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Al Sultan A, Rattray Z, Rattray NJW. Cytotoxicity and toxicoproteomics analysis of thiazolidinedione exposure in human-derived cardiomyocytes. J Appl Toxicol 2024; 44:1214-1235. [PMID: 38654465 DOI: 10.1002/jat.4613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 03/16/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024]
Abstract
Thiazolidinediones (TZDs) (e.g. pioglitazone and rosiglitazone), known insulin sensitiser agents for type II diabetes mellitus, exhibit controversial effects on cardiac tissue. Despite consensus on their association with increased heart failure risk, limiting TZD use in diabetes management, the underlying mechanisms remain uncharacterised. Herein, we report a comprehensive in vitro investigation utilising a novel toxicoproteomics pipeline coupled with cytotoxicity assays in human adult cardiomyocytes to elucidate mechanistic insights into TZD cardiotoxicity. The cytotoxicity assay findings showed a significant loss of mitochondrial adenosine triphosphate production upon exposure to either TZD agents, which may underpin TZD cardiotoxicity. Our toxicoproteomics analysis revealed that mitochondrial dysfunction primarily stems from oxidative phosphorylation impairment, with distinct signalling mechanisms observed for both agents. The type of cell death differed strikingly between the two agents, with rosiglitazone exhibiting features of caspase-dependent apoptosis and pioglitazone implicating mitochondrial-mediated necroptosis, as evidenced by the protein upregulation in the phosphoglycerate mutase family 5-dynamin-related protein 1 axis. Furthermore, our analysis revealed additional mechanistic aspects of cardiotoxicity, showcasing drug specificity. The downregulation of various proteins involved in protein machinery and protein processing in the endoplasmic reticulum was observed in rosiglitazone-treated cells, implicating proteostasis in the rosiglitazone cardiotoxicity. Regarding pioglitazone, the findings suggested the potential activation of the interplay between the complement and coagulation systems and the disruption of the cytoskeletal architecture, which was primarily mediated through the integrin-signalling pathways responsible for pioglitazone-induced myocardial contractile failure. Collectively, this study unlocks substantial mechanistic insight into TZD cardiotoxicity, providing the rationale for future optimisation of antidiabetic therapies.
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Affiliation(s)
- Abdullah Al Sultan
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
- Faculty of Pharmacy, Kuwait University, Safat, Kuwait
| | - Zahra Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Nicholas J W Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
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2
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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Ruan X, Cheng Y, Ye Y, Wang Y, Chen X, Yang Y, Liu T, Yan F. PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data. Brief Bioinform 2024; 25:bbae260. [PMID: 38819254 PMCID: PMC11141296 DOI: 10.1093/bib/bbae260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/11/2024] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.
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Affiliation(s)
- Xinjia Ruan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yu Cheng
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuqing Ye
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuhang Wang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Xinyi Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuqing Yang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Tiantian Liu
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
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Shu C, Street K, Breton CV, Bastain TM, Wilson ML. A review of single-cell transcriptomics and epigenomics studies in maternal and child health. Epigenomics 2024; 16:775-793. [PMID: 38709139 PMCID: PMC11318716 DOI: 10.1080/17501911.2024.2343276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.
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Affiliation(s)
- Chang Shu
- Center for Genetic Epidemiology, Division of Epidemiology & Genetics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Kelly Street
- Division of Biostatistics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Carrie V Breton
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Theresa M Bastain
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Melissa L Wilson
- Division of Disease Prevention, Policy, & Global Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles,CA USA
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Wang H, Liu Z, Ma X. Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data. IEEE J Biomed Health Inform 2024; 28:3134-3145. [PMID: 38709615 DOI: 10.1109/jbhi.2024.3370868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.
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Chen G, Shi Y, Xiao W, Kreil DP. Editorial: Comprehensive profiling cancer immunity with multimodal approaches for clinical management. Front Immunol 2024; 15:1421576. [PMID: 38745672 PMCID: PMC11091410 DOI: 10.3389/fimmu.2024.1421576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024] Open
Affiliation(s)
- Geng Chen
- School of Life Sciences, East China Normal University, Shanghai, China
| | - Yi Shi
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China
| | - Wenming Xiao
- The Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - David P. Kreil
- Department of Biotechnology, Boku University Vienna, Vienna, Austria
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Al Sultan A, Rattray Z, Rattray NJW. Toxicometabolomics-based cardiotoxicity evaluation of Thiazolidinedione exposure in human-derived cardiomyocytes. Metabolomics 2024; 20:24. [PMID: 38393619 PMCID: PMC10891199 DOI: 10.1007/s11306-024-02097-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024]
Abstract
INTRODUCTION Thiazolidinediones (TZDs), represented by pioglitazone and rosiglitazone, are a class of cost-effective oral antidiabetic agents posing a marginal hypoglycaemia risk. Nevertheless, observations of heart failure have hindered the clinical use of both therapies. OBJECTIVE Since the mechanism of TZD-induced heart failure remains largely uncharacterised, this study aimed to explore the as-yet-unidentified mechanisms underpinning TZD cardiotoxicity using a toxicometabolomics approach. METHODS The present investigation included an untargeted liquid chromatography-mass spectrometry-based toxicometabolomics pipeline, followed by multivariate statistics and pathway analyses to elucidate the mechanism(s)of TZD-induced cardiotoxicity using AC16 human cardiomyocytes as a model, and to identify the prognostic features associated with such effects. RESULTS Acute administration of either TZD agent resulted in a significant modulation in carnitine content, reflecting potential disruption of the mitochondrial carnitine shuttle. Furthermore, perturbations were noted in purine metabolism and amino acid fingerprints, strongly conveying aberrations in cardiac energetics associated with TZD usage. Analysis of our findings also highlighted alterations in polyamine (spermine and spermidine) and amino acid (L-tyrosine and valine) metabolism, known modulators of cardiac hypertrophy, suggesting a potential link to TZD cardiotoxicity that necessitates further research. In addition, this comprehensive study identified two groupings - (i) valine and creatine, and (ii) L-tryptophan and L-methionine - that were significantly enriched in the above-mentioned mechanisms, emerging as potential fingerprint biomarkers for pioglitazone and rosiglitazone cardiotoxicity, respectively. CONCLUSION These findings demonstrate the utility of toxicometabolomics in elaborating on mechanisms of drug toxicity and identifying potential biomarkers, thus encouraging its application in the toxicological sciences. (245 words).
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Affiliation(s)
- Abdullah Al Sultan
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow, G4 0RE, UK
- Faculty of Pharmacy, Kuwait University, Safat, 13110, Kuwait
| | - Zahra Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow, G4 0RE, UK
| | - Nicholas J W Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow, G4 0RE, UK.
- Strathclyde Centre for Molecular Bioscience, University of Strathclyde, Glasgow, G4 0RE, UK.
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Li J, Wu YJ, Liu MF, Li N, Dang LH, An GS, Lu XJ, Wang LL, Du QX, Cao J, Sun JH. Multi-omics integration strategy in the post-mortem interval of forensic science. Talanta 2024; 268:125249. [PMID: 37839320 DOI: 10.1016/j.talanta.2023.125249] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/13/2023] [Accepted: 09/25/2023] [Indexed: 10/17/2023]
Abstract
Estimates of post-mortem interval (PMI), which often serve as pivotal evidence in forensic contexts, are fundamentally based on assessments of variability among diverse molecular markers (including proteins and metabolites), their correlations, and their temporal changes in post-mortem organisms. Nevertheless, the present approach to estimating the PMI is not comprehensive and exhibits poor performance. We developed an innovative approach that integrates multi-omics and artificial intelligence, using multimolecular, multimarker, and multidimensional information to accurately describe the intricate biological processes that occur after death, ultimately enabling inference of the PMI. Called the multi-omics stacking model (MOSM), it combines metabolomics, protein microarray electrophoresis, and fourier transform-infrared spectroscopy data. It shows improved prediction accuracy of the PMI, which is urgently needed in the forensic field. It achieved an accuracy of 0.93, generalized area under the receiver operating characteristic curve of 0.98, and minimum mean absolute error of 0.07. The MOSM integration framework not only considers multiple markers but also incorporates machine-learning models with distinct algorithmic principles. The diversity of biological mechanisms and algorithmic models further ensures the generalizability and robustness of PMI estimation.
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Affiliation(s)
- Jian Li
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China
| | - Yan-Juan Wu
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China
| | - Ming-Feng Liu
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China
| | - Na Li
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China
| | - Li-Hong Dang
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China
| | - Guo-Shuai An
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China
| | - Xiao-Jun Lu
- Criminal Investigation Detachment, Baotou City Public Security Bureau, No. 191, Jianshe Road, Qingshan District, Baotou City, Inner Mongolia Autonomous Region, 014030, PR China
| | - Liang-Liang Wang
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China
| | - Qiu-Xiang Du
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China
| | - Jie Cao
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China.
| | - Jun-Hong Sun
- School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China.
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Ji Q, Jiang X, Wang M, Xin Z, Zhang W, Qu J, Liu GH. Multimodal Omics Approaches to Aging and Age-Related Diseases. PHENOMICS (CHAM, SWITZERLAND) 2024; 4:56-71. [PMID: 38605908 PMCID: PMC11003952 DOI: 10.1007/s43657-023-00125-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/09/2023] [Accepted: 08/16/2023] [Indexed: 04/13/2024]
Abstract
Aging is associated with a progressive decline in physiological capacities and an increased risk of aging-associated disorders. An increasing body of experimental evidence shows that aging is a complex biological process coordinately regulated by multiple factors at different molecular layers. Thus, it is difficult to delineate the overall systematic aging changes based on single-layer data. Instead, multimodal omics approaches, in which data are acquired and analyzed using complementary omics technologies, such as genomics, transcriptomics, and epigenomics, are needed for gaining insights into the precise molecular regulatory mechanisms that trigger aging. In recent years, multimodal omics sequencing technologies that can reveal complex regulatory networks and specific phenotypic changes have been developed and widely applied to decode aging and age-related diseases. This review summarizes the classification and progress of multimodal omics approaches, as well as the rapidly growing number of articles reporting on their application in the field of aging research, and outlines new developments in the clinical treatment of age-related diseases based on omics technologies.
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Affiliation(s)
- Qianzhao Ji
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Xiaoyu Jiang
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Minxian Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zijuan Xin
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100190 China
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100190 China
- Advanced Innovation Center for Human Brain Protection, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053 China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
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10
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Singh B, Jevnikar AM, Desjardins E. Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities. Arch Immunol Ther Exp (Warsz) 2024; 72:aite-2024-0006. [PMID: 38421272 DOI: 10.2478/aite-2024-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.
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Affiliation(s)
- Bhagirath Singh
- Department of Microbiology and Immunology, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Rotman Institute of Philosophy, University of Western Ontario, London, ON, Canada
| | - Anthony M Jevnikar
- Department of Microbiology and Immunology, University of Western Ontario, London, ON, Canada
- Department of Medicine, University of Western Ontario, London, ON, Canada
| | - Eric Desjardins
- Rotman Institute of Philosophy, University of Western Ontario, London, ON, Canada
- Department of Philosophy, University of Western Ontario, London, ON, Canada
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11
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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12
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Cembrowska-Lech D, Krzemińska A, Miller T, Nowakowska A, Adamski C, Radaczyńska M, Mikiciuk G, Mikiciuk M. An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture. BIOLOGY 2023; 12:1298. [PMID: 37887008 PMCID: PMC10603917 DOI: 10.3390/biology12101298] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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Affiliation(s)
- Danuta Cembrowska-Lech
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
| | - Adrianna Krzemińska
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | - Tymoteusz Miller
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
| | - Anna Nowakowska
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
| | - Cezary Adamski
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | | | - Grzegorz Mikiciuk
- Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Małgorzata Mikiciuk
- Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
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13
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Mar D, Babenko IM, Zhang R, Noble WS, Denisenko O, Vaisar T, Bomsztyk K. MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.533031. [PMID: 36993219 PMCID: PMC10055122 DOI: 10.1101/2023.03.16.533031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Background The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or "omes," measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies. Methods Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LC-MS/MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression. Results MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LC-MS/MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually. Conclusions The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies - for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.
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Single-cell RNA-seq Reveals Intratumoral Heterogeneity in Osteosarcoma Patients: A Review. J Bone Oncol 2023; 39:100475. [PMID: 37034356 PMCID: PMC10074210 DOI: 10.1016/j.jbo.2023.100475] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/08/2023] [Accepted: 03/15/2023] [Indexed: 03/22/2023] Open
Abstract
While primary bone malignancies make up just 0.2% of all cancers, osteosarcoma (OS) is the third most common cancer in adolescents. Due to its highly complex and heterogeneous tumor microenvironment (TME), OS has proven difficult to treat. There has been little to no improvement in therapy for this disease over the last 40 years. Even the recent success of immunotherapies in other blood-borne and solid malignancies has not translated to OS. With frequent recurrence and lung metastases continuing to pose a challenge in the clinic, recent advancements in molecular profiling, such as single-cell RNA sequencing (scRNA-seq), have proven useful in identifying novel biomarkers of OS tumors while providing new insight into this TME that could potentially lead to new therapeutic options. This review combines the analyses of over 150,000 cells from 18 lesions ranging from primary, recurrent, and metastatic OS lesions, revealing distinct cellular populations and gene signatures that exist between them. Here, we detail these previous findings and ultimately convey the intratumoral heterogeneity that exists within OS tumor specimens.
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15
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Tan P, Chen X, Zhang H, Wei Q, Luo K. Artificial intelligence aids in development of nanomedicines for cancer management. Semin Cancer Biol 2023; 89:61-75. [PMID: 36682438 DOI: 10.1016/j.semcancer.2023.01.005] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/28/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023]
Abstract
Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.
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Affiliation(s)
- Ping Tan
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaoting Chen
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hu Zhang
- Amgen Bioprocessing Centre, Keck Graduate Institute, Claremont, CA 91711, USA
| | - Qiang Wei
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Kui Luo
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
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16
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Kong S, Li R, Tian Y, Zhang Y, Lu Y, Ou Q, Gao P, Li K, Zhang Y. Single-cell omics: A new direction for functional genetic research in human diseases and animal models. Front Genet 2023; 13:1100016. [PMID: 36685871 PMCID: PMC9846559 DOI: 10.3389/fgene.2022.1100016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023] Open
Abstract
Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.
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Affiliation(s)
- Siyuan Kong
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,*Correspondence: Siyuan Kong, ; Yubo Zhang,
| | - Rongrong Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yunhan Tian
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| | - Yaqiu Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yuhui Lu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Qiaoer Ou
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Peiwen Gao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yubo Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,College of Life Science and Engineering, Foshan University, Foshan, China,*Correspondence: Siyuan Kong, ; Yubo Zhang,
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Chen G, Yu R, Chen X. Editorial: Integrative analysis of single-cell and/or bulk multi-omics sequencing data. Front Genet 2023; 13:1121999. [PMID: 36685891 PMCID: PMC9845394 DOI: 10.3389/fgene.2022.1121999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 01/05/2023] Open
Affiliation(s)
- Geng Chen
- Stemirna Therapeutics Co., Ltd., Shanghai, China,*Correspondence: Geng Chen,
| | - Rongshan Yu
- Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
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18
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Martini L, Bardini R, Savino A, Di Carlo S. GAGAM v1.2: An Improvement on Peak Labeling and Genomic Annotated Gene Activity Matrix Construction. Genes (Basel) 2022; 14:genes14010115. [PMID: 36672856 PMCID: PMC9858924 DOI: 10.3390/genes14010115] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/20/2022] [Accepted: 12/24/2022] [Indexed: 01/03/2023] Open
Abstract
Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) is rapidly becoming a powerful technology for assessing the epigenetic landscape of thousands of cells. However, the sparsity of the resulting data poses significant challenges to their interpretability and informativeness. Different computational methods are available, proposing ways to generate significant features from accessibility data and process them to obtain meaningful results. Foremost among them is the peak calling, which interprets the raw scATAC-seq data generating the peaks as features. However, scATAC-seq data are not trivially comparable with single-cell RNA sequencing (scRNA-seq) data, an increasingly pressing challenge since the necessity of multimodal experiments integration. For this reason, this study wants to improve the concept of the Gene Activity Matrix (GAM), which links the accessibility data to the genes, by proposing an improved version of the Genomic-Annotated Gene Activity Matrix (GAGAM) concept. Specifically, this paper presents GAGAM v1.2, a new and better version of GAGAM v1.0. GAGAM aims to label the peaks and link them to the genes through functional annotation of the whole genome. Using genes as features in scATAC-seq datasets makes different datasets comparable and allows linking gene accessibility and expression. This link is crucial for gene regulation understanding and fundamental for the increasing impact of multi-omics data. Results confirm that our method performs better than the previous GAMs and shows a preliminary comparison with scRNA-seq data.
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Zhang Q, Jin S, Zou X. scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data. Nucleic Acids Res 2022; 50:12112-12130. [PMID: 36440766 PMCID: PMC9757078 DOI: 10.1093/nar/gkac1109] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/31/2022] [Accepted: 11/05/2022] [Indexed: 11/29/2022] Open
Abstract
Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger poor-survival association. The identified fine-grain cell subsets are associated with distinct cancer hallmarks and prognosis power. Furthermore, scAB demonstrates its utility as a biomarker identification tool, with the ability to predict immunotherapy, drug responses and survival when applied to melanoma single-cell RNA-seq datasets and glioma single-cell ATAC-seq datasets. Across multiple single-cell and bulk datasets from different cancer types, we also demonstrate the superior performance of scAB in generating prognosis signatures and survival predictions over existing models. Overall, scAB provides an efficient tool for prioritizing clinically-relevant cell subsets and predictive signatures, utilizing large publicly available databases to improve prognosis and treatments.
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Affiliation(s)
- Qinran Zhang
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China,Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan 430072, China
| | - Suoqin Jin
- To whom correspondence should be addressed. Tel: +86 027 68752957; Fax: +86 027 68752256;
| | - Xiufen Zou
- Correspondence may also be addressed to Xiufen Zou. Tel: +86 027 68752957; Fax: +86 027 68752256;
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Chen G, Xie L, Zhao F, Kreil DP. Editorial: The application of sequencing technologies and bioinformatics methods in cancer biology. Front Cell Dev Biol 2022; 10:1002813. [PMID: 36147744 PMCID: PMC9486313 DOI: 10.3389/fcell.2022.1002813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Geng Chen
- Stemirna Therapeutics Co., Ltd., Shanghai, China
- *Correspondence: Geng Chen,
| | - Lu Xie
- Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Fangqing Zhao
- Computational Genomics Laboratory, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
| | - David P. Kreil
- Department of Biotechnology, Boku University Vienna, Vienna, Austria
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21
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Carraro C, Bonaguro L, Schulte-Schrepping J, Horne A, Oestreich M, Warnat-Herresthal S, Helbing T, De Franco M, Haendler K, Mukherjee S, Ulas T, Gandin V, Goettlich R, Aschenbrenner AC, Schultze JL, Gatto B. Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state. eLife 2022; 11:e78012. [PMID: 36043458 PMCID: PMC9433094 DOI: 10.7554/elife.78012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines, a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Furthermore, we implemented a new versatile strategy for the integration of RNA- and ATAC-seq (Assay for Transposase-Accessible Chromatin) data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature predicting cancer cell lines' sensitivity and to further direct compound development against specific tumor types. Overall, this approach offers a scalable pipeline to support the early phases of drug discovery, understanding of mechanisms, and potentially inform the positioning of therapeutics in the clinic.
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Affiliation(s)
- Caterina Carraro
- Department of Pharmaceutical and Pharmacological Sciences, University of PadovaPadovaItaly
| | - Lorenzo Bonaguro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
| | - Jonas Schulte-Schrepping
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
| | - Arik Horne
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
| | - Marie Oestreich
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
| | - Stefanie Warnat-Herresthal
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
| | - Tim Helbing
- Institute of Organic Chemistry, Justus Liebig University GiessenGiessenGermany
| | - Michele De Franco
- Department of Pharmaceutical and Pharmacological Sciences, University of PadovaPadovaItaly
| | - Kristian Haendler
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- PRECISE Platform for Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V. and University of BonnBonnGermany
- Institute of Human Genetics, University of LübeckLübeckGermany
| | - Sach Mukherjee
- Statistics and Machine Learning, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- MRC Biostatistics Unit, University of CambridgeCambridgeUnited Kingdom
| | - Thomas Ulas
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
- PRECISE Platform for Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V. and University of BonnBonnGermany
| | - Valentina Gandin
- Department of Pharmaceutical and Pharmacological Sciences, University of PadovaPadovaItaly
| | - Richard Goettlich
- Institute of Organic Chemistry, Justus Liebig University GiessenGiessenGermany
| | - Anna C Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
- PRECISE Platform for Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V. and University of BonnBonnGermany
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical CenterNijmegenNetherlands
| | - Joachim L Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
- PRECISE Platform for Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V. and University of BonnBonnGermany
| | - Barbara Gatto
- Department of Pharmaceutical and Pharmacological Sciences, University of PadovaPadovaItaly
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Zhang Y, Sun H, Mandava A, Aevermann BD, Kollmann TR, Scheuermann RH, Qiu X, Qian Y. FastMix: a versatile data integration pipeline for cell type-specific biomarker inference. Bioinformatics 2022; 38:4735-4744. [PMID: 36018232 PMCID: PMC9801972 DOI: 10.1093/bioinformatics/btac585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 08/18/2022] [Accepted: 08/25/2022] [Indexed: 01/07/2023] Open
Abstract
MOTIVATION Flow cytometry (FCM) and transcription profiling are the two widely used assays in translational immunology research. However, there is no data integration pipeline for analyzing these two types of assays together with experiment variables for biomarker inference. Current FCM data analysis mainly relies on subjective manual gating analysis, which is difficult to be directly integrated with other automated computational methods. Existing deconvolutional analysis of bulk transcriptomics relies on predefined marker genes in the transcriptomics data, which are unavailable for novel cell types and does not utilize the FCM data that provide canonical phenotypic definitions of the cell types. RESULTS We developed a novel analytics pipeline-FastMix-for computational immunology, which integrates flow cytometry, bulk transcriptomics and clinical covariates for identifying cell type-specific gene expression signatures and biomarker genes. FastMix addresses the 'large p, small n' problem in the gene expression and flow cytometry integration analysis via a linear mixed effects model (LMER) for both cross-sectional and longitudinal studies. Its novel moment-based estimator not only reduces bias in parameter estimation but also is more efficient than iterative optimization. The FastMix pipeline also includes a cutting-edge flow cytometry data analysis method-DAFi-for identifying cell populations of interest and their characteristics. Simulation studies showed that FastMix produced smaller type I/II errors than competing methods. Validation using real data of two vaccine studies showed that FastMix identified a consistent set of signature genes as in independent single-cell RNA-seq analysis, producing additional interesting findings. AVAILABILITY AND IMPLEMENTATION Source code of FastMix is publicly available at https://github.com/terrysun0302/FastMix. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Aishwarya Mandava
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Brian D Aevermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Tobias R Kollmann
- Systems Vaccinology, Telethon Kids Institute, Perth Children’s Hospital, University of Western Australia, Nedlands, WA 6009, Australia
| | - Richard H Scheuermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, USA,Department of Pathology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xing Qiu
- To whom correspondence should be addressed. or
| | - Yu Qian
- To whom correspondence should be addressed. or
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23
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Schaffner SL, Kobor MS. DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome. Front Genet 2022; 13:971298. [PMID: 36061205 PMCID: PMC9437223 DOI: 10.3389/fgene.2022.971298] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/25/2022] [Indexed: 12/15/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.
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Affiliation(s)
- Samantha L. Schaffner
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Michael S. Kobor
- Edwin S. H. Leong Healthy Aging Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, British Columbia Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
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24
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Abondio P, De Intinis C, da Silva Gonçalves Vianez Júnior JL, Pace L. SINGLE CELL MULTIOMIC APPROACHES TO DISENTANGLE T CELL HETEROGENEITY. Immunol Lett 2022; 246:37-51. [DOI: 10.1016/j.imlet.2022.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/16/2022] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
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25
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Huo L, Jiao Li J, Chen L, Yu Z, Hutvagner G, Li J. Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects. Brief Bioinform 2021; 22:bbab229. [PMID: 34111889 PMCID: PMC8344433 DOI: 10.1093/bib/bbab229] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/23/2021] [Accepted: 05/25/2021] [Indexed: 01/19/2023] Open
Abstract
Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.
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Affiliation(s)
- Lu Huo
- Data Science Institute, University of Technology Sydney, Ultimo, NSW 2007, Australia
- School of Computer Science, FEIT, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Jiao Jiao Li
- School of Biomedical Engineering, FEIT, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Ling Chen
- School of Computer Science, FEIT, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Zuguo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105, P.R. China
| | - Gyorgy Hutvagner
- School of Biomedical Engineering, FEIT, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Jinyan Li
- Data Science Institute, University of Technology Sydney, Ultimo, NSW 2007, Australia
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26
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Miao Z, Humphreys BD, McMahon AP, Kim J. Multi-omics integration in the age of million single-cell data. Nat Rev Nephrol 2021; 17:710-724. [PMID: 34417589 PMCID: PMC9191639 DOI: 10.1038/s41581-021-00463-x] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2021] [Indexed: 02/06/2023]
Abstract
An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.
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Affiliation(s)
- Zhen Miao
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Andrew P McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Junhyong Kim
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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