. While an arsenal of estimation methods exists, the applicability of these methods to brain tissue data and whether or not cell estimates can sufficiently account for confounding cellular composition has not been adequately assessed. METHODS We assessed the correspondence between different estimation methods based on transcriptomic (RNA sequencing, RNA-seq) and epigenomic (DNA methylation and histone acetylation) data from brain tissue samples of 49 individuals. We further evaluated the impact of different estimation approaches on the analysis of H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-seq) data from entorhinal cortex of individuals with Alzheimer's disease and controls. RESULTS We show that even closely adjacent tissue samples from the same Brodmann area vary greatly in their cell composition. Comparison across different estimation methods indicates that while different estimation methods applied to the same data produce highly similar outcomes, there is a surprisingly low concordance between estimates based on different omics data modalities. Alarmingly, we show that cell type estimates may not always sufficiently account for confounding variation in cell composition. CONCLUSIONS Our work indicates that cell composition estimation or direct quantification in one tissue sample should not be used as a proxy to the cellular composition of another tissue sample from the same brain region of an individual-even if the samples are directly adjacent. The highly similar outcomes observed among vastly different estimation methods, highlight the need for brain benchmark datasets and better validation approaches. Finally, unless validated through complementary experiments, the interpretation of analyses outcomes based on data confounded by cell composition should be done with great caution, and ideally avoided all together.
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Affiliation(s)
- Lilah Toker
- Neuro-SysMed Center of Excellence, Department of Neurology, Department of Clinical Medicine, Haukeland University Hospital, University of Bergen, 5021, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Pb 7804, 5020, Bergen, Norway
- K.G Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway
| | - Gonzalo S Nido
- Neuro-SysMed Center of Excellence, Department of Neurology, Department of Clinical Medicine, Haukeland University Hospital, University of Bergen, 5021, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Pb 7804, 5020, Bergen, Norway
- K.G Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway
| | - Charalampos Tzoulis
- Neuro-SysMed Center of Excellence, Department of Neurology, Department of Clinical Medicine, Haukeland University Hospital, University of Bergen, 5021, Bergen, Norway.
- Department of Clinical Medicine, University of Bergen, Pb 7804, 5020, Bergen, Norway.
- K.G Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway.
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Shin EJ, Park S, Kang S, Kim J, Chang JH. Improving the quality of ultrasound images acquired using a therapeutic transducer. Ultrasonics 2023; 134:107063. [PMID: 37300907 DOI: 10.1016/j.ultras.2023.107063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/01/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023]
Abstract
To enhance the effectiveness and safety of focused ultrasound (FUS) therapy, ultrasound image-based guidance and treatment monitoring are crucial. However, the use of FUS transducers for both therapy and imaging is impractical due to their low spatial resolution, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). To address this issue, we propose a new method that significantly improve the quality of images obtained by a FUS transducer. The proposed method employs coded excitation to enhance SNR and Wiener deconvolution to solve the problem of low axial resolution resulting from the narrow spectral bandwidth of FUS transducers. Specifically, the method eliminates the impulse response of a FUS transducer from received ultrasound signals using Wiener deconvolution, and pulse compression is performed using a mismatched filter. Simulation and commercial phantom experiments confirmed that the proposed method significantly improves the quality of images acquired by the FUS transducer. The -6 dB axial resolution was improved 1.27 mm to 0.37 mm that was similar to the resolution achieved by the imaging transducer, i.e., 0.33 mm. SNR and CNR also increased from 16.5 dB and 0.69 to 29.1 dB and 3.03, respectively, that were also similar to those by the imaging transducer (27.8 dB and 3.16). Based on the results, we believe that the proposed method has great potential to enhance the clinical utility of FUS transducers in ultrasound image-guided therapy.
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Affiliation(s)
- Eui-Ji Shin
- Department of Electronic Engineering, Sogang University, Seoul, Korea
| | - Sunghun Park
- Department of Electronic Engineering, Sogang University, Seoul, Korea
| | - Sungwoo Kang
- Department of Electrical Engineering and Computer Science, DGIST (Daegu Gyeongbuk Institute of Science and Technology), Daegu, Korea
| | - Jinwoo Kim
- Department of Electrical Engineering and Computer Science, DGIST (Daegu Gyeongbuk Institute of Science and Technology), Daegu, Korea
| | - Jin Ho Chang
- Department of Electrical Engineering and Computer Science, DGIST (Daegu Gyeongbuk Institute of Science and Technology), Daegu, Korea.
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Govarthan PK, Ganapathy N, Agastinose Ronickom JF. Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning. Stud Health Technol Inform 2023; 302:73-77. [PMID: 37203612 DOI: 10.3233/shti230067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance. Decomposition analysis is used to deconvolve the EDA into slow and fast varying tonic and phasic activity, respectively. In this study, we used machine learning models to compare the performance of two EDA decomposition algorithms to detect emotions such as amusing, boring, relaxing, and scary. The EDA data considered in this study were obtained from the publicly available Continuously Annotated Signals of Emotion (CASE) dataset. Initially, we pre-processed and deconvolved the EDA data into tonic and phasic components using decomposition methods such as cvxEDA and BayesianEDA. Further, 12 time-domain features were extracted from the phasic component of EDA data. Finally, we applied machine learning algorithms such as logistic regression (LR) and support vector machine (SVM), to evaluate the performance of the decomposition method. Our results imply that the BayesianEDA decomposition method outperforms the cvxEDA. The mean of the first derivative feature discriminated all the considered emotional pairs with high statistical significance (p<0.05). SVM was able to detect emotions better than the LR classifier. We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision, and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively, using BayesianEDA and SVM classifiers. The proposed framework can be utilized to detect emotional states for the early diagnosis of psychological conditions.
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Affiliation(s)
- Praveen Kumar Govarthan
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India-221005
| | - Nagarajan Ganapathy
- Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana, India
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Bi H, Zhang Y, Wu B, Yuan D, Feng X, Shi Y. Study on reconstruction and analytical method of seawater radioactive gamma spectrum. Appl Radiat Isot 2023; 198:110853. [PMID: 37216724 DOI: 10.1016/j.apradiso.2023.110853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/16/2023] [Accepted: 05/11/2023] [Indexed: 05/24/2023]
Abstract
Gamma detector detection technology based on NaI(Tl) scintillation crystal has become a popular research topic and has been applied in the field of marine radioactive environment automatic monitoring because of its advantages of low power consumption, low cost and strong environmental adaptability. However, insufficient energy resolution of the NaI(Tl) detector and great Compton scattering in the low-energy region caused by the abundance of natural radionuclides in seawater hinder the automatic analysis of radionuclides in seawater. This study adopts the combination of theoretical derivation, simulation experiment, water tank test and seawater field test, establishing an effective and feasible spectrum reconstruction method. The measured spectrum in seawater is regarded as the output signal formed by the convolution of the incident spectrum and the detector response function. The acceleration factor p is introduced to construct the Boosted-WNNLS deconvolution algorithm, which is used to iteratively reconstruct the spectrum. The analysis results of the simulation test, water tank test and field test meet the radionuclide analysis speed and accuracy requirements for the in-situ automatic monitoring of seawater radioactivity. The spectrum reconstruction method in this study converts the physical problem of insufficient detection accuracy of spectrometer in the practical application into a mathematical problem of deconvolution solution, restores the original radiation information in seawater, and improves the resolution of the seawater gamma spectrum.
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Affiliation(s)
- Haijie Bi
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Techno1ogy, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061, Qingdao, China
| | - Yingying Zhang
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Techno1ogy, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061, Qingdao, China.
| | - Bingwei Wu
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Techno1ogy, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061, Qingdao, China
| | - Da Yuan
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Techno1ogy, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061, Qingdao, China.
| | - Xiandong Feng
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Techno1ogy, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061, Qingdao, China
| | - Yan Shi
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Techno1ogy, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061, Qingdao, China
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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. ArXiv 2023:arXiv:2305.06501v1. [PMID: 37214135 PMCID: PMC10197733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes. However, brain tissue or immune cell populations have cell types with substantially different cell sizes, total mRNA expression, and transcriptional activity. When existing deconvolution approaches are applied to these tissues, these systematic differences in cell sizes and transcriptomic activity confound accurate cell proportion estimates and instead may quantify total mRNA content. Furthermore, there is a lack of standard reference atlases and computational approaches to facilitate integrative analyses, including not only bulk and single cell/nuclei RNA-seq data, but also new data modalities from spatial -omic or imaging approaches. New multi-assay datasets need to be collected with orthogonal data types generated from the same tissue block and the same individual, to serve as a "gold standard" for evaluating new and existing deconvolution methods. Below, we discuss these key challenges and how they can be addressed with the acquisition of new datasets and approaches to analysis.
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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
| | | | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 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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Chen Y, Hunter E, Arbabi K, Guet-McCreight A, Consens M, Felsky D, Sibille E, Tripathy SJ. Robust differences in cortical cell type proportions across healthy human aging inferred through cross-dataset transcriptome analyses. Neurobiol Aging 2023; 125:49-61. [PMID: 36841202 DOI: 10.1016/j.neurobiolaging.2023.01.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 01/22/2023] [Accepted: 01/24/2023] [Indexed: 02/01/2023]
Abstract
Age-related declines in cognitive function are driven by cell type-specific changes in the brain. However, it remains challenging to study cellular differences associated with healthy aging as traditional approaches scale poorly to the sample sizes needed to capture aging and cellular heterogeneity. Here, we employed cellular deconvolution to estimate relative cell type proportions using frontal cortex bulk gene expression from individuals without psychiatric conditions or brain pathologies. Our analyses comprised 8 datasets and 6 cohorts (1142 subjects and 1429 samples) with ages of death spanning 15-90 years. We found aging associated with profound differences in cellular proportions, with the largest changes reflecting fewer somatostatin- and vasoactive intestinal peptide-expressing interneurons, more astrocytes and other non-neuronal cells, and a suggestive "U-shaped" quadratic relationship for microglia. Cell type associations with age were markedly robust across bulk-and single nucleus datasets. Altogether, we present a comprehensive account of proportional differences in cortical cell types associated with healthy aging.
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Affiliation(s)
- Yuxiao Chen
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Emma Hunter
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Keon Arbabi
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Alex Guet-McCreight
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Micaela Consens
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Daniel Felsky
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Shreejoy J Tripathy
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada.
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Saout JR, Lecuyer G, Léonard S, Evrard B, Kammerer-Jacquet SF, Noël L, Khene ZE, Mathieu R, Brunot A, Rolland AD, Bensalah K, Rioux-Leclercq N, Lardenois A, Chalmel F. Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients. Eur Urol 2023; 83:441-451. [PMID: 36801089 DOI: 10.1016/j.eururo.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/10/2022] [Accepted: 02/03/2023] [Indexed: 02/17/2023]
Abstract
BACKGROUND Intratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC. OBJECTIVE To explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients. DESIGN, SETTING, AND PARTICIPANTS We performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples. INTERVENTION Radical or partial nephrectomy on untreated ccRCC patients. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Viability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters. RESULTS AND LIMITATIONS We analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients. CONCLUSIONS We described eITH in ccRCCs, and used this information to establish significant cell population-based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management. PATIENT SUMMARY We sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.
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Affiliation(s)
- Judikael R Saout
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France
| | - Gwendoline Lecuyer
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France
| | - Simon Léonard
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France; LabEx IGO "Immunotherapy, Graft, Oncology", Nantes, France; INSERM, EFS, UMR S1236, Univ Rennes, Rennes, France
| | - Bertrand Evrard
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France
| | - Solène-Florence Kammerer-Jacquet
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France; Pathology Department, University Hospital of Rennes, Rennes, France
| | - Laurence Noël
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France
| | | | - Romain Mathieu
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France; Urology Department, University Hospital of Rennes, Rennes, France
| | - Angélique Brunot
- Department of Medical Oncology, Centre Eugène Marquis, Unicancer, Rennes, France
| | - Antoine D Rolland
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France
| | - Karim Bensalah
- Urology Department, University Hospital of Rennes, Rennes, France
| | - Nathalie Rioux-Leclercq
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France; Pathology Department, University Hospital of Rennes, Rennes, France
| | - Aurélie Lardenois
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France
| | - Frédéric Chalmel
- Inserm, EHESP, Univ Rennes, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.
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Nissen E, Reiner A, Liu S, Wallace RB, Molinaro AM, Salas LA, Christensen BC, Wiencke JK, Koestler DC, Kelsey KT. Assessment of immune cell profiles among post-menopausal women in the Women's Health Initiative using DNA methylation-based methods. Clin Epigenetics 2023; 15:69. [PMID: 37118842 PMCID: PMC10141818 DOI: 10.1186/s13148-023-01488-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 04/19/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND Over the past decade, DNA methylation (DNAm)-based deconvolution methods that leverage cell-specific DNAm markers of immune cell types have been developed to provide accurate estimates of the proportions of leukocytes in peripheral blood. Immune cell phenotyping using DNAm markers, termed immunomethylomics or methylation cytometry, offers a solution for determining the body's immune cell landscape that does not require fresh blood and is scalable to large sample sizes. Despite significant advances in DNAm-based deconvolution, references at the population level are needed for clinical and research interpretation of these additional immune layers. Here we aim to provide some references for immune populations in a group of multi-ethnic post-menopausal American women. RESULTS We applied DNAm-based deconvolution to a large sample of post-menopausal women enrolled in the Women's Health Initiative (baseline, N = 58) or the ancillary Long Life Study (WHI-LLS, N = 1237) to determine the reference ranges of 58 immune parameters, including proportions and absolute counts for 19 leukocyte subsets and 20 derived cell ratios. Participants were 50-94 years old at the time of blood draw, and N = 898 (69.3%) self-identified as White. Using linear regression models, we observed significant associations between age at blood draw and absolute counts and proportions of naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ memory, neutrophils, and natural killer cells. We also assessed the same immune profiles in a subset of paired longitudinal samples collected 14-18 years apart across N = 52 participants. Our results demonstrate high inter-individual variability in rates of change of leukocyte subsets over this time. And, when conducting paired t tests to test the difference in counts and proportions between the baseline visit and LLS visit, there were significant changes in naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ cells and neutrophils, similar to the results seen when analyzing the association with age in the entire cohort. CONCLUSIONS Here, we show that derived cell counts largely reflect the immune profile associated with proportions and that these novel methods replicate the known immune profiles associated with age. Further, we demonstrate the value this methylation cytometry approach can add as a potential application in epidemiological studies.
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Affiliation(s)
- Emily Nissen
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, USA
| | - Alexander Reiner
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Simin Liu
- Departments of Epidemiology, Medicine, and Surgery, Brown University, Providence, RI, USA
| | - Robert B Wallace
- Departments of Epidemiology and Internal Medicine, School of Public Health, University of Iowa, Iowa City, IA, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Devin C Koestler
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, USA
| | - Karl T Kelsey
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, 70 Ship St, Providence, RI, 02903, USA.
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Dessources K, Ferrando L, Zhou QC, Iasonos A, Abu-Rustum NR, Reis-Filho JS, Riaz N, Zamarin D, Weigelt B. Impact of immune infiltration signatures on prognosis in endometrial carcinoma is dependent on the underlying molecular subtype. Gynecol Oncol 2023; 171:15-22. [PMID: 36804617 PMCID: PMC10040428 DOI: 10.1016/j.ygyno.2023.01.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/04/2023] [Accepted: 01/30/2023] [Indexed: 02/18/2023]
Abstract
OBJECTIVES Increased numbers of tumor infiltrating lymphocytes (TIL) in endometrial cancer (EC) are associated with improved survival, but it is unclear how this prognostic significance relates to the underlying EC molecular subtype. In this explorative hypothesis-generating study, we sought to define the immune signatures associated with the molecular subtypes of EC (i.e., POLE-mutated, microsatellite unstable (MSI-high), copy number (CN)-low, and CN-high) and to determine their correlation with patient outcomes. METHODS RNA-sequencing and molecular subtype data of 232 primary ECs were obtained from The Cancer Genome Atlas. Deconvolution of bulk gene expression data was performed using single sample Gene Set Enrichment Analysis (ssGSEA) and Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT). The association of the resultant immune signatures with overall survival was determined across molecular subtypes. RESULTS Statistically significant differences in enrichment were identified in 16/30 and 6/23 immune gene sets by ssGSEA and CIBERSORT, respectively. Signature of CD8+ cells in ECs of CN-high molecular subtype was associated with improved overall survival by ssGSEA (p = 0.0108), while CD8 signatures did not appear to be prognostic in MSI-high (p = 0.74) or CN-low EC molecular subtypes (p = 0.793). Of all molecular subtypes, CN-high ECs exhibited the lowest levels of CD8+ T cell infiltration. Consistent with antigen-induced T cell activation and exhaustion, enrichment for immunomodulatory receptors was predominantly observed in ECs of MSI-high and POLE-mutated molecular subtypes. CONCLUSIONS Deconvolution of bulk gene expression data can be used to identify populations of immune infiltrated endometrial cancers with improved survival. These data support the existence of unique mechanisms of immune resistance within molecular subgroups of the disease.
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Affiliation(s)
- Kimberly Dessources
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lorenzo Ferrando
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Current address: IRCCS - Ospedale Policlinico San Martino, Genova, Italy
| | - Qin C Zhou
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexia Iasonos
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nadeem R Abu-Rustum
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jorge S Reis-Filho
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dmitriy Zamarin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, USA.
| | - Britta Weigelt
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Zhang S, Peng T, Ke Z, Yang H, Berendschot TTJM, Zhou J. Retinex-qDPC: Automatic background-rectified quantitative differential phase contrast imaging. Comput Methods Programs Biomed 2023; 230:107327. [PMID: 36610260 DOI: 10.1016/j.cmpb.2022.107327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/18/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE The quality of quantitative differential phase contrast reconstruction (qDPC) can be severely degenerated by the mismatch of the background of two oblique illuminated images, yielding problematic phase recovery results. These background mismatches may result from illumination patterns, inhomogeneous media distribution, or other defocusing layers. In previous reports, the background is manually calibrated which is time-consuming, and unstable, since new calibrations are needed if any modification to the optical system was made. It is also impossible to calibrate the background from the defocusing layers, or for high dynamic observation as the background changes over time. The background mismatch reduces the experimental robustness of qDPC and largely limits its applications. To tackle the mismatch of background and increases the experimental robustness, we propose the Retinex-qDPC. METHODS In Retinex-qDPC, we replace the data fidelity term of the previous cost function for qDPC inverse problem, by the images' edge features yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high background-robustness qDPC reconstruction. The split Bregman method is used to solve the L1-Retinex DPC. We compare both Retinex-qDPC models against state-of-the-art DPC reconstruction algorithms including total-variation regularized qDPC, and isotropic-qDPC using both simulated and experimental data. RESULTS Retinex qDPC can significantly improve the phase recovery quality by suppressing the impact of mismatch background. Within, the L1-Retinex-qDPC is better than L2-Retinex and other state-of-the-art qDPC algorithms. CONCLUSIONS The Retinex-qDPC increases the experimental robustness against background illumination without any modification of the optical system, which will benefit all qDPC applications.
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Affiliation(s)
- Shuhe Zhang
- School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China; University Eye Clinic Maastricht, Maastricht University Medical Center +, P.O. Box 5800, Maastricht, AZ 6202, the Netherlands.
| | - Tao Peng
- School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China
| | - Zeyu Ke
- School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China
| | - Han Yang
- School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China
| | - Tos T J M Berendschot
- University Eye Clinic Maastricht, Maastricht University Medical Center +, P.O. Box 5800, Maastricht, AZ 6202, the Netherlands
| | - Jinhua Zhou
- School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei 230032, China.
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47
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Schmid N, Bruderer S, Paruzzo F, Fischetti G, Toscano G, Graf D, Fey M, Henrici A, Ziebart V, Heitmann B, Grabner H, Wegner JD, Sigel RKO, Wilhelm D. Deconvolution of 1D NMR spectra: A deep learning-based approach. J Magn Reson 2023; 347:107357. [PMID: 36563418 DOI: 10.1016/j.jmr.2022.107357] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/01/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and characterize their parameters, often referred to as deconvolution, is a crucial step in the quantification, elucidation, and verification of the structure of molecular systems. However, deconvolution of 1D NMR spectra is a challenge for both experts and machines. We propose a robust, expert-level quality deep learning-based deconvolution algorithm for 1D experimental NMR spectra. The algorithm is based on a neural network trained on synthetic spectra. Our customized pre-processing and labeling of the synthetic spectra enable the estimation of critical peak parameters. Furthermore, the neural network model transfers well to the experimental spectra and demonstrates low fitting errors and sparse peak lists in challenging scenarios such as crowded, high dynamic range, shoulder peak regions as well as broad peaks. We demonstrate in challenging spectra that the proposed algorithm is superior to expert results.
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Affiliation(s)
- N Schmid
- Zurich University of Applied Sciences (ZHAW), Switzerland; University of Zurich (UZH), Switzerland.
| | | | | | | | | | - D Graf
- Bruker Switzerland AG, Switzerland
| | - M Fey
- Bruker Switzerland AG, Switzerland
| | - A Henrici
- Zurich University of Applied Sciences (ZHAW), Switzerland
| | - V Ziebart
- Zurich University of Applied Sciences (ZHAW), Switzerland
| | | | - H Grabner
- Zurich University of Applied Sciences (ZHAW), Switzerland
| | | | | | - D Wilhelm
- Zurich University of Applied Sciences (ZHAW), Switzerland
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48
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Huang A, Adler J, Parmryd I. Optimised generalized polarisation analysis of C-laurdan reveals clear order differences between T cell membrane compartments. Biochim Biophys Acta Biomembr 2023; 1865:184094. [PMID: 36379264 DOI: 10.1016/j.bbamem.2022.184094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/15/2022]
Abstract
Heterogenous packing of plasma membrane lipids is important for cellular processes like signalling, adhesion and sorting of membrane components. Solvatochromic membrane fluorophores that respond to changes from liquid-ordered (lo) phase to liquid-disordered (ld) by red shifts in their emission spectra are often used to assess lipid packing. Their response can be quantified using generalized polarisation (GP) using fluorescence microscopy images from two emission ranges, preferably from a region of interest (ROI) limited to a specific membrane compartment. However, image quality is limited by Poisson noise and convolution by the point spread function of the imaging system. Examining GP-analysis of C-laurdan labelled T cells using the image restoration procedure deconvolution, we demonstrate that deconvolution substantially improves the image resolution by making the plasma membrane clearly discernible and facilitating plasma membrane ROI selection. We conclude that automatic ROI selection has advantages over manual ROI selection when it comes to reproducibility and speed, but reliable GP-measurements can also be obtained by manually demarcated ROIs. We find that deconvolution enhances the difference in GP-values between the plasma and intracellular membranes and demonstrate that moving an intensity defined plasma membrane ROI outwards from the cell further improves this differentiation. By systematically changing the key deconvolution regularization parameter signal to noise, we establish a protocol for deconvolution optimisation applicable to any solvatochromic dye and imaging system. The image processing and ROI selection protocol presented improves both the resolution and precision of GP-measurement and will enable detection of smaller changes in membrane order than is currently achievable.
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Affiliation(s)
- Ainsley Huang
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jeremy Adler
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Ingela Parmryd
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
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49
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Kayikcioglu T, Amirzadegan J, Rand H, Tesfaldet B, Timme RE, Pettengill JB. Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples. PeerJ 2023; 11:e14596. [PMID: 36721781 PMCID: PMC9884472 DOI: 10.7717/peerj.14596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 11/29/2022] [Indexed: 01/27/2023] Open
Abstract
Background The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). Methods We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Results Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). Conclusion This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.
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Affiliation(s)
- Tunc Kayikcioglu
- Biostatistics and Bioinformatics Staff, Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, United States of America,Joint Institute for Food Safety and Applied Nutrition, University of Maryland College Park, College Park, MD, United States of America
| | - Jasmine Amirzadegan
- Biostatistics and Bioinformatics Staff, Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, United States of America,Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States of America
| | - Hugh Rand
- Biostatistics and Bioinformatics Staff, Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, United States of America
| | - Bereket Tesfaldet
- Biostatistics and Bioinformatics Staff, Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, United States of America
| | - Ruth E. Timme
- Division of Microbiology, Office of Regulatory Science, Center for Food Safety and Applied Nutrition, United States Food and Drug Administration, College Park, MD, United States of America
| | - James B. Pettengill
- Biostatistics and Bioinformatics Staff, Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, United States of America
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50
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Hasan MM, Camps J, Rogiers B, Laloy E, Rutten J, Boden S, Huysmans M. 2D inversion of in situ gamma-ray spectrometric measurements of 137Cs for site characterization. J Environ Radioact 2023; 256:107052. [PMID: 36308943 DOI: 10.1016/j.jenvrad.2022.107052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/12/2022] [Accepted: 10/16/2022] [Indexed: 06/16/2023]
Abstract
Environmental contamination by radioactive materials can be characterized by in situ gamma surface measurements. During such measurements, the field of view of a gamma detector can be tens of meters wide, resulting in a count rate that integrates the signal over a large measurement support volume/area. The contribution of a specific point to the signal depends on various parameters, such as the height of the detector above the ground surface, the gamma energy and the detector properties, etc. To improve the spatial resolution of the activity concentration, contributions of a radionuclide from nearby areas to the count rate of a single measurement should be disentangled. The experiments described in this paper, deployed 2D inversion of in situ gamma spectrometric measurements using a non-negative least squares-based Tikhonov regularization method. Data were acquired using a portable LaBr3 gamma detector. The detector response as a function of the distance of the radioactive source, required for the inversion process, was simulated using the Monte Carlo N-Particle (MCNP) transport code. The uncertainty on activity concentration was calculated using the Monte Carlo error propagation method. The 2D inversion methodology was first satisfactorily assessed for 133Ba and 137Cs source activity distributions using reference pads. Secondly, this method was applied on a 137Cs contaminated site, making use of above-ground in-situ gamma spectrometry measurements, conducted on a regular grid. The inversion process results were compared with the results from in-situ borehole measurements and laboratory analyses of soil samples. The calculated 137Cs activity concentration levels were compared against the activity concentration value for exemption or clearance of materials which can be applied by default to any amount and any type of solid material. Using the 2D inversion and the Monte Carlo error propagation method, a high spatial resolution classification of the site, in terms of exceeding the exemption limit, could be made. The 137Cs activity concentrations obtained using the inversion process agreed well with the results from the in-situ borehole measurements and those from the soil samples, showing that the 2D inversion is a convenient approach to deconvolute the contribution of radioactive sources from nearby areas within a detector's field of view, and increases the resolution of spatial contamination mapping.
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Affiliation(s)
- Md Moudud Hasan
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, BE-2400, Mol, Belgium; Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, BE-1050, Brussels, Belgium.
| | - Johan Camps
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, BE-2400, Mol, Belgium
| | - Bart Rogiers
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, BE-2400, Mol, Belgium
| | - Eric Laloy
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, BE-2400, Mol, Belgium
| | - Jos Rutten
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, BE-2400, Mol, Belgium
| | - Sven Boden
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, BE-2400, Mol, Belgium
| | - Marijke Huysmans
- Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, BE-1050, Brussels, Belgium
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