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Yu B, Ma W. Biomarker discovery in hepatocellular carcinoma (HCC) for personalized treatment and enhanced prognosis. Cytokine Growth Factor Rev 2024; 79:29-38. [PMID: 39191624 DOI: 10.1016/j.cytogfr.2024.08.006] [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: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 08/29/2024]
Abstract
Hepatocellular carcinoma (HCC) is a leading contributor to cancer-related deaths worldwide and presents significant challenges in diagnosis and treatment due to its heterogeneous nature. The discovery of biomarkers has become crucial in addressing these challenges, promising early detection, precise diagnosis, and personalized treatment plans. Key biomarkers, such as alpha fetoprotein (AFP) glypican 3 (GPC3) and des gamma carboxy prothrombin (DCP) have shown potential in improving clinical results. Progress in proteomic technologies, including next-generation sequencing (NGS), mass spectrometry, and liquid biopsies detecting circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), has deepened our understanding of HCC's molecular landscape. Immunological markers, like PD-L1 expression and tumor-infiltrating lymphocytes (TILs), also play a crucial role in guiding immunotherapy decisions. Despite these advancements, challenges remain in biomarker validation, standardization, integration into clinical practice, and cost-related barriers. Emerging technologies like single-cell sequencing and machine learning offer promising avenues for further exploration. Continued investment in research and collaboration among researchers, healthcare providers, and policymakers is vital to harness the potential of biomarkers fully, ultimately revolutionizing HCC management and improving patient outcomes through personalized treatment approaches.
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Affiliation(s)
- Baofa Yu
- Taimei Baofa Cancer Hospital, Dongping, Shandong 271500, China; Jinan Baofa Cancer Hospital, Jinan, Shandong 250000, China; Beijing Baofa Cancer Hospital, Beijing, 100010, China; Immune Oncology Systems, Inc, San Diego, CA 92102, USA.
| | - Wenxue Ma
- Department of Medicine, Sanford Stem Cell Institute, and Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA.
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Xu J, Yu B, Wang F, Yang J. Single-cell RNA sequencing to map tumor heterogeneity in gastric carcinogenesis paving roads to individualized therapy. Cancer Immunol Immunother 2024; 73:233. [PMID: 39271545 DOI: 10.1007/s00262-024-03820-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 08/27/2024] [Indexed: 09/15/2024]
Abstract
Gastric cancer (GC) is a highly heterogeneous disease with a complex tumor microenvironment (TME) that encompasses multiple cell types including cancer cells, immune cells, stromal cells, and so on. Cancer-associated cells could remodel the TME and influence the progression of GC and therapeutic response. Single-cell RNA sequencing (scRNA-seq), as an emerging technology, has provided unprecedented insights into the complicated biological composition and characteristics of TME at the molecular, cellular, and immunological resolutions, offering a new idea for GC studies. In this review, we discuss the novel findings from scRNA-seq datasets revealing the origin and evolution of GC, and scRNA-seq is a powerful tool for investigating transcriptional dynamics and intratumor heterogeneity (ITH) in GC. Meanwhile, we demonstrate that the vital immune cells within TME, including T cells, B cells, macrophages, and stromal cells, play an important role in the disease progression. Additionally, we also overview that how scRNA-seq facilitates our understanding about the effects on individualized therapy of GC patients. Spatial transcriptomes (ST) have been designed to determine spatial distribution and capture local intercellular communication networks, enabling a further understanding of the relationship between the spatial background of a particular cell and its functions. In summary, scRNA-seq and other single-cell technologies provide a valuable perspective for molecular and pathological disease characteristics and hold promise for advancing basic research and clinical practice in GC.
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Affiliation(s)
- Jiao Xu
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 West Yanta Road., Xi'an, 710061, Shaanxi, People's Republic of China
| | - Bixin Yu
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 West Yanta Road., Xi'an, 710061, Shaanxi, People's Republic of China
| | - Fan Wang
- Phase I Clinical Trial Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, People's Republic of China.
| | - Jin Yang
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 West Yanta Road., Xi'an, 710061, Shaanxi, People's Republic of China.
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 West Yanta Road., Xi'an, 710061, Shaanxi, People's Republic of China.
- Phase I Clinical Trial Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, People's Republic of China.
- Cancer Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, People's Republic of China.
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3
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Chen Y, Deng X, Li Y, Han Y, Peng Y, Wu W, Wang X, Ma J, Hu E, Zhou X, Shen E, Zeng S, Cai C, Qin Y, Shen H. Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC. Hepatology 2024; 80:536-551. [PMID: 38537130 PMCID: PMC11332383 DOI: 10.1097/hep.0000000000000869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 02/07/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND AND AIMS Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for patients with HCC. The clinical applications of documented molecular subtypes are constrained by several issues. APPROACH AND RESULTS We integrated 3 single-cell data sets to describe the TME landscape and identified 6 prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry, and multiplex immunofluorescence. The prognosis-related score was constructed based on a machine-learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The 5 transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes, and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while classes 2 and 4 were characterized by a lack of T-cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that classes 5 and 3 were sensitive to immune checkpoint blockade and targeted therapy, whereas classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistance to all conventional HCC therapies. Four potential therapeutic agents and 4 targets were further identified for high prognosis-related score patients with HCC. CONCLUSIONS Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.
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Affiliation(s)
- Yihong Chen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiangying Deng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yin Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ying Han
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yinghui Peng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wantao Wu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xinwen Wang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jiayao Ma
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Erya Hu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xin Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Edward Shen
- Department of Life Science, McMaster University, Hamilton, Ontario, Canada
| | - Shan Zeng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Changjing Cai
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yiming Qin
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hong Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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4
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Yang GN, Sun YBY, Roberts PK, Moka H, Sung MK, Gardner-Russell J, El Wazan L, Toussaint B, Kumar S, Machin H, Dusting GJ, Parfitt GJ, Davidson K, Chong EW, Brown KD, Polo JM, Daniell M. Exploring single-cell RNA sequencing as a decision-making tool in the clinical management of Fuchs' endothelial corneal dystrophy. Prog Retin Eye Res 2024; 102:101286. [PMID: 38969166 DOI: 10.1016/j.preteyeres.2024.101286] [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: 01/17/2024] [Revised: 06/14/2024] [Accepted: 07/02/2024] [Indexed: 07/07/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has enabled the identification of novel gene signatures and cell heterogeneity in numerous tissues and diseases. Here we review the use of this technology for Fuchs' Endothelial Corneal Dystrophy (FECD). FECD is the most common indication for corneal endothelial transplantation worldwide. FECD is challenging to manage because it is genetically heterogenous, can be autosomal dominant or sporadic, and progress at different rates. Single-cell RNA sequencing has enabled the discovery of several FECD subtypes, each with associated gene signatures, and cell heterogeneity. Current FECD treatments are mainly surgical, with various Rho kinase (ROCK) inhibitors used to promote endothelial cell metabolism and proliferation following surgery. A range of emerging therapies for FECD including cell therapies, gene therapies, tissue engineered scaffolds, and pharmaceuticals are in preclinical and clinical trials. Unlike conventional disease management methods based on clinical presentations and family history, targeting FECD using scRNA-seq based precision-medicine has the potential to pinpoint the disease subtypes, mechanisms, stages, severities, and help clinicians in making the best decision for surgeries and the applications of therapeutics. In this review, we first discuss the feasibility and potential of using scRNA-seq in clinical diagnostics for FECD, highlight advances from the latest clinical treatments and emerging therapies for FECD, integrate scRNA-seq results and clinical notes from our FECD patients and discuss the potential of applying alternative therapies to manage these cases clinically.
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Affiliation(s)
- Gink N Yang
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Yu B Y Sun
- Department of Anatomy and Development Biology, Monash University, Clayton, Australia
| | - Philip Ke Roberts
- Department of Ophthalmology, Medical University Vienna, 18-20 Währinger Gürtel, Vienna, Austria
| | - Hothri Moka
- Mogrify Limited, 25 Cambridge Science Park Milton Road, Milton, Cambridge, UK
| | - Min K Sung
- Mogrify Limited, 25 Cambridge Science Park Milton Road, Milton, Cambridge, UK
| | - Jesse Gardner-Russell
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Layal El Wazan
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Bridget Toussaint
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Satheesh Kumar
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Heather Machin
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia; Lions Eye Donation Service, Level 7, Smorgon Family Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia
| | - Gregory J Dusting
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Geraint J Parfitt
- Mogrify Limited, 25 Cambridge Science Park Milton Road, Milton, Cambridge, UK
| | - Kathryn Davidson
- Department of Anatomy and Development Biology, Monash University, Clayton, Australia
| | - Elaine W Chong
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia; Department of Ophthalmology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Karl D Brown
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Jose M Polo
- Department of Anatomy and Development Biology, Monash University, Clayton, Australia
| | - Mark Daniell
- Centre for Eye Research Australia, Level 7, Peter Howson Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia; Lions Eye Donation Service, Level 7, Smorgon Family Wing, 32 Gisborne Street, East Melbourne, Victoria, Australia.
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5
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Jin W, Pei J, Roy JR, Jayaraman S, Ahalliya RM, Kanniappan GV, Mironescu M, Palanisamy CP. Comprehensive review on single-cell RNA sequencing: A new frontier in Alzheimer's disease research. Ageing Res Rev 2024; 100:102454. [PMID: 39142391 DOI: 10.1016/j.arr.2024.102454] [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: 07/08/2024] [Revised: 08/07/2024] [Accepted: 08/09/2024] [Indexed: 08/16/2024]
Abstract
Alzheimer's disease (AD) is a multifaceted neurodegenerative condition marked by gradual cognitive deterioration and the loss of neurons. While conventional bulk RNA sequencing techniques have shed light on AD pathology, they frequently obscure the cellular diversity within brain tissues. The advent of single-cell RNA sequencing (scRNA-seq) has transformed our capability to analyze the cellular composition of AD, allowing for the detection of unique cell populations, rare cell types, and gene expression alterations at an individual cell level. This review examines the use of scRNA-seq in AD research, focusing on its contributions to understanding cellular diversity, disease progression, and potential therapeutic targets. We discuss key technological innovations, data analysis techniques, and challenges associated with scRNA-seq in studying AD. Furthermore, we highlight recent studies that have utilized scRNA-seq to identify novel biomarkers, uncover disease-associated pathways, and elucidate the role of non-neuronal cells, such as microglia and astrocytes, in AD pathogenesis. By providing a comprehensive overview of advancements in scRNA-seq for unraveling cellular heterogeneity in AD, this review highlights the transformative impact of scRNA-seq on our comprehension of disease mechanisms and the creation of targeted treatments.
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Affiliation(s)
- Wengang Jin
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 2011 QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C, Shaanxi Province Key Laboratory of Bio-Resources, College of Bioscience and Bioengineering, Shaanxi University of Technology, Hanzhong 723001, China
| | - JinJin Pei
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 2011 QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C, Shaanxi Province Key Laboratory of Bio-Resources, College of Bioscience and Bioengineering, Shaanxi University of Technology, Hanzhong 723001, China
| | - Jeane Rebecca Roy
- Department of Anatomy, Bhaarath Medical College and hospital, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu 600073, India
| | - Selvaraj Jayaraman
- Centre of Molecular Medicine and Diagnostics (COMManD), Department of Biochemistry, Saveetha Dental College & Hospital, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai 600077, India
| | - Rathi Muthaiyan Ahalliya
- Department of Biochemistry and Cancer Research Centre, FASCM, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu 641021, India
| | - Gopalakrishnan Velliyur Kanniappan
- Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, Tamil Nadu 602105, India.
| | - Monica Mironescu
- Faculty of Agricultural Sciences Food Industry and Environmental Protection, Lucian Blaga University of Sibiu, Bv. Victoriei 10, Sibiu 550024, Romania.
| | - Chella Perumal Palanisamy
- Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
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6
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Feng Y, Pan M, Li R, He W, Chen Y, Xu S, Chen H, Xu H, Lin Y. Recent developments and new directions in the use of natural products for the treatment of inflammatory bowel disease. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 132:155812. [PMID: 38905845 DOI: 10.1016/j.phymed.2024.155812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/13/2024] [Accepted: 06/06/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) represents a significant global health challenge, and there is an urgent need to explore novel therapeutic interventions. Natural products have demonstrated highly promising effectiveness in the treatment of IBD. PURPOSE This study systematically reviews the latest research advancements in leveraging natural products for IBD treatment. METHODS This manuscript strictly adheres to the PRISMA guidelines. Relevant literature on the effects of natural products on IBD was retrieved from the PubMed, Web of Science and Cochrane Library databases using the search terms "natural product," "inflammatory bowel disease," "colitis," "metagenomics", "target identification", "drug delivery systems", "polyphenols," "alkaloids," "terpenoids," and so on. The retrieved data were then systematically summarized and reviewed. RESULTS This review assessed the different effects of various natural products, such as polyphenols, alkaloids, terpenoids, quinones, and others, in the treatment of IBD. While these natural products offer promising avenues for IBD management, they also face challenges in terms of clinical translation and drug discovery. The advent of metagenomics, single-cell sequencing, target identification techniques, drug delivery systems, and other cutting-edge technologies heralds a new era in overcoming these challenges. CONCLUSION This paper provides an overview of current research progress in utilizing natural products for the treatment of IBD, exploring how contemporary technological innovations can aid in discovering and harnessing bioactive natural products for the treatment of IBD.
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Affiliation(s)
- Yaqian Feng
- Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| | - Mengting Pan
- Institute of Structural Pharmacology & TCM Chemical Biology, Fujian Key Laboratory of Chinese Materia Medica, College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| | - Ruiqiong Li
- College of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| | - Weishen He
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Yangyang Chen
- Institute of Structural Pharmacology & TCM Chemical Biology, Fujian Key Laboratory of Chinese Materia Medica, College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| | - Shaohua Xu
- Institute of Structural Pharmacology & TCM Chemical Biology, Fujian Key Laboratory of Chinese Materia Medica, College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China.
| | - Hui Chen
- Department of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350004, China.
| | - Huilong Xu
- Institute of Structural Pharmacology & TCM Chemical Biology, Fujian Key Laboratory of Chinese Materia Medica, College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China.
| | - Yao Lin
- Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China.
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Park MY, Lee S, Kim HH, Jeong SH, Abusaliya A, Bhosale PB, Seong JK, Park KI, Heo JD, Ahn M, Kim HW, Kim GS. Correlation with Apoptosis Process through RNA-Seq Data Analysis of Hep3B Hepatocellular Carcinoma Cells Treated with Glehnia littoralis Extract (GLE). Int J Mol Sci 2024; 25:9462. [PMID: 39273406 DOI: 10.3390/ijms25179462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/27/2024] [Accepted: 08/29/2024] [Indexed: 09/15/2024] Open
Abstract
Glehnia littoralis is a perennial herb found in coastal sand dunes throughout East Asia. This herb has been reported to have hepatoprotective, immunomodulatory, antioxidant, antibacterial, antifungal, anti-inflammatory, and anticancer activities. It may be effective against hepatocellular carcinoma (HCC). However, whether this has been proven through gene-level RNA-seq analysis is still being determined. Therefore, we are attempting to identify target genes for the cell death process by analyzing the transcriptome of Hep3B cells among HCC treated with GLE (Glehnia littoralis extract) using RNA-seq. Hep3B was used for the GLE treatment, and the MTT test was performed. Hep3B was then treated with GLE at a set concentration of 300 μg/mL and stored for 24 h, followed by RNA isolation and sequencing. We then used the data to create a plot. As a result of the MTT analysis, cell death was observed when Hep3B cells were treated with GLE, and the IC50 was about 300 μg/mL. As a result of making plots using the RNA-seq data of Hep3B treated with 300 μg/mL GLE, a tendency for the apoptotic process was found. Flow cytometry and annexin V/propidium iodide (PI) staining verified the apoptosis of HEP3B cells treated with GLE. Therefore, an increase or decrease in the DEGs involved in the apoptosis process was confirmed. The top five genes increased were GADD45B, DDIT3, GADD45G, CHAC1, and PPP1R15A. The bottom five genes decreased were SGK1, CX3CL1, ZC3H12A, IER3, and HNF1A. In summary, we investigated the RNA-seq dataset of GLE to identify potential targets that may be involved in the apoptotic process in HCC. These goals may aid in the identification and management of HCC.
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Affiliation(s)
- Min-Yeong Park
- Research Institute of Life Science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Republic of Korea
| | - Sujin Lee
- Research Institute of Molecular Alchemy, Gyeongsang National University, 501, Jinju-daero, Jinju 52828, Republic of Korea
| | - Hun-Hwan Kim
- Research Institute of Life Science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Republic of Korea
| | - Se-Hyo Jeong
- Research Institute of Life Science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Republic of Korea
| | - Abuyaseer Abusaliya
- Research Institute of Life Science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Republic of Korea
| | - Pritam Bhangwan Bhosale
- Research Institute of Life Science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Republic of Korea
| | - Je-Kyung Seong
- Laboratory of Developmental Biology and Genomics, BK21 PLUS Program for Creative Veterinary Science Research, Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul 08826, Republic of Korea
| | - Kwang-Il Park
- Research Institute of Life Science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Republic of Korea
| | - Jeong-Doo Heo
- Biological Resources Research Group, Gyeongnam Department of Environment Toxicology and Chemistry, Korea Institute of Toxicology, 17 Jegok-gil, Jinju 52834, Republic of Korea
| | - Meejung Ahn
- Department of Animal Science, College of Life Science, Sangji University, Wonju 26339, Republic of Korea
| | - Hyun-Wook Kim
- Division of Animal Bioscience and Integrated Biotechnology, Jinju 52725, Republic of Korea
| | - Gon-Sup Kim
- Research Institute of Life Science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Republic of Korea
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8
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Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00768-2. [PMID: 39169166 DOI: 10.1038/s41580-024-00768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/23/2024]
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
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Affiliation(s)
- Gunsagar S Gulati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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9
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Liu B, Hu S, Wang X. Applications of single-cell technologies in drug discovery for tumor treatment. iScience 2024; 27:110486. [PMID: 39171294 PMCID: PMC11338156 DOI: 10.1016/j.isci.2024.110486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2024] Open
Abstract
Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.
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Affiliation(s)
- Bingyu Liu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| | - Shunfeng Hu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong 250021, China
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10
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Eastburn DJ, White KS, Jayne ND, Camiolo S, Montis G, Ha S, Watson KG, Yeakley JM, McComb J, Seligmann B. High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.03.606484. [PMID: 39149288 PMCID: PMC11326174 DOI: 10.1101/2024.08.03.606484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ~50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.
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11
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Ma Q, Jiang Y, Cheng H, Xu D. Harnessing the deep learning power of foundation models in single-cell omics. Nat Rev Mol Cell Biol 2024; 25:593-594. [PMID: 38926531 DOI: 10.1038/s41580-024-00756-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Affiliation(s)
- Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Hao Cheng
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
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12
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Ramkissoon R, Cao S, Shah VH. The Pathophysiology of Portal Hypertension. Clin Liver Dis 2024; 28:369-381. [PMID: 38945632 DOI: 10.1016/j.cld.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
This article reviews the pathophysiology of portal hypertension that includes multiple mechanisms internal and external to the liver. This article starts with a review of literature describing the cellular and molecular mechanisms of portal hypertension, microvascular thrombosis, sinusoidal venous congestion, portal angiogenesis, vascular hypocontractility, and hyperdynamic circulation. Mechanotransduction and the gut-liver axis, which are newer areas of research, are reviewed. Dysfunction of this axis contributes to chronic liver injury, inflammation, fibrosis, and portal hypertension. Sequelae of portal hypertension are discussed in subsequent studies.
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Affiliation(s)
- Resham Ramkissoon
- Department of Gastroenterology & Hepatology, Mayo Clinic, 200 1st Street, SW, Rochester, MN 55902, USA
| | - Sheng Cao
- Mayo College of Medicine, Mayo Clinic, 200 1st Street, SW, Rochester, MN 55902, USA
| | - Vijay H Shah
- Department of Gastroenterology & Hepatology, Mayo Clinic, 200 1st Street, SW, Rochester, MN 55902, USA; Department of Internal Medicine, Mayo Clinic, 200 1st Street, SW, Rochester, MN 55902, USA.
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13
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Tang X, Gao L, Jiang X, Hou Z, Wang Y, Hou S, Qu H. Single-cell profiling reveals altered immune landscape and impaired NK cell function in gastric cancer liver metastasis. Oncogene 2024; 43:2635-2646. [PMID: 39060439 DOI: 10.1038/s41388-024-03114-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 07/18/2024] [Accepted: 07/22/2024] [Indexed: 07/28/2024]
Abstract
Gastric cancer (GC) is a substantial global health concern, and the development of liver metastasis (LM) in GC represents a critical stage linked to unfavorable patient prognoses. In this study, we employed single-cell RNA sequencing (scRNA-seq) to investigate the immune landscape of GC liver metastasis, revealing several immuno-suppressive components within the tumor immune microenvironment (TIM). Our findings unveiled an increased presence of cancer-associated fibroblasts (CAFs), myeloid-derived suppressor cell (MDSC)-like macrophages, tumor-associated macrophage (TAM)-like macrophages, and naive T cells, while conventional dendritic cells (cDCs) and effector CD8 T cells declined in LM. Additionally, we identified two distinct natural killer (NK) cell clusters exhibiting differential cytotoxicity-related gene expression, with cytotoxic NK cells notably reduced in LM. Strikingly, TGFβ was identified as an inducer of NK cell dysfunction, potentially contributing to immune evasion and tumor metastasis. In preclinical LM models, the combined approach of inhibiting TGFβ and transferring NK cells exhibited a synergistic impact, resulting in a significant reduction in liver metastasis. This work highlights the importance of understanding the complex immune dynamics within GC liver metastasis and presents a promising strategy combining TGFβ inhibition and NK-based immunotherapy to improve patient outcomes.
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Affiliation(s)
- Xiaolong Tang
- Department of General Surgery, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Lei Gao
- Department of General Surgery, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Xingzhi Jiang
- Department of Clinical Medicine, Qilu Medical College of Shandong University, Jinan, 250011, China
| | - Zhenyu Hou
- Department of General Surgery, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Yiwen Wang
- Department of Clinical Medicine, Qilu Medical College of Shandong University, Jinan, 250011, China
| | - Shiyang Hou
- Department of Clinical Medicine, Qilu Medical College of Shandong University, Jinan, 250011, China
| | - Hui Qu
- Department of General Surgery, Qilu Hospital of Shandong University, Jinan, 250012, China.
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14
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Li M, Su Y, Gao Y, Tian W. ReCIDE: robust estimation of cell type proportions by integrating single-reference-based deconvolutions. Brief Bioinform 2024; 25:bbae422. [PMID: 39177263 PMCID: PMC11342246 DOI: 10.1093/bib/bbae422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 07/16/2024] [Accepted: 08/12/2024] [Indexed: 08/24/2024] Open
Abstract
In this study, we introduce Robust estimation of Cell type proportions by Integrating single-reference-based DEconvolutions (ReCIDE), an innovative framework for robust estimation of cell type proportions by integrating single-reference-based deconvolutions. ReCIDE outperforms existing approaches in benchmark and real datasets, particularly excelling in estimating rare cell type proportions. Through exploratory analysis on public bulk data of triple-negative breast cancer (TNBC) patients using ReCIDE, we demonstrate a significant correlation between the prognosis of TNBC patients and the proportions of both T cell and perivascular-like cell subtypes. Built upon this discovery, we develop a prognostic assessment model for TNBC patients. Our contribution presents a novel framework for enhancing deconvolution accuracy, showcasing its effectiveness in medical research.
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Affiliation(s)
- Minghan Li
- State Key Laboratory of Genetic Engineering, Department of Computational Biology, School of Life Sciences, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China
| | - Yuqing Su
- State Key Laboratory of Genetic Engineering, Department of Computational Biology, School of Life Sciences, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China
| | - Yanbo Gao
- Shanghai SPH Jiaolian Pharmaceutical Technology Company, Limited, Buliding 4, 998 Ha Lei Road, Pudong District, Shanghai 201203, China
| | - Weidong Tian
- State Key Laboratory of Genetic Engineering, Department of Computational Biology, School of Life Sciences, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China
- Children’s Hospital of Fudan University, 399 Wanyuan Road, Minhang District, Shanghai 201102, China
- Children’s Hospital of Shandong University, 23976 Jingshi Road, Huaiyin District, Jinan, Shandong 250022, China
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15
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Catacutan DB, Alexander J, Arnold A, Stokes JM. Machine learning in preclinical drug discovery. Nat Chem Biol 2024:10.1038/s41589-024-01679-1. [PMID: 39030362 DOI: 10.1038/s41589-024-01679-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/13/2024] [Indexed: 07/21/2024]
Abstract
Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that can augment the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specifically, we highlight an array of ML-based efforts, across diverse disease areas, to accelerate initial hit discovery, mechanism-of-action (MOA) elucidation and chemical property optimization. With advances in the application of ML across diverse therapeutic areas, we posit that fully ML-integrated drug-discovery pipelines will define the future of drug-development programs.
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Affiliation(s)
- Denise B Catacutan
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Jeremie Alexander
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Autumn Arnold
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Jonathan M Stokes
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.
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16
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Merz KM, Wei G, Zhu F. Editorial: Cutting-Edge Methodologies in Omics Data Analysis. J Chem Inf Model 2024; 64:4939-4940. [PMID: 38973305 DOI: 10.1021/acs.jcim.4c01021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Department of Mathematics, Michigan State University, East Lansing 48824, Michigan, United States
| | - Guowei Wei
- Department of Chemistry, Department of Mathematics, Michigan State University, East Lansing 48824, Michigan, United States
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, Zhejiang, China
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17
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Papulino C, Benedetti R, Altucci L. Harnessing normalcy: The potential of healthy tissue for patient outcomes. Int J Cancer 2024. [PMID: 38973564 DOI: 10.1002/ijc.35083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2024] [Indexed: 07/09/2024]
Affiliation(s)
- Chiara Papulino
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Vico Luigi De Crecchio, Naples, Italy
| | - Rosaria Benedetti
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Vico Luigi De Crecchio, Naples, Italy
- Program of Medical Epigenetics, Vanvitelli Hospital, Naples, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Vico Luigi De Crecchio, Naples, Italy
- Program of Medical Epigenetics, Vanvitelli Hospital, Naples, Italy
- Biogem Institute of Molecular and Genetic Biology, Contrada Camporeale, Ariano Irpino, Italy
- Institute of Endocrinology and Oncology "Gaetano Salvatore" (IEOS), Via Sergio Pansini, Naples, Italy
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18
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Zeng Y, Ma Q, Chen J, Kong X, Chen Z, Liu H, Liu L, Qian Y, Wang X, Lu S. Single-cell sequencing: Current applications in various tuberculosis specimen types. Cell Prolif 2024:e13698. [PMID: 38956399 DOI: 10.1111/cpr.13698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/21/2024] [Accepted: 06/07/2024] [Indexed: 07/04/2024] Open
Abstract
Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.
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Affiliation(s)
- Yuqin Zeng
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Quan Ma
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Jinyun Chen
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Xingxing Kong
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Zhanpeng Chen
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Huazhen Liu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Lanlan Liu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Yan Qian
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Xiaomin Wang
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
| | - Shuihua Lu
- National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China
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19
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Škarková A, Bizzarri M, Janoštiak R, Mašek J, Rosel D, Brábek J. Educate, not kill: treating cancer without triggering its defenses. Trends Mol Med 2024; 30:673-685. [PMID: 38658206 DOI: 10.1016/j.molmed.2024.04.003] [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: 02/19/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/26/2024]
Abstract
Traditionally, anticancer therapies focus on restraining uncontrolled proliferation. However, these cytotoxic therapies expose cancer cells to direct killing, instigating the process of natural selection favoring survival of resistant cells that become the foundation for tumor progression and therapy failure. Recognizing this phenomenon has prompted the development of alternative therapeutic strategies. Here we propose strategies targeting cancer hallmarks beyond proliferation, aiming at re-educating cancer cells towards a less malignant phenotype. These strategies include controlling cell dormancy, transdifferentiation therapy, normalizing the cancer microenvironment, and using migrastatic therapy. Adaptive resistance to these educative strategies does not confer a direct proliferative advantage to resistant cells, as non-resistant cells are not subject to eradication, thereby delaying or preventing the development of therapy-resistant tumors.
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Affiliation(s)
- Aneta Škarková
- Department of Cell Biology, BIOCEV, Faculty of Science, Charles University, Vestec, Czech Republic
| | - Mariano Bizzarri
- System Biology Group Laboratory, Sapienza University, Rome, Italy
| | - Radoslav Janoštiak
- First Faculty of Medicine, BIOCEV, Charles University, Vestec, Czech Republic
| | - Jan Mašek
- Department of Cell Biology, Faculty of Science, Charles University, Prague, Czech Republic
| | - Daniel Rosel
- Department of Cell Biology, BIOCEV, Faculty of Science, Charles University, Vestec, Czech Republic.
| | - Jan Brábek
- Department of Cell Biology, BIOCEV, Faculty of Science, Charles University, Vestec, Czech Republic.
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20
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Sin DD. What Single Cell RNA Sequencing Has Taught Us about Chronic Obstructive Pulmonary Disease. Tuberc Respir Dis (Seoul) 2024; 87:252-260. [PMID: 38369875 PMCID: PMC11222093 DOI: 10.4046/trd.2024.0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/17/2024] [Indexed: 02/20/2024] Open
Abstract
Chronic obstructive pulmonary disease (COPD) affects close to 400 million people worldwide and is the 3rd leading cause of mortality. It is a heterogeneous disorder with multiple endophenotypes, each driven by specific molecular networks and processes. Therapeutic discovery in COPD has lagged behind other disease areas owing to a lack of understanding of its pathobiology and scarcity of biomarkers to guide therapies. Single cell RNA sequencing (scRNA-seq) is a powerful new tool to identify important cellular and molecular networks that play a crucial role in disease pathogenesis. This paper provides an overview of the scRNA-seq technology and its application in COPD and the lessons learned to date from scRNA-seq experiments in COPD.
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Affiliation(s)
- Don D. Sin
- Centre for Heart Lung Innovation, St. Paul’s Hospital and Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada
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21
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Li S, Chao H, Li Z, Chen S, Zhang J, Hao W, Zhang S, Liu C, Liu H. Sex dimorphism of IL-17-secreting peripheral blood mononuclear cells in ankylosing spondylitis based on bioinformatics analysis and machine learning. BMC Musculoskelet Disord 2024; 25:490. [PMID: 38914997 PMCID: PMC11194900 DOI: 10.1186/s12891-024-07589-6] [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: 03/19/2024] [Accepted: 06/12/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Ankylosing spondylitis (AS) with radiographic damage is more prevalent in men than in women. IL-17, which is mainly secreted from peripheral blood mononuclear cells (PBMCs), plays an important role in the development of AS. Its expression is different between male and female. However, it is still unclear whether sex dimorphism of IL-17 contribute to sex differences in AS. METHODS GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812 datasets were collected from the Gene Expression Omnibus (GEO) database. Differential expressed genes (DEGs) were analyzed with the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods. CIBERSORTx and EcoTyper algorithms were used for immune infiltration analyses. Machine learning based on the XGBoost algorithm model was used to identify the impact of DEGs. The Connectivity Map (CMAP) database was used as a drug discovery tool for exploring potential drugs based on the DEGs. RESULTS According to immune infiltration analyses, T cells accounted for the largest proportion of IL-17-secreting PBMCs, and KEGG analyses suggested an enhanced activation of mast cells among male AS patients, whereas the expression of TNF was higher in female AS patients. Other signaling pathways, including those involving metastasis-associated 1 family member 3 (MAT3) or proteasome, were found to be more activated in male AS patients. Regarding metabolic patterns, oxidative phosphorylation pathways and lipid oxidation were significantly upregulated in male AS patients. In XGBoost algorithm model, DEGs including METRN and TMC4 played important roles in the disease process. we integrated the CMAP database for systematic analyses of polypharmacology and drug repurposing, which indicated that atorvastatin, famciclocir, ATN-161 and taselisib may be applicable to the treatment of AS. CONCLUSIONS We analyzed the sex dimorphism of IL-17-secreting PBMCs in AS. The results showed that mast cell activation was stronger in males, while the expression of TNF was higher in females. In addition, through machine learning and the CMAP database, we found that genes such as METRN and TMC4 may promote the development of AS, and drugs such as atorvastatin potentially could be used for AS treatment.
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Affiliation(s)
- Sifang Li
- Department of Spine Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China
- Guangdong Province Key Laboratory of Orthopaedics and Traumatology, Guangzhou, Guangdong, 510080, China
| | - Hua Chao
- Department of Spine Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China
- Guangdong Province Key Laboratory of Orthopaedics and Traumatology, Guangzhou, Guangdong, 510080, China
| | - Zihao Li
- Department of Spine Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China
- Guangdong Province Key Laboratory of Orthopaedics and Traumatology, Guangzhou, Guangdong, 510080, China
| | - Siwen Chen
- Department of Spine Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China
- Guangdong Province Key Laboratory of Orthopaedics and Traumatology, Guangzhou, Guangdong, 510080, China
| | - Jingyu Zhang
- Department of Spine Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China
- Guangdong Province Key Laboratory of Orthopaedics and Traumatology, Guangzhou, Guangdong, 510080, China
| | - Wenjun Hao
- Department of Spine Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China
- Guangdong Province Key Laboratory of Orthopaedics and Traumatology, Guangzhou, Guangdong, 510080, China
| | - Shuai Zhang
- Department of Spine Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China
- Guangdong Province Key Laboratory of Orthopaedics and Traumatology, Guangzhou, Guangdong, 510080, China
| | - Caijun Liu
- Department of Spine Surgery, The Third Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510378, China.
- Guangdong research institute for Orthopedics & Traumatology of Chinese Medicine, No. 22, Qingzhu Street, Jiangnan West Road, Guangzhou, 510378, China.
| | - Hui Liu
- Department of Spine Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China.
- Guangdong Province Key Laboratory of Orthopaedics and Traumatology, Guangzhou, Guangdong, 510080, China.
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22
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Chin KW, Khoo SC, Paul RPM, Luang-In V, Lam SD, Ma NL. Potential of Synbiotics and Probiotics as Chemopreventive Agent. Probiotics Antimicrob Proteins 2024:10.1007/s12602-024-10299-z. [PMID: 38896220 DOI: 10.1007/s12602-024-10299-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/22/2024] [Indexed: 06/21/2024]
Abstract
Cancer remains a global problem, with millions of new cases diagnosed yearly and countless lives lost. The financial burden of cancer therapy, along with worries about the long-term safety of existing medicines, necessitates the investigation of alternative approaches to cancer prevention. Probiotics generate chemopreventive compounds such as bacteriocins, short-chain fatty acids (SCFA), and extracellular polymeric substances (EPS), which have demonstrated the ability to impede cancer cell proliferation, induce apoptosis, and bolster the expression of pro-apoptotic genes. On the other hand, prebiotics, classified as non-digestible food ingredients, promote the proliferation of probiotics within the colon, thereby ensuring sustained functionality of the gut microbiota. Consequently, the synergistic effect of combining prebiotics with probiotics, known as the synbiotic effect, in dietary interventions holds promise for potentially mitigating cancer risk and augmenting preventive measures. The utilization of gut microbiota in cancer treatment has shown promise in alleviating adverse health effects. This review explored the potential and the role of probiotics and synbiotics in enhancing health and contributing to cancer prevention efforts. In this review, the applications of functional probiotics and synbiotics, the mechanisms of action of probiotics in cancer, and the relationship of probiotics with various drugs were discussed, shedding light on the potential of probiotics and synbiotics to alleviate the burdens of cancer treatment.
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Affiliation(s)
- Kah Wei Chin
- Bioses Research Interest Group (BIOSES), Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Terengganu, Malaysia
| | - Shing Ching Khoo
- Bioses Research Interest Group (BIOSES), Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Terengganu, Malaysia
| | - Richard Paul Merisha Paul
- Bioses Research Interest Group (BIOSES), Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Terengganu, Malaysia
| | - Vijitra Luang-In
- Natural Antioxidant Innovation Research Unit, Department of Biotechnology, Faculty of Technology, Mahasarakham University, Khamriang, 44150, Kantarawichai, Maha Sarakham, Thailand
| | - Su Datt Lam
- School of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia
| | - Nyuk Ling Ma
- Bioses Research Interest Group (BIOSES), Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Terengganu, Malaysia.
- Department of Sustainable Engineering, Institute of Biotechnology, Saveetha School of Engineering, SIMATS, Chennai, 602105, India.
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23
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Nouri N, Gaglia G, Mattoo H, de Rinaldis E, Savova V. GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions. CELL REPORTS METHODS 2024; 4:100794. [PMID: 38861988 PMCID: PMC11228368 DOI: 10.1016/j.crmeth.2024.100794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/28/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.
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Affiliation(s)
- Nima Nouri
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA.
| | - Giorgio Gaglia
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Hamid Mattoo
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Emanuele de Rinaldis
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Virginia Savova
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA.
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24
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Carraro C, Montgomery JV, Klimmt J, Paquet D, Schultze JL, Beyer MD. Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs. Front Mol Neurosci 2024; 17:1414886. [PMID: 38952421 PMCID: PMC11215216 DOI: 10.3389/fnmol.2024.1414886] [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: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.
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Affiliation(s)
- Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jessica V. Montgomery
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| | - Julien Klimmt
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
- Immunogenomics & Neurodegeneration, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
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25
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors. Cancer Res 2024; 84:2021-2033. [PMID: 38581448 PMCID: PMC11178452 DOI: 10.1158/0008-5472.can-23-3005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/18/2023] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors. SIGNIFICANCE A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Robert F. Gruener
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Israa G. Abdelbar
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
- Clinical Pharmacy Practice Department, The British University in Egypt, El Sherouk, 11837, Egypt
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Anand G. Patel
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105
| | - R. Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
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26
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Jin X, Fu C, Qi J, Chen C. Revolutionary multi-omics analysis revealing prognostic signature of thyroid cancer and subsequent in vitro validation of SNAI1 in mediating thyroid cancer progression through EMT. Clin Exp Med 2024; 24:127. [PMID: 38869635 DOI: 10.1007/s10238-024-01387-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/30/2024] [Indexed: 06/14/2024]
Abstract
Thyroid carcinoma (TC), the most commonly diagnosed malignancy of the endocrine system, has witnessed a significant rise in incidence over the past few decades. The integration of scRNA-seq with other sequencing approaches offers researchers a distinct perspective to explore mechanisms underlying TC progression. Therefore, it is crucial to develop a prognostic model for TC patients by utilizing a multi-omics approach. We acquired and processed transcriptomic data from the TCGA-THCA dataset, including mRNA expression profiles, lncRNA expression profiles, miRNA expression profiles, methylation chip data, gene mutation data, and clinical data. We constructed a tumor-related risk model using machine learning methods and developed a consensus machine learning-driven signature (CMLS) for accurate and stable prediction of TC patient outcomes. 2 strains of undifferentiated TC cell lines and 1 strain of PTC cell line were utilized for in vitro validation. mRNA, protein levels of hub genes, epithelial-mesenchymal transition (EMT)-associated phenotypes were detected by a series of in vitro experiments. We identified 3 molecular subtypes of TC based on integrated multi-omics clustering algorithms, which were associated with overall survival and displayed distinct molecular features. We developed a CMLS based on 28 hub genes to predict patient outcomes, and demonstrated that CMLS outperformed other prognostic models. TC patients of relatively lower CMLS score had significantly higher levels of T cells, B cells, and macrophages, indicating an immune-activated state. Fibroblasts were predominantly enriched in the high CMLS group, along with markers associated with immune suppression and evasion. We identified several drugs that could be suitable for patients with high CMLS, including Staurosporine_1034, Rapamycin_1084, gemcitabine, and topotecan. SNAI1 was elevated in both undifferentiated TC cell lines, comparing to PTC cells. Knockdown of SNAI1 reduced the cell proliferation and EMT phenotypes of undifferentiated TC cells. Our findings highlight the importance of multi-omics analysis in understanding the molecular subtypes and immune characteristics of TC, and provide a novel prognostic model and potential therapeutic targets for this disease. Moreover, we identified SNAI1 in mediating TC progression through EMT in vitro.
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Affiliation(s)
- Xin Jin
- Department of Breast Surgery, Zhuji Affiliated Hospital of Wenzhou Medical University, Zhuji, 311899, Zhejiang, China
| | - Chunlan Fu
- Department of Hematology, Zhuji Affiliated Hospital of Wenzhou Medical University, Zhuji, 311899, Zhejiang, China
| | - Jiahui Qi
- Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chuanzhi Chen
- Department of Thyroid Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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27
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Lteif C, Huang Y, Guerra LA, Gawronski BE, Duarte JD. Using Omics to Identify Novel Therapeutic Targets in Heart Failure. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004398. [PMID: 38766848 PMCID: PMC11187651 DOI: 10.1161/circgen.123.004398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.
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Affiliation(s)
- Christelle Lteif
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Yimei Huang
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Leonardo A Guerra
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Brian E Gawronski
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Julio D Duarte
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
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28
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Huayamares SG, Loughrey D, Kim H, Dahlman JE, Sorscher EJ. Nucleic acid-based drugs for patients with solid tumours. Nat Rev Clin Oncol 2024; 21:407-427. [PMID: 38589512 DOI: 10.1038/s41571-024-00883-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2024] [Indexed: 04/10/2024]
Abstract
The treatment of patients with advanced-stage solid tumours typically involves a multimodality approach (including surgery, chemotherapy, radiotherapy, targeted therapy and/or immunotherapy), which is often ultimately ineffective. Nucleic acid-based drugs, either as monotherapies or in combination with standard-of-care therapies, are rapidly emerging as novel treatments capable of generating responses in otherwise refractory tumours. These therapies include those using viral vectors (also referred to as gene therapies), several of which have now been approved by regulatory agencies, and nanoparticles containing mRNAs and a range of other nucleotides. In this Review, we describe the development and clinical activity of viral and non-viral nucleic acid-based treatments, including their mechanisms of action, tolerability and available efficacy data from patients with solid tumours. We also describe the effects of the tumour microenvironment on drug delivery for both systemically administered and locally administered agents. Finally, we discuss important trends resulting from ongoing clinical trials and preclinical testing, and manufacturing and/or stability considerations that are expected to underpin the next generation of nucleic acid agents for patients with solid tumours.
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Affiliation(s)
- Sebastian G Huayamares
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Emory University School of Medicine, Atlanta, GA, USA
| | - David Loughrey
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Emory University School of Medicine, Atlanta, GA, USA
| | - Hyejin Kim
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Emory University School of Medicine, Atlanta, GA, USA
| | - James E Dahlman
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Emory University School of Medicine, Atlanta, GA, USA.
| | - Eric J Sorscher
- Emory University School of Medicine, Atlanta, GA, USA.
- Department of Pediatrics, Emory University, Atlanta, GA, USA.
- Winship Cancer Institute, Emory University, Atlanta, GA, USA.
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29
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Balan D, Kampan NC, Plebanski M, Abd Aziz NH. Unlocking ovarian cancer heterogeneity: advancing immunotherapy through single-cell transcriptomics. Front Oncol 2024; 14:1388663. [PMID: 38873253 PMCID: PMC11169633 DOI: 10.3389/fonc.2024.1388663] [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: 02/20/2024] [Accepted: 05/15/2024] [Indexed: 06/15/2024] Open
Abstract
Ovarian cancer, a highly fatal gynecological cancer, warrants the need for understanding its heterogeneity. The disease's prevalence and impact are underscored with statistics on mortality rates. Ovarian cancer is categorized into distinct morphological groups, each with its characteristics and prognosis. Despite standard treatments, survival rates remain low due to relapses and chemoresistance. Immune system involvement is evident in ovarian cancer's progression, although the tumor employs immune evasion mechanisms. Immunotherapy, particularly immune checkpoint blockade therapy, is promising, but ovarian cancer's heterogeneity limits its efficacy. Single-cell sequencing technology could be explored as a solution to dissect the heterogeneity within tumor-associated immune cell populations and tumor microenvironments. This cutting-edge technology has the potential to enhance diagnosis, prognosis, and personalized immunotherapy in ovarian cancer, reflecting its broader application in cancer research. The present review focuses on recent advancements and the challenges in applying single-cell transcriptomics to ovarian cancer.
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Affiliation(s)
- Dharvind Balan
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Nirmala Chandralega Kampan
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Magdalena Plebanski
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia
| | - Nor Haslinda Abd Aziz
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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30
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Jin C, Lu X, Yang M, Hou S. Integrative analysis indicates the potential values of ANKRD53 in stomach adenocarcinoma. Discov Oncol 2024; 15:188. [PMID: 38801557 PMCID: PMC11130106 DOI: 10.1007/s12672-024-01054-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/23/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Ankyrin repeat domain 53 (ANKRD53) plays an important role in maintaining chromosome integrity and stability, and chromosome instability is associated with cancer. Through integrative analysis, this study investigates the potential value of ANKRD53 in stomach adenocarcinoma (STAD). METHODS RNA-seq and scRNA-seq data were used for integrative analysis based on online databases. Expression of ANKRD53 was confirmed by RT-PCR after bioinformatic analysis. Kaplan-Meier and Cox regression analyses were performed to evaluate the prognostic value of ANKRD53 in STAD. Gene set enrichment analysis (GSEA) was performed to evaluate ANKRD53-related signaling pathways. In addition, the interaction of ANKRD53 with immunity was also investigated. RESULTS RT-PCR in STAD cell lines confirmed that ANKRD53 was downregulated in STAD samples compared to normal samples in the online databases. As an independent predictive biomarker, ANKRD53 was combined with other clinicopathological parameters to create a prognostic nomogram. Using GSEA, ANKRD53 was found to be involved in five pathways, including the TGF-β signaling pathway. Further investigation revealed that ANKRD53 was associated with immune checkpoint molecules, immunological pathways, and immunotherapy, in addition to MSI, TMB and neoantigens. In addition, scRNA-seq data revealed that ANKRD53 is mainly expressed in CD8+ T and dendritic cells. CONCLUSIONS ANKRD53 is an important biomarker for STAD that deserves further attention.
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Affiliation(s)
- Chunjing Jin
- Laboratory Medicine Center, The Affiliated Chuzhou Hospital of Anhui Medical University, The First People's Hospital of Chuzhou, Chuzhou, China
| | - Xu Lu
- Department of General Surgery, The Affiliated Chuzhou Hospital of Anhui Medical University, The First People's Hospital of Chuzhou, Chuzhou, China
| | - Minfeng Yang
- School of Public Health, Nantong University, Nantong, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
| | - Shiqiang Hou
- Department of Neurosurgery, The Affiliated Chuzhou Hospital of Anhui Medical University, The First People's Hospital of Chuzhou, Chuzhou, China.
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31
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Ryu S, Lee EK. The Pivotal Role of Macrophages in the Pathogenesis of Pancreatic Diseases. Int J Mol Sci 2024; 25:5765. [PMID: 38891952 PMCID: PMC11171839 DOI: 10.3390/ijms25115765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 05/20/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
The pancreas is an organ with both exocrine and endocrine functions, comprising a highly organized and complex tissue microenvironment composed of diverse cellular and non-cellular components. The impairment of microenvironmental homeostasis, mediated by the dysregulation of cell-to-cell crosstalk, can lead to pancreatic diseases such as pancreatitis, diabetes, and pancreatic cancer. Macrophages, key immune effector cells, can dynamically modulate their polarization status between pro-inflammatory (M1) and anti-inflammatory (M2) modes, critically influencing the homeostasis of the pancreatic microenvironment and thus playing a pivotal role in the pathogenesis of the pancreatic disease. This review aims to summarize current findings and provide detailed mechanistic insights into how alterations mediated by macrophage polarization contribute to the pathogenesis of pancreatic disorders. By analyzing current research comprehensively, this article endeavors to deepen our mechanistic understanding of regulatory molecules that affect macrophage polarity and the intricate crosstalk that regulates pancreatic function within the microenvironment, thereby facilitating the development of innovative therapeutic strategies that target perturbations in the pancreatic microenvironment.
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Affiliation(s)
- Seungyeon Ryu
- Department of Biochemistry, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea;
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Eun Kyung Lee
- Department of Biochemistry, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea;
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Institute for Aging and Metabolic Diseases, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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32
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Cho J, Baik B, Nguyen HCT, Park D, Nam D. Characterizing efficient feature selection for single-cell expression analysis. Brief Bioinform 2024; 25:bbae317. [PMID: 38975891 PMCID: PMC11229035 DOI: 10.1093/bib/bbae317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/31/2024] [Accepted: 06/17/2024] [Indexed: 07/09/2024] Open
Abstract
Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.
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Affiliation(s)
- Juok Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50, UNIST-gil, Ulsan 44919, Republic of Korea
| | - Bukyung Baik
- Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), 50, UNIST-gil, Ulsan 44919, Republic of Korea
| | - Hai C T Nguyen
- Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), 50, UNIST-gil, Ulsan 44919, Republic of Korea
| | - Daeui Park
- Department of Predictive Toxicology, Korea Institute of Toxicology, 141, Gajeong-ro, Daejeon 34114, Republic of Korea
| | - Dougu Nam
- Department of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), 50, UNIST-gil, Ulsan 44919, Republic of Korea
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology (UNIST), 50, UNIST-gil, Ulsan 44919, Republic of Korea
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33
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Xiao R, Lin M, Liu M, Ma Q. Single cells and TRUST4 reveal immunological features of the HFRS transcriptome. Front Med (Lausanne) 2024; 11:1403335. [PMID: 38803345 PMCID: PMC11128564 DOI: 10.3389/fmed.2024.1403335] [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: 03/19/2024] [Accepted: 04/24/2024] [Indexed: 05/29/2024] Open
Abstract
The etiology of hemorrhagic fever with renal syndrome (HFRS) is significantly impacted by a variety of immune cells. Nevertheless, the existing techniques for sequencing peripheral blood T cell receptor (TCR) or B cell receptor (BCR) libraries in HFRS are constrained by both limitations and high costs. In this investigation, we utilized the computational tool TRUST4 to generate TCR and BCR libraries utilizing comprehensive RNA-seq data from peripheral blood specimens of HFRS patients. This facilitated the examination of clonality and diversity within immune libraries linked to the condition. Despite previous research on immune cell function, the underlying mechanisms remain intricate, and differential gene expression across immune cell types and cell-to-cell interactions within immune cell clusters have not been thoroughly explored. To address this gap, we performed clustering analysis on 11 cell subsets derived from raw single-cell RNA-seq data, elucidating characteristic changes in cell subset proportions under disease conditions. Additionally, we utilized CellChat, a tool for cell-cell communication analysis, to investigate the impact of MIF family, CD70 family, and GALECTIN family cytokines-known to be involved in cell communication-on immune cell subsets. Furthermore, hdWGCNA analysis identified core genes implicated in HFRS pathogenesis within T cells and B cells. Trajectory analysis revealed that most cell subsets were in a developmental stage, with high expression of transcription factors such as NFKB and JUN in Effector CD8+ T cells, as well as in Naive CD4+ T cells and Naive B cells. Our findings provide a comprehensive understanding of the dynamic changes in immune cells during HFRS pathogenesis, identifying specific V genes and J genes in TCR and BCR that contribute to advancing our knowledge of HFRS. These insights offer potential implications for the diagnosis and treatment of this autoimmune disease.
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Affiliation(s)
| | | | | | - Qingqing Ma
- The Central Laboratory of Guizhou Aerospace Hospital, Zunyi, China
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Cao J, Li C, Cui Z, Deng S, Lei T, Liu W, Yang H, Chen P. Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery. Theranostics 2024; 14:2946-2968. [PMID: 38773973 PMCID: PMC11103497 DOI: 10.7150/thno.95908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/25/2024] [Indexed: 05/24/2024] Open
Abstract
Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.
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Affiliation(s)
- Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Tong Lei
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Wei Liu
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
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Zhang X, Wang H, Sun C. BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation. J Cancer Res Clin Oncol 2024; 150:237. [PMID: 38713378 PMCID: PMC11076393 DOI: 10.1007/s00432-024-05740-3] [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: 02/18/2024] [Accepted: 04/03/2024] [Indexed: 05/08/2024]
Abstract
PURPOSE Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies. METHODS Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets' machine learning prediction with important features and rationales, generating interpretable analytical reports. RESULTS In this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers. CONCLUSION The novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.
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Affiliation(s)
- Xin Zhang
- Beijing Engineering Research Center of Protein and Antibody, Sinocelltech Ltd., Beijing, 100176, China
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Huiyu Wang
- Beijing Engineering Research Center of Protein and Antibody, Sinocelltech Ltd., Beijing, 100176, China
| | - Chunyun Sun
- Beijing Engineering Research Center of Protein and Antibody, Sinocelltech Ltd., Beijing, 100176, China.
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Murao A, Jha A, Aziz M, Wang P. An engineered poly(A) tail attenuates gut ischemia/reperfusion-induced acute lung injury. Surgery 2024; 175:1346-1351. [PMID: 38342730 PMCID: PMC11001521 DOI: 10.1016/j.surg.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/28/2023] [Accepted: 01/02/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND Gut ischemia/reperfusion causes the release of damage-associated molecular patterns, leading to acute lung injury and high mortality. Cold-inducible ribonucleic acid-binding protein is a ribonucleic acid chaperon that binds the polyadenylation tail of messenger ribonucleic acid intracellularly. Upon cell stress, cold-inducible ribonucleic acid-binding protein is released, and extracellular cold-inducible ribonucleic acid-binding protein acts as a damage-associated molecular pattern, worsening inflammation. To inhibit extracellular cold-inducible ribonucleic acid-binding protein, we have recently developed an engineered polyadenylation tail named A12. Here, we sought to investigate the therapeutic potential of A12 in gut ischemia/reperfusion-induced acute lung injury. METHODS Male C57BL6/J mice underwent superior mesenteric artery occlusion and were treated with intraperitoneal A12 (0.5 nmol/g body weight) or vehicle at the time of reperfusion. Blood and lungs were collected 4 hours after gut ischemia/reperfusion. Systemic levels of extracellular cold-inducible ribonucleic acid-binding protein, interleukin-6, aspartate transaminase, alanine transaminase, and lactate dehydrogenase were determined. The pulmonary gene expression of cytokines (interleukin-6, interleukin-1β) and chemokines (macrophage-inflammatory protein-2, keratinocyte-derived chemokine) was also assessed. In addition, lung myeloperoxidase, injury score, and cell death were determined. Mice were monitored for 48 hours after gut ischemia/reperfusion for survival assessment. RESULTS Gut ischemia/reperfusion significantly increased the serum extracellular cold-inducible ribonucleic acid-binding protein levels. A12 treatment markedly reduced the elevated serum interleukin-6, alanine transaminase, aspartate transaminase, and lactate dehydrogenase by 53%, 23%, 23%, and 24%, respectively, in gut ischemia/reperfusion mice. A12 also significantly decreased cytokine and chemokine messenger ribonucleic acids and myeloperoxidase activity in the lungs of gut ischemia/reperfusion mice. Histological analysis revealed that A12 attenuated tissue injury and cell death in the lungs of gut ischemia/reperfusion mice. Finally, administration of A12 markedly improved the survival of gut ischemia/reperfusion mice. CONCLUSION A12, a novel extracellular cold-inducible ribonucleic acid-binding protein inhibitor, diminishes inflammation and mitigates acute lung injury when employed as a treatment during gut ischemia/reperfusion. Hence, the targeted approach toward extracellular cold-inducible ribonucleic acid-binding protein emerges as a promising therapeutic strategy for alleviating gut ischemia/reperfusion-induced acute lung injury.
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Affiliation(s)
- Atsushi Murao
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY
| | - Alok Jha
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY
| | - Monowar Aziz
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY; Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
| | - Ping Wang
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY; Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY.
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Zhengdong A, Xiaoying X, Shuhui F, Rui L, Zehui T, Guanbin S, Li Y, Xi T, Wanqian L. Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma. Cancer Cell Int 2024; 24:130. [PMID: 38584256 PMCID: PMC11000322 DOI: 10.1186/s12935-024-03306-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/20/2024] [Indexed: 04/09/2024] Open
Abstract
BACKGROUND Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive. METHODS The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein-protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments. RESULTS Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells. CONCLUSIONS This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.
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Affiliation(s)
- Ai Zhengdong
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China
| | - Xing Xiaoying
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China
| | - Fu Shuhui
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China
| | - Liang Rui
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China
| | - Tang Zehui
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China
| | - Song Guanbin
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China
| | - Yang Li
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China
| | - Tang Xi
- Gastrointestinal Cancer Center, Chongqing University Cancer Hospital, Chongqing, 400000, People's Republic of China.
| | - Liu Wanqian
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, 174 Shazheng Street, Chongqing, 400000, People's Republic of China.
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Bhardwaj S, Bulluss M, D'Aubeterre A, Derakhshani A, Penner R, Mahajan M, Mahajan VB, Dufour A. Integrating the analysis of human biopsies using post-translational modifications proteomics. Protein Sci 2024; 33:e4979. [PMID: 38533548 DOI: 10.1002/pro.4979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/07/2024] [Accepted: 03/16/2024] [Indexed: 03/28/2024]
Abstract
Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.
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Affiliation(s)
- Sonali Bhardwaj
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Mitchell Bulluss
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Ana D'Aubeterre
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Afshin Derakhshani
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Regan Penner
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - MaryAnn Mahajan
- Molecular Surgery Laboratory, Stanford University, Palo Alto, California, USA
| | - Vinit B Mahajan
- Molecular Surgery Laboratory, Stanford University, Palo Alto, California, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California, USA
| | - Antoine Dufour
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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Ghandikota SK, Jegga AG. Application of artificial intelligence and machine learning in drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:171-211. [PMID: 38789178 DOI: 10.1016/bs.pmbts.2024.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.
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Affiliation(s)
- Sudhir K Ghandikota
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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Ullas S, Sinclair C. Applications of Flow Cytometry in Drug Discovery and Translational Research. Int J Mol Sci 2024; 25:3851. [PMID: 38612661 PMCID: PMC11011675 DOI: 10.3390/ijms25073851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
Flow cytometry is a mainstay technique in cell biology research, where it is used for phenotypic analysis of mixed cell populations. Quantitative approaches have unlocked a deeper value of flow cytometry in drug discovery research. As the number of drug modalities and druggable mechanisms increases, there is an increasing drive to identify meaningful biomarkers, evaluate the relationship between pharmacokinetics and pharmacodynamics (PK/PD), and translate these insights into the evaluation of patients enrolled in early clinical trials. In this review, we discuss emerging roles for flow cytometry in the translational setting that supports the transition and evaluation of novel compounds in the clinic.
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Affiliation(s)
| | - Charles Sinclair
- Flagship Pioneering, 140 First Street, Cambridge, MA 02141, USA;
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Li P, Jiang Z, Liu T, Liu X, Qiao H, Yao X. Improving drug response prediction via integrating gene relationships with deep learning. Brief Bioinform 2024; 25:bbae153. [PMID: 38600666 PMCID: PMC11006795 DOI: 10.1093/bib/bbae153] [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: 12/26/2023] [Revised: 03/05/2024] [Accepted: 03/18/2024] [Indexed: 04/12/2024] Open
Abstract
Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.
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Affiliation(s)
- Pengyong Li
- School of Computer Science and Technology,Xidian University, 710126 Xi’an, Shaanxi, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, 519020 Macau, China
| | - Zhengxiang Jiang
- School of Electronic Engineering, Xidian University, 710126 Xi’an, Shaanxi, China
| | - Tianxiao Liu
- School of Computer Science and Technology,Xidian University, 710126 Xi’an, Shaanxi, China
| | - Xinyu Liu
- Beijing Laboratory of Biomedical Materials, Department of Geriatric Dentistry, Peking University School and Hospital of Stomatology, 100081 Beijing, China
| | - Hui Qiao
- Department of Oncology, Tai’an Municipal Hospital, 271021 Tai’an, Shandong, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078 Macao, China
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Ermini L, Driguez P. The Application of Long-Read Sequencing to Cancer. Cancers (Basel) 2024; 16:1275. [PMID: 38610953 PMCID: PMC11011098 DOI: 10.3390/cancers16071275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Cancer is a multifaceted disease arising from numerous genomic aberrations that have been identified as a result of advancements in sequencing technologies. While next-generation sequencing (NGS), which uses short reads, has transformed cancer research and diagnostics, it is limited by read length. Third-generation sequencing (TGS), led by the Pacific Biosciences and Oxford Nanopore Technologies platforms, employs long-read sequences, which have marked a paradigm shift in cancer research. Cancer genomes often harbour complex events, and TGS, with its ability to span large genomic regions, has facilitated their characterisation, providing a better understanding of how complex rearrangements affect cancer initiation and progression. TGS has also characterised the entire transcriptome of various cancers, revealing cancer-associated isoforms that could serve as biomarkers or therapeutic targets. Furthermore, TGS has advanced cancer research by improving genome assemblies, detecting complex variants, and providing a more complete picture of transcriptomes and epigenomes. This review focuses on TGS and its growing role in cancer research. We investigate its advantages and limitations, providing a rigorous scientific analysis of its use in detecting previously hidden aberrations missed by NGS. This promising technology holds immense potential for both research and clinical applications, with far-reaching implications for cancer diagnosis and treatment.
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Affiliation(s)
- Luca Ermini
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, L-1210 Luxembourg, Luxembourg
| | - Patrick Driguez
- Bioscience Core Lab, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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Luo C, Peng Y, Gu J, Li T, Wang Q, Qi X, Wei A. Single-cell RNA sequencing reveals critical modulators of extracellular matrix of penile cavernous cells in erectile dysfunction. Sci Rep 2024; 14:5886. [PMID: 38467692 PMCID: PMC10928087 DOI: 10.1038/s41598-024-56428-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 03/06/2024] [Indexed: 03/13/2024] Open
Abstract
Erectile dysfunction (ED) is a common and difficult to treat disease, and has a high incidence rate worldwide. As a marker of vascular disease, ED usually occurs in cardiovascular disease, 2-5 years prior to cardiovascular disease events. The extracellular matrix (ECM) network plays a crucial role in maintaining cardiac homeostasis, not only by providing structural support, but also by promoting force transmission, and by transducing key signals to intracardiac cells. However, the relationship between ECM and ED remains unclear. To help fill this gap, we profiled single-cell RNA-seq (scRNA-seq) to obtain transcriptome maps of 82,554 cavernous single cells from ED and non-ED samples. Cellular composition of cavernous tissues was explored by uniform manifold approximation and projection. Pseudo-time cell trajectory combined with gene enrichment analysis were performed to unveil the molecular pathways of cell fate determination. The relationship between cavernous cells and the ECM, and the changes in related genes were elucidated. The CellChat identified ligand-receptor pairs (e.g., PTN-SDC2, PTN-NCL, and MDK-SDC2) among the major cell types in the cavernous tissue microenvironment. Differential analysis revealed that the cell type-specific transcriptomic changes in ED are related to ECM and extracellular structure organization, external encapsulating structure organization, and regulation of vasculature development. Trajectory analysis predicted the underlying target genes to modulate ECM (e.g., COL3A1, MDK, MMP2, and POSTN). Together, this study highlights potential cell-cell interactions and the main regulatory factors of ECM, and reveals that genes may represent potential marker features of ED progression.
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Affiliation(s)
- Chao Luo
- Department of Urology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, No. 9 Beijing Road, Yunyan District, Guiyang City, 550004, Guizhou Province, China
| | - Yaqian Peng
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, No. 9 Beijing Road, Yunyan District, Guiyang City, 550004, Guizhou Province, China
| | - Jiang Gu
- Department of Urology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Tao Li
- Department of Urology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Qiang Wang
- Department of Urology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Xiaolan Qi
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, No. 9 Beijing Road, Yunyan District, Guiyang City, 550004, Guizhou Province, China.
| | - Anyang Wei
- Department of Urology, Nanfang Hospital, Southern Medical University, No.1838 Guangzhou Avenue North, Guangzhou City, Guangdong Province, China.
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Gezelius H, Enblad AP, Lundmark A, Åberg M, Blom K, Rudfeldt J, Raine A, Harila A, Rendo V, Heinäniemi M, Andersson C, Nordlund J. Comparison of high-throughput single-cell RNA-seq methods for ex vivo drug screening. NAR Genom Bioinform 2024; 6:lqae001. [PMID: 38288374 PMCID: PMC10823582 DOI: 10.1093/nargab/lqae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/22/2023] [Accepted: 01/24/2024] [Indexed: 01/31/2024] Open
Abstract
Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+ Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27 327 cells are freely available to extend to future scRNA-seq methodological comparisons.
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Affiliation(s)
- Henrik Gezelius
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
| | - Anna Pia Enblad
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
- Department of Women's and Children's Health, Uppsala University, Uppsala 751 85, Sweden
| | - Anders Lundmark
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
| | - Martin Åberg
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
- Department of Clinical Chemistry and Pharmacology, Uppsala University Hospital, Uppsala 751 85, Sweden
| | - Kristin Blom
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
- Department of Clinical Chemistry and Pharmacology, Uppsala University Hospital, Uppsala 751 85, Sweden
| | - Jakob Rudfeldt
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
- Department of Clinical Chemistry and Pharmacology, Uppsala University Hospital, Uppsala 751 85, Sweden
| | - Amanda Raine
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
| | - Arja Harila
- Department of Women's and Children's Health, Uppsala University, Uppsala 751 85, Sweden
| | - Verónica Rendo
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala 751 85, Sweden
| | - Merja Heinäniemi
- School of Medicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - Claes Andersson
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
- Department of Clinical Chemistry and Pharmacology, Uppsala University Hospital, Uppsala 751 85, Sweden
| | - Jessica Nordlund
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala 751 85, Sweden
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Gianopoulos I, Daskalopoulou SS. Macrophage profiling in atherosclerosis: understanding the unstable plaque. Basic Res Cardiol 2024; 119:35-56. [PMID: 38244055 DOI: 10.1007/s00395-023-01023-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: 08/09/2023] [Revised: 11/01/2023] [Accepted: 11/01/2023] [Indexed: 01/22/2024]
Abstract
The development and rupture of atherosclerotic plaques is a major contributor to myocardial infarctions and ischemic strokes. The dynamic evolution of the plaque is largely attributed to monocyte/macrophage functions, which respond to various stimuli in the plaque microenvironment. To this end, macrophages play a central role in atherosclerotic lesions through the uptake of oxidized low-density lipoprotein that gets trapped in the artery wall, and the induction of an inflammatory response that can differentially affect the stability of the plaque in men and women. In this environment, macrophages can polarize towards pro-inflammatory M1 or anti-inflammatory M2 phenotypes, which represent the extremes of the polarization spectrum that include Mhem, M(Hb), Mox, and M4 populations. However, this traditional macrophage model paradigm has been redefined to include numerous immune and nonimmune cell clusters based on in-depth unbiased single-cell approaches. The goal of this review is to highlight (1) the phenotypic and functional properties of monocyte subsets in the circulation, and macrophage populations in atherosclerotic plaques, as well as their contribution towards stable or unstable phenotypes in men and women, and (2) single-cell RNA sequencing studies that have advanced our knowledge of immune, particularly macrophage signatures present in the atherosclerotic niche. We discuss the importance of performing high-dimensional approaches to facilitate the development of novel sex-specific immunotherapies that aim to reduce the risk of cardiovascular events.
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Affiliation(s)
- Ioanna Gianopoulos
- Division of Experimental Medicine, Department of Medicine, Faculty of Medicine and Health Sciences, Research Institute of the McGill University Health Centre, McGill University, Montreal, Canada
| | - Stella S Daskalopoulou
- Division of Experimental Medicine, Department of Medicine, Faculty of Medicine and Health Sciences, Research Institute of the McGill University Health Centre, McGill University, Montreal, Canada.
- Division of Internal Medicine, Department of Medicine, Faculty of Medicine and Health Sciences, McGill University Health Centre, McGill University, Montreal, Canada.
- Department of Medicine, Research Institute of the McGill University Health Centre, Glen Site, 1001 Decarie Boulevard, EM1.2210, Montreal, Quebec, H4A 3J1, Canada.
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He Z, Chen Q, Wang K, Lin J, Peng Y, Zhang J, Yan X, Jie Y. Single-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. Eur J Neurosci 2024; 59:333-357. [PMID: 38221677 DOI: 10.1111/ejn.16242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 12/04/2023] [Accepted: 12/12/2023] [Indexed: 01/16/2024]
Abstract
Single-cell transcriptomics analysis is an advanced technology that can describe the intracellular transcriptome in complex tissues. It profiles and analyses datasets by single-cell RNA sequencing. Neurodegenerative diseases are identified by the abnormal apoptosis of neurons in the brain with few or no effective therapy strategies at present, which has been a growing healthcare concern and brought a great burden to society. The transcriptome of individual cells provides deep insights into previously unforeseen cellular heterogeneity and gene expression differences in neurodegenerative disorders. It detects multiple cell subsets and functional changes during pathological progression, which deepens the understanding of the molecular underpinnings and cellular basis of neurodegenerative diseases. Furthermore, the transcriptome analysis of immune cells shows the regulation of immune response. Different subtypes of immune cells and their interaction are found to contribute to disease progression. This finding enables the discovery of novel targets and biomarkers for early diagnosis. In this review, we emphasize the principles of the technology, and its recent progress in the study of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. The application of single-cell transcriptomics analysis in neurodegenerative disorders would help explore the pathogenesis of these diseases and develop novel therapeutic methods.
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Affiliation(s)
- Ziping He
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Clinical Medicine Eight-Year Program, Xiangya School of Medicine, Central South University, Changsha, China
| | - Qianqian Chen
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Kaiyue Wang
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Clinical Medicine Eight-Year Program, Xiangya School of Medicine, Central South University, Changsha, China
| | - Jiang Lin
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Yilin Peng
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Jinlong Zhang
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Department of Forensic Science, School of Basic Medical Science, Xinjiang Medical University, Urumqi, China
| | - Xisheng Yan
- Department of Cardiovascular Medicine, Wuhan Third Hospital & Tongren Hospital of Wuhan University, Wuhan, China
| | - Yan Jie
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Department of Forensic Science, School of Basic Medical Science, Xinjiang Medical University, Urumqi, China
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Murao A, Jha A, Aziz M, Wang P. Transcriptomic profiling of immune cells in murine polymicrobial sepsis. Front Immunol 2024; 15:1347453. [PMID: 38343542 PMCID: PMC10853340 DOI: 10.3389/fimmu.2024.1347453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/09/2024] [Indexed: 02/15/2024] Open
Abstract
Introduction Various immune cell types play critical roles in sepsis with numerous distinct subsets exhibiting unique phenotypes even within the same cell population. Single-cell RNA sequencing (scRNA-seq) enables comprehensive transcriptome profiling and unbiased cell classification. In this study, we have unveiled the transcriptomic landscape of immune cells in sepsis through scRNA-seq analysis. Methods We induced sepsis in mice by cecal ligation and puncture. 20 h after the surgery, the spleen and peritoneal lavage were collected. Single-cell suspensions were processed using a 10× Genomics pipeline and sequenced on an Illumina platform. Count matrices were generated using the Cell Ranger pipeline, which maps reads to the mouse reference transcriptome, GRCm38/mm10. Subsequent scRNA-seq analysis was performed using the R package Seurat. Results After quality control, we subjected the entire data set to unsupervised classification. Four major clusters were identified as neutrophils, macrophages, B cells, and T cells according to their putative markers. Based on the differentially expressed genes, we identified activated pathways in sepsis for each cell type. In neutrophils, pathways related to inflammatory signaling, such as NF-κB and responses to pathogen-associated molecular patterns (PAMPs), cytokines, and hypoxia were activated. In macrophages, activated pathways were the ones related to cell aging, inflammatory signaling, and responses to PAMPs. In B cells, pathways related to endoplasmic reticulum stress were activated. In T cells, activated pathways were the ones related to inflammatory signaling, responses to PAMPs, and acute lung injury. Next, we further classified each cell type into subsets. Neutrophils consisted of four clusters. Some subsets were activated in inflammatory signaling or cell metabolism, whereas others possessed immunoregulatory or aging properties. Macrophages consisted of four clusters, namely, the ones with enhanced aging, lymphocyte activation, extracellular matrix organization, or cytokine activity. B cells consisted of four clusters, including the ones possessing the phenotype of cell maturation or aging. T cells consisted of six clusters, whose phenotypes include molecular translocation or cell activation. Conclusions Transcriptomic analysis by scRNA-seq has unveiled a comprehensive spectrum of immune cell responses and distinct subsets in the context of sepsis. These findings are poised to enhance our understanding of sepsis pathophysiology, offering avenues for targeting novel molecules, cells, and pathways to combat infectious diseases.
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Affiliation(s)
- Atsushi Murao
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Alok Jha
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Monowar Aziz
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
- Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States
| | - Ping Wang
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
- Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States
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Liu GY, Yu D, Fan MM, Zhang X, Jin ZY, Tang C, Liu XF. Antimicrobial resistance crisis: could artificial intelligence be the solution? Mil Med Res 2024; 11:7. [PMID: 38254241 PMCID: PMC10804841 DOI: 10.1186/s40779-024-00510-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.
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Affiliation(s)
- Guang-Yu Liu
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Dan Yu
- National Key Discipline of Pediatrics Key Laboratory of Major Diseases in Children Ministry of Education, Laboratory of Dermatology, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Mei-Mei Fan
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ze-Yu Jin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christoph Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK.
| | - Xiao-Fen Liu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Key Laboratory of Clinical Pharmacology of Antibiotics, National Health Commission of the People's Republic of China, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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Thakur M, Buniello A, Brooksbank C, Gurwitz KT, Hall M, Hartley M, Hulcoop DG, Leach AR, Marques D, Martin M, Mithani A, McDonagh EM, Mutasa-Gottgens E, Ochoa D, Perez-Riverol Y, Stephenson J, Varadi M, Velankar S, Vizcaino JA, Witham R, McEntyre J. EMBL's European Bioinformatics Institute (EMBL-EBI) in 2023. Nucleic Acids Res 2024; 52:D10-D17. [PMID: 38015445 PMCID: PMC10767983 DOI: 10.1093/nar/gkad1088] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/23/2023] [Accepted: 10/30/2023] [Indexed: 11/29/2023] Open
Abstract
The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the latest developments in the services provided by EMBL-EBI data resources to scientific communities globally. These developments aim to ensure EMBL-EBI resources meet the current and future needs of these scientific communities, accelerating the impact of open biological data for all.
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Affiliation(s)
- Matthew Thakur
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Annalisa Buniello
- Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Catherine Brooksbank
- Training Team, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Kim T Gurwitz
- Training Team, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Matthew Hall
- Industry Partnerships, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Matthew Hartley
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - David G Hulcoop
- Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Andrew R Leach
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
- Industry Partnerships, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Diana Marques
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Maria Martin
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Aziz Mithani
- Training Team, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Ellen M McDonagh
- Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Euphemia Mutasa-Gottgens
- Industry Partnerships, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - David Ochoa
- Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Yasset Perez-Riverol
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - James Stephenson
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Mihaly Varadi
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Sameer Velankar
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Juan Antonio Vizcaino
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Rick Witham
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Johanna McEntyre
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
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Curry AR, Ooi L, Matosin N. How spatial omics approaches can be used to map the biological impacts of stress in psychiatric disorders: a perspective, overview and technical guide. Stress 2024; 27:2351394. [PMID: 38752853 DOI: 10.1080/10253890.2024.2351394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 04/29/2024] [Indexed: 05/21/2024] Open
Abstract
Exposure to significant levels of stress and trauma throughout life is a leading risk factor for the development of major psychiatric disorders. Despite this, we do not have a comprehensive understanding of the mechanisms that explain how stress raises psychiatric disorder risk. Stress in humans is complex and produces variable molecular outcomes depending on the stress type, timing, and duration. Deciphering how stress increases disorder risk has consequently been challenging to address with the traditional single-target experimental approaches primarily utilized to date. Importantly, the molecular processes that occur following stress are not fully understood but are needed to find novel treatment targets. Sequencing-based omics technologies, allowing for an unbiased investigation of physiological changes induced by stress, are rapidly accelerating our knowledge of the molecular sequelae of stress at a single-cell resolution. Spatial multi-omics technologies are now also emerging, allowing for simultaneous analysis of functional molecular layers, from epigenome to proteome, with anatomical context. The technology has immense potential to transform our understanding of how disorders develop, which we believe will significantly propel our understanding of how specific risk factors, such as stress, contribute to disease course. Here, we provide our perspective of how we believe these technologies will transform our understanding of the neurobiology of stress, and also provided a technical guide to assist molecular psychiatry and stress researchers who wish to implement spatial omics approaches in their own research. Finally, we identify potential future directions using multi-omics technology in stress research.
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Affiliation(s)
- Amber R Curry
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
- Molecular Horizons, School of Chemistry and Molecular Bioscience, Faculty of Science Medicine and Health, University of Wollongong, Wollongong, NSW, Australia
| | - Lezanne Ooi
- Molecular Horizons, School of Chemistry and Molecular Bioscience, Faculty of Science Medicine and Health, University of Wollongong, Wollongong, NSW, Australia
| | - Natalie Matosin
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
- Molecular Horizons, School of Chemistry and Molecular Bioscience, Faculty of Science Medicine and Health, University of Wollongong, Wollongong, NSW, Australia
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