1
|
Weng C, Groh AMR, Yaqubi M, Cui QL, Stratton JA, Moore GRW, Antel JP. Heterogeneity of mature oligodendrocytes in the central nervous system. Neural Regen Res 2025; 20:1336-1349. [PMID: 38934385 DOI: 10.4103/nrr.nrr-d-24-00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Mature oligodendrocytes form myelin sheaths that are crucial for the insulation of axons and efficient signal transmission in the central nervous system. Recent evidence has challenged the classical view of the functionally static mature oligodendrocyte and revealed a gamut of dynamic functions such as the ability to modulate neuronal circuitry and provide metabolic support to axons. Despite the recognition of potential heterogeneity in mature oligodendrocyte function, a comprehensive summary of mature oligodendrocyte diversity is lacking. We delve into early 20 th -century studies by Robertson and Río-Hortega that laid the foundation for the modern identification of regional and morphological heterogeneity in mature oligodendrocytes. Indeed, recent morphologic and functional studies call into question the long-assumed homogeneity of mature oligodendrocyte function through the identification of distinct subtypes with varying myelination preferences. Furthermore, modern molecular investigations, employing techniques such as single cell/nucleus RNA sequencing, consistently unveil at least six mature oligodendrocyte subpopulations in the human central nervous system that are highly transcriptomically diverse and vary with central nervous system region. Age and disease related mature oligodendrocyte variation denotes the impact of pathological conditions such as multiple sclerosis, Alzheimer's disease, and psychiatric disorders. Nevertheless, caution is warranted when subclassifying mature oligodendrocytes because of the simplification needed to make conclusions about cell identity from temporally confined investigations. Future studies leveraging advanced techniques like spatial transcriptomics and single-cell proteomics promise a more nuanced understanding of mature oligodendrocyte heterogeneity. Such research avenues that precisely evaluate mature oligodendrocyte heterogeneity with care to understand the mitigating influence of species, sex, central nervous system region, age, and disease, hold promise for the development of therapeutic interventions targeting varied central nervous system pathology.
Collapse
Affiliation(s)
- Chao Weng
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Adam M R Groh
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Moein Yaqubi
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Qiao-Ling Cui
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jo Anne Stratton
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - G R Wayne Moore
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jack P Antel
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| |
Collapse
|
2
|
Zhou S, Lin N, Yu L, Su X, Liu Z, Yu X, Gao H, Lin S, Zeng Y. Single-cell multi-omics in the study of digestive system cancers. Comput Struct Biotechnol J 2024; 23:431-445. [PMID: 38223343 PMCID: PMC10787224 DOI: 10.1016/j.csbj.2023.12.007] [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] [Received: 08/04/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 01/16/2024] Open
Abstract
Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.
Collapse
Affiliation(s)
- Shuang Zhou
- The Second Clinical Medical School of Fujian Medical University, Quanzhou, Fujian Province, China
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Nanfei Lin
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Liying Yu
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Xiaoshan Su
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Respirology Medicine Centre of Fujian Province, Quanzhou, China
| | - Zhenlong Liu
- Lady Davis Institute for Medical Research, Jewish General Hospital, & Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC, Canada
| | - Xiaowan Yu
- Clinical Laboratory, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Hongzhi Gao
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Shu Lin
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- Diabetes and Metabolism Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, NSW 2010, Australia
| | - Yiming Zeng
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Respirology Medicine Centre of Fujian Province, Quanzhou, China
- Fujian Provincial Key Laboratory of Lung Stem Cells, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan, Shandong Province, China
| |
Collapse
|
3
|
Cheng X, Meng X, Chen R, Song Z, Li S, Wei S, Lv H, Zhang S, Tang H, Jiang Y, Zhang R. The molecular subtypes of autoimmune diseases. Comput Struct Biotechnol J 2024; 23:1348-1363. [PMID: 38596313 PMCID: PMC11001648 DOI: 10.1016/j.csbj.2024.03.026] [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] [Received: 11/12/2023] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/11/2024] Open
Abstract
Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.
Collapse
Affiliation(s)
| | | | | | - Zerun Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuai Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuhao Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hao Tang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| |
Collapse
|
4
|
Yao Y, Tian G, Zhang J, Zhang S, Liu X, Hou J. Integrating bulk and single-cell sequencing reveals metastasis-associated CAFs in head and neck squamous cell carcinoma. Life Sci 2024; 351:122768. [PMID: 38851417 DOI: 10.1016/j.lfs.2024.122768] [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: 02/16/2024] [Revised: 05/18/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
AIMS Cancer-associated fibroblasts (CAFs) have been shown to promote the metastasis of head and neck squamous cell carcinoma (HNSCC), but the underlying mechanisms remain unclear. The purpose of this study is to identify gene in CAFs that are associated with metastasis and to preliminarily validate its impact on the metastasis of HNSCC. MATERIALS AND METHODS Scissor analysis was utilized to process single-cell and bulk RNA sequencing datasets, identifying genes associated with the metastasis of HNSCC through differential gene expression analysis. A risk model was constructed using LASSO regression analysis. Quantitative real time-PCR and Western blot were employed to measure mRNA and protein expressions, respectively. Multiplex immunohistochemistry (mIHC) was used to assess protein expression in CAFs. siRNA was utilized to achieve gene knockdown. CCK-8 and Transwell assays were employed to evaluate the biological characteristics of HNSCC cells. KEY FINDINGS Compare to the nonmetastatic primary CAFs (nmCAFs), tissue inhibitors of metalloproteinase-1 (TIMP1) was founded to be overexpressed in the cells and tissues of metastatic primary CAFs (mCAFs). Knocking down TIMP1 in CAFs can signifucantly inhibit the proliferation, invasion, and migration of HNSCC cells. SIGNIFICANCE CAFs facilitate HNSCC cell metastasis by upregulating TIMP1, highlighting TIMP1 as a potential therapeutic target in HNSCC metastasis management.
Collapse
Affiliation(s)
- Yihuan Yao
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, 56 Ling-yuan west Street, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Zhong Shan Er Road 74, Guangzhou 510080, China
| | - Guoli Tian
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, 56 Ling-yuan west Street, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Zhong Shan Er Road 74, Guangzhou 510080, China
| | - Jiaqiang Zhang
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, 56 Ling-yuan west Street, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Zhong Shan Er Road 74, Guangzhou 510080, China
| | - Shuaiyuan Zhang
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, 56 Ling-yuan west Street, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Zhong Shan Er Road 74, Guangzhou 510080, China
| | - Xiaoyong Liu
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, 56 Ling-yuan west Street, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Zhong Shan Er Road 74, Guangzhou 510080, China
| | - Jingsong Hou
- Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, 56 Ling-yuan west Street, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Zhong Shan Er Road 74, Guangzhou 510080, China.
| |
Collapse
|
5
|
Wei C, Ma Y, Wang M, Wang S, Yu W, Dong S, Deng W, Bie L, Zhang C, Shen W, Xia Q, Luo S, Li N. Tumor-associated macrophage clusters linked to immunotherapy in a pan-cancer census. NPJ Precis Oncol 2024; 8:176. [PMID: 39117688 PMCID: PMC11310399 DOI: 10.1038/s41698-024-00660-4] [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: 11/24/2023] [Accepted: 07/17/2024] [Indexed: 08/10/2024] Open
Abstract
Transcriptional heterogeneity of tumor-associated macrophages (TAMs) has been investigated in individual cancers, but the extent to which these states transcend tumor types and represent a general feature of cancer remains unclear. We performed pan-cancer single-cell RNA sequencing analysis across nine cancer types and identified distinct monocyte/TAM composition patterns. Using spatial analysis from clinical study tissues, we assessed TAM functions in shaping the tumor microenvironment (TME) and influencing immunotherapy. Two specific TAM clusters (pro-inflammatory and pro-tumor) and four TME subtypes showed distinct immunological features, genomic profiles, immunotherapy responses, and cancer prognosis. Pro-inflammatory TAMs resided in immune-enriched niches with exhausted CD8+ T cells, while pro-tumor TAMs were restricted to niches associated with a T-cell-excluded phenotype and hypoxia. We developed a machine learning model to predict immune checkpoint blockade response by integrating TAMs and clinical data. Our study comprehensively characterizes the common features of TAMs and highlights their interaction with the TME.
Collapse
Affiliation(s)
- Chen Wei
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yijie Ma
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Mengyu Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Siyi Wang
- Department of Surgical Oncology and General Surgery, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Wenyue Yu
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Shuailei Dong
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Wenying Deng
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Liangyu Bie
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Chi Zhang
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Wei Shen
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Qingxin Xia
- Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
| | - Suxia Luo
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
| | - Ning Li
- Department of Internal Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
| |
Collapse
|
6
|
Liu Q, Niu Y, Pei Z, Yang Y, Xie Y, Wang M, Wang J, Wu M, Zheng J, Yang P, Hao H, Pang Y, Bao L, Dai Y, Niu Y, Zhang R. Gas6-Axl signal promotes indoor VOCs exposure-induced pulmonary fibrosis via pulmonary microvascular endothelial cells-fibroblasts cross-talk. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134786. [PMID: 38824778 DOI: 10.1016/j.jhazmat.2024.134786] [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: 03/22/2024] [Revised: 05/14/2024] [Accepted: 05/30/2024] [Indexed: 06/04/2024]
Abstract
Volatile organic compounds (VOCs) as environmental pollutants were associated with respiratory diseases. Pulmonary fibrosis (PF) was characterized by an increase of extracellular matrix, leading to deterioration of lung function. The adverse effects on lung and the potential mechanism underlying VOCs induced PF had not been elucidated clearly. In this study, the indoor VOCs exposure mouse model along with an ex vivo biosensor assay was established. Based on scRNA-seq analysis, the adverse effects on lung and potential molecular mechanism were studied. Herein, the results showed that VOCs exposure from indoor decoration contributed to decreased lung function and facilitated pulmonary fibrosis in mice. Then, the whole lung cell atlas after VOCs exposure and the heterogeneity of fibroblasts were revealed. We explored the molecular interactions among various pulmonary cells, suggesting that endothelial cells contributed to fibroblasts activation in response to VOCs exposure. Mechanistically, pulmonary microvascular endothelial cells (MPVECs) secreted Gas6 after VOCs-induced PANoptosis phenotype, bound to the Axl in fibroblasts, and then activated fibroblasts. Moreover, Atf3 as the key gene negatively regulated PANoptosis phenotype to ameliorate fibrosis induced by VOCs exposure. These novel findings provided a new perspective about MPVECs could serve as the initiating factor of PF induced by VOCs exposure.
Collapse
Affiliation(s)
- Qingping Liu
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Yong Niu
- Key Laboratory of Chemical Safety and Health, National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Zijie Pei
- Department of Thoracic Surgery, the 2nd Hospital of Hebei Medical University, Shijiazhuang 050017, PR China
| | - Yizhe Yang
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Yujia Xie
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Mengruo Wang
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Jingyuan Wang
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Mengqi Wu
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Jie Zheng
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Peihao Yang
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Haiyan Hao
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China; Hebei Province Center for Disease Control and Prevention, Shijiazhuang 050021, Hebei, PR China
| | - Yaxian Pang
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China; Hebei Key Laboratory of Environment and Human Health, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Lei Bao
- Hebei Key Laboratory of Environment and Human Health, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China; Occupational Health and Environmental Health, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Yufei Dai
- Key Laboratory of Chemical Safety and Health, National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Yujie Niu
- Hebei Key Laboratory of Environment and Human Health, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China; Occupational Health and Environmental Health, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China
| | - Rong Zhang
- Department of Toxicology, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China; Hebei Key Laboratory of Environment and Human Health, Hebei Medical University, Shijiazhuang 050017, Hebei, PR China.
| |
Collapse
|
7
|
Zhang Y, Zhang C, He J, Lai G, Li W, Zeng H, Zhong X, Xie B. Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy. Inflamm Res 2024; 73:1393-1409. [PMID: 38896289 DOI: 10.1007/s00011-024-01905-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 06/07/2024] [Accepted: 06/09/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized. METHODS We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database. RESULTS We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups. CONCLUSION Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.
Collapse
Affiliation(s)
- Yuan Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Cong Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Jing He
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Guichuan Lai
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Wenlong Li
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Haijiao Zeng
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Xiaoni Zhong
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China.
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China.
| | - Biao Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Yixue Road, Chongqing, 400016, China.
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China.
| |
Collapse
|
8
|
Ghosh G, Shannon AE, Searle BC. Data acquisition approaches for single cell proteomics. Proteomics 2024:e2400022. [PMID: 39088833 DOI: 10.1002/pmic.202400022] [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: 05/18/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/03/2024]
Abstract
Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.
Collapse
Affiliation(s)
- Gautam Ghosh
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Ariana E Shannon
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
| | - Brian C Searle
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
| |
Collapse
|
9
|
Wang Q, Song X, Zhang Y, Liang S, Zhang M, Wang H, Feng Y, Li R, Ding H, Chen Y, Xia W, Dong G, Xu L, Mao Q, Jiang F. A pro-metastatic tRNA fragment drives aldolase A oligomerization to enhance aerobic glycolysis in lung adenocarcinoma. Cell Rep 2024; 43:114550. [PMID: 39058593 DOI: 10.1016/j.celrep.2024.114550] [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: 03/26/2024] [Revised: 06/19/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
Despite being the leading cause of lung cancer-related deaths, the underlying molecular mechanisms driving metastasis progression are still not fully understood. Transfer RNA-derived fragments (tRFs) have been implicated in various biological processes in cancer. However, the role of tRFs in lung adenocarcinoma (LUAD) remains unclear. Our study identified a tRF, tRF-Val-CAC-024, associated with the high-risk component of LUAD, through validation using 3 cohorts. Our findings demonstrated that tRF-Val-CAC-024 acts as an oncogene in LUAD. Mechanistically, tRF-Val-CAC-024 was revealed to bind to aldolase A (ALDOA) dependent on Q125/E224 and promote the oligomerization of ALDOA, resulting in increased enzyme activity and enhanced aerobic glycolysis in LUAD cells. Additionally, we provide preliminary evidence of its potential clinical value by investigating the therapeutic effects of tRF-Val-CAC-024 antagomir-loaded lipid nanoparticles (LNPs) in cell-line-derived xenograft models. These results could enhance our understanding of the regulatory mechanisms of tRFs in LUAD and provide a potential therapeutic target.
Collapse
Affiliation(s)
- Qinglin Wang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Xuming Song
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Yijian Zhang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Si Liang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Minhao Zhang
- Department of Anesthesiology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and The Affiliated Cancer Hospital of Nanjing Medical University, No. 42, Baiziting, Nanjing 210000, Jiangsu, China
| | - Hui Wang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Yipeng Feng
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Rutao Li
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Hanlin Ding
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Yuzhong Chen
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Wenjie Xia
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Gaochao Dong
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Lin Xu
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China.
| | - Qixing Mao
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China.
| | - Feng Jiang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China.
| |
Collapse
|
10
|
Shin SW, Mudvari P, Thaploo S, Wheeler MA, Douek DC, Quintana FJ, Boritz EA, Abate AR, Clark IC. FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics. Nat Protoc 2024:10.1038/s41596-024-01021-y. [PMID: 39039320 DOI: 10.1038/s41596-024-01021-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 05/09/2024] [Indexed: 07/24/2024]
Abstract
Rare cells have an important role in development and disease, and methods for isolating and studying cell subsets are therefore an essential part of biology research. Such methods traditionally rely on labeled antibodies targeted to cell surface proteins, but large public databases and sophisticated computational approaches increasingly define cell subsets on the basis of genomic, epigenomic and transcriptomic sequencing data. Methods for isolating cells on the basis of nucleic acid sequences powerfully complement these approaches by providing experimental access to cell subsets discovered in cell atlases, as well as those that cannot be otherwise isolated, including cells infected with pathogens, with specific DNA mutations or with unique transcriptional or splicing signatures. We recently developed a nucleic acid cytometry platform called 'focused interrogation of cells by nucleic acid detection and sequencing' (FIND-seq), capable of isolating rare cells on the basis of RNA or DNA markers, followed by bulk or single-cell transcriptomic analysis. This platform has previously been used to characterize the splicing-dependent activation of the transcription factor XBP1 in astrocytes and HIV persistence in memory CD4 T cells from people on long-term antiretroviral therapy. Here, we outline the molecular and microfluidic steps involved in performing FIND-seq, including protocol updates that allow detection and whole transcriptome sequencing of rare HIV-infected cells that harbor genetically intact virus genomes. FIND-seq requires knowledge of microfluidics, optics and molecular biology. We expect that FIND-seq, and this comprehensive protocol, will enable mechanistic studies of rare HIV+ cells, as well as other cell subsets that were previously difficult to recover and sequence.
Collapse
Affiliation(s)
- Seung Won Shin
- Department of Bioengineering, College of Engineering, California Institute for Quantitative Biosciences (QB3), University of California Berkeley, Berkeley, CA, USA
| | - Prakriti Mudvari
- Virus Persistence and Dynamics Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Shravan Thaploo
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael A Wheeler
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel C Douek
- Human Immunology Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Francisco J Quintana
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Eli A Boritz
- Virus Persistence and Dynamics Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Adam R Abate
- Department of Bioengineering and Therapeutic Sciences, School of Pharmacy, University of California San Francisco, San Francisco, CA, USA
| | - Iain C Clark
- Department of Bioengineering, College of Engineering, California Institute for Quantitative Biosciences (QB3), University of California Berkeley, Berkeley, CA, USA.
| |
Collapse
|
11
|
Zhang Z, Chen M, Peng X. Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on drug response genes to predict prognosis and therapeutic response in ovarian cancer. Heliyon 2024; 10:e33367. [PMID: 39040239 PMCID: PMC11260940 DOI: 10.1016/j.heliyon.2024.e33367] [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] [Received: 03/30/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/24/2024] Open
Abstract
Ovarian cancer represents a severe gynecological malignancy with a dire prognosis, underscoring the imperative need for dependable biomarkers that can accurately predict drug response and guide therapeutic choices. In this study, we harnessed online single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) datasets, applying the Scissor algorithm to identify cells responsive to paclitaxel. From these cells, we derived a gene signature, subsequently used to construct a prognostic model that demonstrated high sensitivity and specificity in predicting patient outcomes. Moreover, we conducted pathway and functional enrichment analyses to uncover potential molecular mechanisms driving the prognostic gene signature. This study illustrates the critical role of scRNAseq and bulk RNAseq in developing precise prognostic models for ovarian cancer, potentially transforming clinical decision-making.
Collapse
Affiliation(s)
- ZhenWei Zhang
- Jinjiang Municipal Hospital(Shanghai Sixth People's Hospital Fujian Campus), No. 16, Luoshan Section, Jinguang Road, Luoshan Street, Jinjiang City, Quanzhou, Fujian, China
| | - MianMian Chen
- Jinjiang Municipal Hospital(Shanghai Sixth People's Hospital Fujian Campus), No. 16, Luoshan Section, Jinguang Road, Luoshan Street, Jinjiang City, Quanzhou, Fujian, China
| | - XiaoLian Peng
- Jinjiang Municipal Hospital(Shanghai Sixth People's Hospital Fujian Campus), No. 16, Luoshan Section, Jinguang Road, Luoshan Street, Jinjiang City, Quanzhou, Fujian, China
| |
Collapse
|
12
|
Lin BB, Huang Q, Yan B, Liu M, Zhang Z, Lei H, Huang R, Dong JT, Pang J. An 18-gene signature of recurrence-associated endothelial cells predicts tumor progression and castration resistance in prostate cancer. Br J Cancer 2024:10.1038/s41416-024-02761-0. [PMID: 38997406 DOI: 10.1038/s41416-024-02761-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 06/08/2024] [Accepted: 06/11/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND The prognostic and therapeutic implications of endothelial cells (ECs) heterogeneity in prostate cancer (PCa) are poorly understood. METHODS We investigated associations of EC heterogeneity with PCa recurrence and castration resistance in 8 bulk transcriptomic and 4 single-cell RNA-seq cohorts. A recurrence-associated EC (RAEC) signature was constructed by comparing 11 machine learning algorithms through nested cross-validation. Functional relevances of RAEC-specific genes were also tested. RESULTS A subset of ECs was significantly associated with recurrence in primary PCa and named RAECs. RAECs were characteristic of tip and immature cells and were enriched in migration, angiogenesis, and collagen-related pathways. We then developed an 18-gene RAEC signature (RAECsig) representative of RAECs. Higher RAECsig scores independently predicted tumor recurrence and performed better or comparably compared to clinicopathological factors and commercial gene signatures in multiple PCa cohorts. Of the 18 RAECsig genes, FSCN1 was upregulated in ECs from PCa with higher Gleason scores; and the silencing of FSCN1, TMEME255B, or GABRD in ECs either attenuated tube formation or inhibited PCa cell proliferation. Finally, higher RAECsig scores predicted castration resistance in both primary and castration-resistant PCa. CONCLUSION This study establishes an endothelial signature that links a subset of ECs to prostate cancer recurrence and castration resistance.
Collapse
Affiliation(s)
- Bing-Biao Lin
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518000, China
- Department of Human Cell Biology and Genetics, School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen, 518055, China
- Department of Radiotherapy, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Qingqing Huang
- Department of Human Cell Biology and Genetics, School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen, 518055, China
| | - Binyuan Yan
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518000, China
| | - Mingcheng Liu
- Department of Human Cell Biology and Genetics, School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen, 518055, China
| | - Zhiqian Zhang
- Department of Human Cell Biology and Genetics, School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen, 518055, China
| | - Hanqi Lei
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518000, China
| | - Ronghua Huang
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515000, China
| | - Jin-Tang Dong
- Department of Human Cell Biology and Genetics, School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen, 518055, China.
| | - Jun Pang
- Department of Urology, Kidney and Urology Center, Pelvic Floor Disorders Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518000, China.
| |
Collapse
|
13
|
Yang L, Wang G, Tian H, Jia S, Wang S, Cui R, Zhuang A. RBMS1 reflects a distinct microenvironment and promotes tumor progression in ocular melanoma. Exp Eye Res 2024; 246:109990. [PMID: 38969283 DOI: 10.1016/j.exer.2024.109990] [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: 03/22/2024] [Revised: 06/25/2024] [Accepted: 07/02/2024] [Indexed: 07/07/2024]
Abstract
Ocular melanoma, including uveal melanoma (UM) and conjunctival melanoma (CM), is the most common ocular cancer among adults with a high rate of recurrence and poor prognosis. Loss of epigenetic homeostasis disturbed gene expression patterns, resulting in oncogenesis. Herein, we comprehensively analyzed the DNA methylation, transcriptome profiles, and corresponding clinical information of UM patients through multiple machine-learning algorithms, finding that a methylation-driven gene RBMS1 was correlated with poor clinical outcomes of UM patients. RNA-seq and single-cell RNA-seq analyses revealed that RBMS1 reflected diverse tumor microenvironments, where high RBMS1 expression marked an immune active TME. Furthermore, we found that tumor cells were identified to have the higher communication probability in RBMS1+ state. The functional enrichment analysis revealed that RBMS1 was associated with pigment granule and melanosome, participating in cell proliferation as well as apoptotic signaling pathway. Biological experiments were performed and demonstrated that the silencing of RBMS1 inhibited ocular melanoma proliferation and promoted apoptosis. Our study highlighted that RBMS1 reflects a distinct microenvironment and promotes tumor progression in ocular melanoma, contributing to the therapeutic customization and clinical decision-making.
Collapse
Affiliation(s)
- Ludi Yang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, 200011, PR China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, PR China
| | - Gaoming Wang
- Department of Medical Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, PR China
| | - Hao Tian
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, 200011, PR China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, PR China
| | - Shichong Jia
- Tianjin Eye Hospital, Tianjin Key Lab of Ophthalmology and Visual Science, Nankai University Affiliated Eye Hospital, Tianjin Eye Institute, Tianjin, 300020, PR China
| | - Shaoyun Wang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, 200011, PR China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, PR China.
| | - Ran Cui
- Department of Medical Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, PR China.
| | - Ai Zhuang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, 200011, PR China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, PR China.
| |
Collapse
|
14
|
Wang X, Chen B, Zhang H, Peng L, Liu X, Zhang Q, Wang X, Peng S, Wang K, Liao L. Integrative analysis identifies molecular features of fibroblast and the significance of fibrosis on neoadjuvant chemotherapy response in breast cancer. Int J Surg 2024; 110:4083-4095. [PMID: 38546506 PMCID: PMC11254208 DOI: 10.1097/js9.0000000000001360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 03/03/2024] [Indexed: 07/19/2024]
Abstract
BACKGROUND The molecular features of fibroblasts and the role of fibrosis in neoadjuvant chemotherapy (NAC) response and breast cancer (BRCA) prognosis remain unclear. Therefore, this study aimed to investigate the impact of interstitial fibrosis on the response and prognosis of patients with BRCA undergoing NAC treatment. MATERIALS AND METHODS The molecular characteristics of pathologic complete response (pCR) and non-pCR (npCR) in patients with BRCA were analyzed using multiomics analysis. A clinical cohort was collected to investigate the predictive value of fibrosis in patients with BRCA. RESULTS Fibrosis-related signaling pathways were significantly upregulated in patients with npCR. npCR may be associated with distinct and highly active fibroblast subtypes. Patients with high fibrosis had lower pCR rates. The fibrosis-dependent nomogram for pCR showed efficient predictive ability [training set: area under the curve [AUC]=0.871, validation set: AUC=0.792]. Patients with low fibrosis had a significantly better prognosis than those with high fibrosis, and those with a high fibrotic focus index had significantly shorter overall and recurrence-free survival. Therefore, fibrosis can be used to predict pCR. Our findings provide a basis for decision-making in the treatment of BRCA. CONCLUSIONS npCR is associated with a distinct and highly active fibroblast subtype. Furthermore, patients with high fibrosis have lower pCR rates and shorter long-term survival. Therefore, fibrosis can predict pCR. A nomogram that includes fibrosis can provide a basis for decision-making in the treatment of BRCA.
Collapse
Affiliation(s)
- Xiaomin Wang
- Department of Breast Surgery, Xiangya Hospital, Central South University
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital
- Clinical Research Center For Breast Cancer In Hunan Province
- Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, Hunan
| | - Bo Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University
- Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong, People’s Republic of China
| | - Hanghao Zhang
- Department of Breast Surgery, Xiangya Hospital, Central South University
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University
- Clinical Research Center For Breast Cancer In Hunan Province
| | - Lushan Peng
- Department of Pathology, Xiangya Hospital, Central South University
| | - Xiangyan Liu
- Department of Breast Surgery, Xiangya Hospital, Central South University
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University
- Clinical Research Center For Breast Cancer In Hunan Province
| | - Qian Zhang
- Department of Breast Surgery, Xiangya Hospital, Central South University
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University
- Clinical Research Center For Breast Cancer In Hunan Province
| | - Xiaoxiao Wang
- Department of Breast Surgery, Xiangya Hospital, Central South University
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University
- Clinical Research Center For Breast Cancer In Hunan Province
| | - Shuai Peng
- Department of Breast Surgery, Xiangya Hospital, Central South University
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University
- Clinical Research Center For Breast Cancer In Hunan Province
| | - Kuangsong Wang
- Department of Pathology, Xiangya Hospital, Central South University
| | - Liqiu Liao
- Department of Breast Surgery, Xiangya Hospital, Central South University
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University
- Clinical Research Center For Breast Cancer In Hunan Province
| |
Collapse
|
15
|
Zhang M, Zhang J, Hu H, Zhou Y, Lin Z, Jing H, Sun B. Multiomic analysis of monocyte-derived alveolar macrophages in idiopathic pulmonary fibrosis. J Transl Med 2024; 22:598. [PMID: 38937806 PMCID: PMC11209973 DOI: 10.1186/s12967-024-05398-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 06/13/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Monocyte-derived alveolar macrophages (Mo_AMs) are increasingly recognised as potential pathogenic factors for idiopathic pulmonary fibrosis (IPF). While scRNAseq analysis has proven valuable in the transcriptome profiling of Mo_AMs, the integration analysis of multi-omics may provide additional dimensions of understanding of these cellular populations. METHODS We performed multi-omics analysis on 116 scRNAseq, 119 bulkseq and five scATACseq lung tissue samples from IPF. We built a large-scale IPF scRNAseq atlas and conducted the Monocle 2/3 as well as the Cellchat to explore the developmental path and intercellular communication on Mo_AMs. We also reported the difference in metabolisms, tissue repair and phagocytosis between Mo_AMs and tissue-resident alveolar macrophages (TRMs). To determine whether Mo_AMs affected pulmonary function, we projected clinical phenotypes (FVC%pred) from the bulkseq dataset onto the scRNAseq atlas. Finally, we used scATATCseq to uncover the upstream regulatory mechanisms and determine key drivers in Mo_AMs. RESULTS We identified three Mo_AMs clusters and the trajectory analysis further validated the origin of these clusters. Moreover, via the Cellchat analysis, the CXCL12/CXCR4 axis was found to be involved in the molecular basis of reciprocal interactions between Mo_AMs and fibroblasts through the activation of the ERK pathway in Mo_AMs. SPP1_RecMacs (RecMacs, recruited macrophages) were higher in the low-FVC group than in the high-FVC group. Specifically, compared with TRMs, the functions of lipid and energetic metabolism as well as tissue repair were higher in Mo_AMs than TRMs. But, TRMs may have higher level of phagocytosis than TRMs. SPIB (PU.1), JUNB, JUND, BACH2, FOSL2, and SMARCC1 showed stronger association with open chromatin of Mo_AMs than TRMs. Significant upregulated expression and deep chromatin accessibility of APOE were observed in both SPP1_RecMacs and TRMs. CONCLUSION Through trajectory analysis, it was confirmed that SPP1_RecMacs derived from Monocytes. Besides, Mo_AMs may influence FVC% pred and aggravate pulmonary fibrosis through the communication with fibroblasts. Furthermore, distinctive transcriptional regulators between Mo_AMs and TRMs implied that they may depend on different upstream regulatory mechanisms. Overall, this work provides a global overview of how Mo_AMs govern IPF and also helps determine better approaches and intervention therapies.
Collapse
Affiliation(s)
- Miaomiao Zhang
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Internal Medicine II, University Hospital Bonn, Section of Pneumology, Bonn, Germany
| | - Jinghao Zhang
- Department of Respiratory and Critical Care Medicine, Xuzhou Central Hospital, Xuzhou, China
| | - Haisheng Hu
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuan Zhou
- Department of Medicine II, Heart Center Bonn, University Hospital Bonn, Bonn, Germany
| | - ZhiWei Lin
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hui Jing
- Department of Respiratory and Critical Care Medicine, Xuzhou Central Hospital, Xuzhou, China
| | - Baoqing Sun
- Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Guangzhou Laboratory, Guangzhou, 510005, China.
| |
Collapse
|
16
|
Gao J, Song X, Ou H, Cheng X, Zhang L, Liu C, Dong Y, Wang X. The association between vitamin D and the progression of diabetic nephropathy: insights into potential mechanisms. Front Med (Lausanne) 2024; 11:1388074. [PMID: 38978780 PMCID: PMC11228314 DOI: 10.3389/fmed.2024.1388074] [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: 02/19/2024] [Accepted: 06/05/2024] [Indexed: 07/10/2024] Open
Abstract
Aims Vitamin D deficiency (VDD) is prevalent in the population, with inadequate intake, impaired absorption and metabolism as the main causative factors. VDD increases the risk of developing chronic diseases such as type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN), but the molecular mechanisms underlying this phenomenon are not known. The aim of this study was to investigate the association and potential mechanisms of vitamin D levels with the progression of DN by analyzing general clinical data and using bioinformatics methods. Methods The study included 567 diabetes mellitus type 2 (T2DM) patients from the Rocket Force Characteristic Medical Center as the case group and 221 healthy examinees as the normal control group. T2DM patients were categorized into T2DM, early diabetic nephropathy (EDN), and advanced diabetic nephropathy (ADN) based on the progression of diabetic nephropathy. The renal RNA-seq and scRNA-seq data of patients with DN were mined from public databases, and the differential expression of vitamin D-related genes in normal-EDN-ADN was analyzed by bioinformatics method, protein interaction network was constructed, immune infiltration was evaluated, single cell map was drawn, and potential mechanisms of VD and DN interaction were explored. Results Chi-square test showed that vitamin D level was significantly negatively correlated with DN progression (p < 0.001). Bioinformatics showed that the expression of vitamin D-related cytochrome P450 family genes was down-regulated, and TLR4 and other related inflammatory genes were abnormally up-regulated with the progression of DN. Vitamin D metabolism disturbance up-regulate "Nf-Kappa B signaling pathway," B cell receptor signaling pathway and other immune regulation and insulin resistance related pathways, and inhibit a variety of metabolic pathways. In addition, vitamin D metabolism disturbance are strongly associated with the development of diabetic cardiomyopathy and several neurological disease complications. Conclusion VDD or vitamin D metabolism disturbance is positively associated with the severity of renal injury. The mechanisms may involve abnormal regulation of the immune system by vitamin D metabolism disturbance, metabolic suppression, upregulation of insulin resistance and inflammatory signalling pathways.
Collapse
Affiliation(s)
- Jiachen Gao
- The PLA Rocket Force Characteristic Medical Center, The Postgraduate Training Base of Jinzhou Medical University, Beijing, China
| | - Xiujun Song
- Department of Clinical Laboratory, The PLA Rocket Force Characteristic Medical Center, Beijing, China
| | - Hongling Ou
- Department of Clinical Laboratory, The PLA Rocket Force Characteristic Medical Center, Beijing, China
| | - Xiyu Cheng
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, Beijing, China
| | - Lishu Zhang
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, Beijing, China
| | - Chen Liu
- Department of Clinical Laboratory, The PLA Rocket Force Characteristic Medical Center, Beijing, China
| | - Ya Dong
- The PLA Rocket Force Characteristic Medical Center, The Postgraduate Training Base of Jinzhou Medical University, Beijing, China
| | - Xinru Wang
- Department of Clinical Laboratory, The PLA Rocket Force Characteristic Medical Center, Beijing, China
| |
Collapse
|
17
|
Li C, Shao X, Zhang S, Wang Y, Jin K, Yang P, Lu X, Fan X, Wang Y. scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network. Cell Rep Med 2024; 5:101568. [PMID: 38754419 PMCID: PMC11228399 DOI: 10.1016/j.xcrm.2024.101568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 12/27/2023] [Accepted: 04/21/2024] [Indexed: 05/18/2024]
Abstract
Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.
Collapse
Affiliation(s)
- Chengyu Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
| | - Shujing Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Yingchao Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Kaiyu Jin
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Penghui Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China; Jinhua Institute of Zhejiang University, Jinhua 321299, China; Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
| |
Collapse
|
18
|
Ning Z, Liu Y, Wan M, Zuo Y, Chen S, Shi Z, Xu Y, Li H, Ko H, Zhang J, Xiao S, Guo D, Tang Y. APOE2 protects against Aβ pathology by improving neuronal mitochondrial function through ERRα signaling. Cell Mol Biol Lett 2024; 29:87. [PMID: 38867189 PMCID: PMC11170814 DOI: 10.1186/s11658-024-00600-x] [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: 12/21/2023] [Accepted: 05/21/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disease and apolipoprotein E (APOE) genotypes (APOE2, APOE3, and APOE4) show different AD susceptibility. Previous studies indicated that individuals carrying the APOE2 allele reduce the risk of developing AD, which may be attributed to the potential neuroprotective role of APOE2. However, the mechanisms underlying the protective effects of APOE2 is still unclear. METHODS We analyzed single-nucleus RNA sequencing and bulk RNA sequencing data of APOE2 and APOE3 carriers from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort. We validated the findings in SH-SY5Y cells and AD model mice by evaluating mitochondrial functions and cognitive behaviors respectively. RESULTS The pathway analysis of six major cell types revealed a strong association between APOE2 and cellular stress and energy metabolism, particularly in excitatory and inhibitory neurons, which was found to be more pronounced in the presence of beta-amyloid (Aβ). Moreover, APOE2 overexpression alleviates Aβ1-42-induced mitochondrial dysfunction and reduces the generation of reactive oxygen species in SH-SY5Y cells. These protective effects may be due to ApoE2 interacting with estrogen-related receptor alpha (ERRα). ERRα overexpression by plasmids or activation by agonist was also found to show similar mitochondrial protective effects in Aβ1-42-stimulated SH-SY5Y cells. Additionally, ERRα agonist treatment improve the cognitive performance of Aβ injected mice in both Y maze and novel object recognition tests. ERRα agonist treatment increased PSD95 expression in the cortex of agonist-treated-AD mice. CONCLUSIONS APOE2 appears to enhance neural mitochondrial function via the activation of ERRα signaling, which may be the protective effect of APOE2 to treat AD.
Collapse
Affiliation(s)
- Zhiyuan Ning
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan, 528200, China
| | - Ying Liu
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan, 528200, China
| | - Mengyao Wan
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan, 528200, China
| | - You Zuo
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Siqi Chen
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan, 528200, China
| | - Zhongshan Shi
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Yongteng Xu
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Honghong Li
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Ho Ko
- Division of Neurology, Department of Medicine and Therapeutics & Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Jing Zhang
- Department of Neurology, Fujian Medical University Union Hospital, Fujian Key Laboratory of Molecular Neurology and Institute of Neuroscience, Fujian Medical University, Fuzhou, China
| | - Songhua Xiao
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
| | - Daji Guo
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan, 528200, China.
| | - Yamei Tang
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-Sen Memorial Hospital, Foshan, 528200, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
| |
Collapse
|
19
|
Fan Y, Bian X, Meng X, Li L, Fu L, Zhang Y, Wang L, Zhang Y, Gao D, Guo X, Lammi MJ, Peng G, Sun S. Unveiling inflammatory and prehypertrophic cell populations as key contributors to knee cartilage degeneration in osteoarthritis using multi-omics data integration. Ann Rheum Dis 2024; 83:926-944. [PMID: 38325908 PMCID: PMC11187367 DOI: 10.1136/ard-2023-224420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 01/23/2024] [Indexed: 02/09/2024]
Abstract
OBJECTIVES Single-cell and spatial transcriptomics analysis of human knee articular cartilage tissue to present a comprehensive transcriptome landscape and osteoarthritis (OA)-critical cell populations. METHODS Single-cell RNA sequencing and spatially resolved transcriptomic technology have been applied to characterise the cellular heterogeneity of human knee articular cartilage which were collected from 8 OA donors, and 3 non-OA control donors, and a total of 19 samples. The novel chondrocyte population and marker genes of interest were validated by immunohistochemistry staining, quantitative real-time PCR, etc. The OA-critical cell populations were validated through integrative analyses of publicly available bulk RNA sequencing data and large-scale genome-wide association studies. RESULTS We identified 33 cell population-specific marker genes that define 11 chondrocyte populations, including 9 known populations and 2 new populations, that is, pre-inflammatory chondrocyte population (preInfC) and inflammatory chondrocyte population (InfC). The novel findings that make this an important addition to the literature include: (1) the novel InfC activates the mediator MIF-CD74; (2) the prehypertrophic chondrocyte (preHTC) and hypertrophic chondrocyte (HTC) are potentially OA-critical cell populations; (3) most OA-associated differentially expressed genes reside in the articular surface and superficial zone; (4) the prefibrocartilage chondrocyte (preFC) population is a major contributor to the stratification of patients with OA, resulting in both an inflammatory-related subtype and a non-inflammatory-related subtype. CONCLUSIONS Our results highlight InfC, preHTC, preFC and HTC as potential cell populations to target for therapy. Also, we conclude that profiling of those cell populations in patients might be used to stratify patient populations for defining cohorts for clinical trials and precision medicine.
Collapse
Affiliation(s)
- Yue Fan
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, Shaanxi Province; Key Laboratory of Trace Elements and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education; Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xuzhao Bian
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Xiaogao Meng
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, China-New Zealand Joint Laboratory on Biomedicine and Health, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, China
| | - Lei Li
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Laiyi Fu
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yanan Zhang
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Long Wang
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Yan Zhang
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
- Department of Orthopaedics, Honghui Hospital, Xi'an, Shaanxi, China
| | - Dalong Gao
- Department of Orthopaedics, The Central Hospital of Xianyang, Xianyang, China
| | - Xiong Guo
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, Shaanxi Province; Key Laboratory of Trace Elements and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Mikko Juhani Lammi
- Department of Integrative Medical Biology, University of Umeå, Umeå, Sweden
| | - Guangdun Peng
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, China-New Zealand Joint Laboratory on Biomedicine and Health, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Shiquan Sun
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region, Shaanxi Province; Key Laboratory of Trace Elements and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education; Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| |
Collapse
|
20
|
Zhao Y, Yu J, Zheng C, Zhou B. Establishment of a prognostic model for hypoxia-associated genes in OPSCC and revelation of intercellular crosstalk. Front Immunol 2024; 15:1371365. [PMID: 38887298 PMCID: PMC11181350 DOI: 10.3389/fimmu.2024.1371365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/16/2024] [Indexed: 06/20/2024] Open
Abstract
Hypoxia exerts a profound influence on the tumor microenvironment and immune response, shaping treatment outcomes and prognosis. Utilizing consistency clustering, we discerned two hypoxia subtypes in OPSCC bulk sequencing data from GEO. Key modules within OPSCC were identified through weighted gene correlation network analysis (WGCNA). Core modules underwent CIBERSORT immune infiltration analysis and GSEA functional enrichment. Univariate Cox and LASSO analyses were employed to construct prognostic models for seven hypoxia-related genes. Further investigation into clinical characteristics, the immune microenvironment, and TIDE algorithm prediction for immunotherapy response was conducted in high- and low-risk groups. scRNA-seq data were visually represented through TSNE clustering, employing the scissors algorithm to map hypoxia phenotypes. Interactions among cellular subpopulations were explored using the Cellchat package, with additional assessments of metabolic and transcriptional activities. Integration with clinical data unveiled a prevalence of HPV-positive patients in the low hypoxia and low-risk groups. Immunohistochemical validation demonstrated low TDO2 expression in HPV-positive (P16-positive) patients. Our prediction suggested that HPV16 E7 promotes HIF-1α inhibition, leading to reduced glycolytic activity, ultimately contributing to better prognosis and treatment sensitivity. The scissors algorithm effectively segregated epithelial cells and fibroblasts into distinct clusters based on hypoxia characteristics. Cellular communication analysis illuminated significant crosstalk among hypoxia-associated epithelial, fibroblast, and endothelial cells, potentially fostering tumor proliferation and metastasis.
Collapse
Affiliation(s)
| | | | | | - Baosen Zhou
- Department of Clinical Epidemiology and Center of Evidence-Based Medicine, The First Hospital of China Medical University, Shenyang, China
| |
Collapse
|
21
|
Xiao Y, Jin W, Qian K, Ju L, Wang G, Wu K, Cao R, Chang L, Xu Z, Luo J, Shan L, Yu F, Chen X, Liu D, Cao H, Wang Y, Cao X, Zhou W, Cui D, Tian Y, Ji C, Luo Y, Hong X, Chen F, Peng M, Zhang Y, Wang X. Integrative Single Cell Atlas Revealed Intratumoral Heterogeneity Generation from an Adaptive Epigenetic Cell State in Human Bladder Urothelial Carcinoma. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308438. [PMID: 38582099 PMCID: PMC11200000 DOI: 10.1002/advs.202308438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/22/2024] [Indexed: 04/08/2024]
Abstract
Intratumor heterogeneity (ITH) of bladder cancer (BLCA) contributes to therapy resistance and immune evasion affecting clinical prognosis. The molecular and cellular mechanisms contributing to BLCA ITH generation remain elusive. It is found that a TM4SF1-positive cancer subpopulation (TPCS) can generate ITH in BLCA, evidenced by integrative single cell atlas analysis. Extensive profiling of the epigenome and transcriptome of all stages of BLCA revealed their evolutionary trajectories. Distinct ancestor cells gave rise to low-grade noninvasive and high-grade invasive BLCA. Epigenome reprograming led to transcriptional heterogeneity in BLCA. During early oncogenesis, epithelial-to-mesenchymal transition generated TPCS. TPCS has stem-cell-like properties and exhibited transcriptional plasticity, priming the development of transcriptionally heterogeneous descendent cell lineages. Moreover, TPCS prevalence in tumor is associated with advanced stage cancer and poor prognosis. The results of this study suggested that bladder cancer interacts with its environment by acquiring a stem cell-like epigenomic landscape, which might generate ITH without additional genetic diversification.
Collapse
Affiliation(s)
- Yu Xiao
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Wan Jin
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
- Euler TechnologyBeijing102206China
| | - Kaiyu Qian
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Lingao Ju
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Gang Wang
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Kai Wu
- Euler TechnologyBeijing102206China
| | - Rui Cao
- Department of UrologyBeijing Friendship HospitalCapital Medical UniversityBeijing100050China
| | | | - Zilin Xu
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Jun Luo
- Department of PathologyZhongnan Hospital of Wuhan UniversityWuhan430071China
| | | | - Fang Yu
- Department of PathologyZhongnan Hospital of Wuhan UniversityWuhan430071China
| | | | | | - Hong Cao
- Department of PathologyZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Yejinpeng Wang
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Xinyue Cao
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
- Clinical Trial CenterZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Wei Zhou
- Hubei Key Laboratory of Medical Technology on TransplantationInstitute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan UniversityWuhan430071China
| | - Diansheng Cui
- Department of UrologyHubei Cancer HospitalWuhan430079China
| | - Ye Tian
- Department of UrologyBeijing Friendship HospitalCapital Medical UniversityBeijing100050China
| | - Chundong Ji
- Department of UrologyThe Affiliated Hospital of Panzhihua UniversityPanzhihua617099China
| | - Yongwen Luo
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
| | - Xin Hong
- Department of UrologyPeking University International HospitalBeijing102206China
| | - Fangjin Chen
- Center for Quantitative BiologySchool of Life SciencesPeking UniversityBeijing100091China
| | - Minsheng Peng
- State Key Laboratory of Genetic Resources and EvolutionKunming Institute of ZoologyChinese Academy of SciencesKunming650201China
- Kunming College of Life ScienceUniversity of Academy of SciencesKunming650201China
| | - Yi Zhang
- Euler TechnologyBeijing102206China
| | - Xinghuan Wang
- Department of Urology, Hubei Key Laboratory of Urological Diseases, Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei ProvinceZhongnan Hospital of Wuhan UniversityWuhan430071China
- Medical Research InstituteWuhan UniversityWuhan430071China
| |
Collapse
|
22
|
Guo H, Zhang L, Guo H, Cui X, Fan Y, Li T, Qi X, Yan T, Chen A, Shi F, Zeng F. Single-cell transcriptome atlas reveals somatic cell embryogenic differentiation features during regeneration. PLANT PHYSIOLOGY 2024; 195:1414-1431. [PMID: 38401160 DOI: 10.1093/plphys/kiae107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 12/15/2023] [Accepted: 01/16/2024] [Indexed: 02/26/2024]
Abstract
Understanding somatic cell totipotency remains a challenge facing scientific inquiry today. Plants display remarkable cell totipotency expression, illustrated by single-cell differentiation during somatic embryogenesis (SE) for plant regeneration. Determining cell identity and exploring gene regulation in such complex heterogeneous somatic cell differentiation have been major challenges. Here, we performed high-throughput single-cell sequencing assays to define the precise cellular landscape and revealed the modulation mode of marker genes during embryogenic differentiation in cotton (Gossypium hirsutum L.) as the crop for biotechnology application. We demonstrated that nonembryogenic calli (NEC) and primary embryogenic calli (PEC) tissues were composed of heterogeneous cells that could be partitioned into four broad populations with six distinct cell clusters. Enriched cell clusters and cell states were identified in NEC and PEC samples, respectively. Moreover, a broad repertoire of new cluster-specific genes and associated expression modules were identified. The energy metabolism, signal transduction, environmental adaptation, membrane transport pathways, and a series of transcription factors were preferentially enriched in cell embryogenic totipotency expression. Notably, the SE-ASSOCIATED LIPID TRANSFER PROTEIN (SELTP) gene dose-dependently marked cell types with distinct embryogenic states and exhibited a parabolic curve pattern along the somatic cell embryogenic differentiation trajectory, suggesting that SELTP could serve as a favorable quantitative cellular marker for detecting embryogenic expression at the single-cell level. In addition, RNA velocity and Scissor analysis confirmed the pseudo-temporal model and validated the accuracy of the scRNA-seq data, respectively. This work provides valuable marker-genes resources and defines precise cellular taxonomy and trajectory atlases for somatic cell embryogenic differentiation in plant regeneration.
Collapse
Affiliation(s)
- Huihui Guo
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Li Zhang
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Haixia Guo
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Xiwang Cui
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Yupeng Fan
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Tongtong Li
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Xiushan Qi
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Tongdi Yan
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Aiyun Chen
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Fengjuan Shi
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| | - Fanchang Zeng
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, China
| |
Collapse
|
23
|
Song T, Yang Y, Wang Y, Ni Y, Yang Y, Zhang L. Bulk and single-cell RNA sequencing reveal the contribution of laminin γ2 -CD44 to the immune resistance in lymphocyte-infiltrated squamous lung cancer subtype. Heliyon 2024; 10:e31299. [PMID: 38803944 PMCID: PMC11129014 DOI: 10.1016/j.heliyon.2024.e31299] [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] [Received: 06/19/2023] [Revised: 04/01/2024] [Accepted: 05/14/2024] [Indexed: 05/29/2024] Open
Abstract
The high heterogeneity of lung squamous cell carcinomas (LUSC) and the complex tumor microenvironment lead to non-response to immunotherapy in many patients. Therefore, characterizing the heterogeneity of the tumor microenvironment in patients with LUSC and further exploring the immune features and molecular mechanisms that lead to immune resistance will help improve the efficacy of immunotherapy in such patients. Herein, we retrospectively analyzed the RNA sequencing (RNA-seq) data of 513 LUSC samples with other multiomics and single-cell RNA-seq data and validated key features using multiplex immunohistochemistry. We divided these samples into six subtypes (CS1-CS6) based on the RNA-seq data and found that CS3 activates the immune response with a high level of lymphocyte infiltration and gathers a large number of patients with advanced-stage disease but increases the expression of exhausted markers cytotoxic T-lymphocyte associated protein 4, lymphocyte-activation gene 3, and programmed death-1. The prediction of the response to immunotherapy showed that CS3 is potentially resistant to immune checkpoint blockade therapy, and multi-omic data analysis revealed that CS3 specifically expresses immunosuppression-related proteins B cell lymphoma 2, GRB2-associated binding protein, and dual-specificity phosphatase 4 and has a high mutation ratio of the driver gene ATP binding cassette subfamily A member 13. Furthermore, single-cell RNA-seq verified lymphocyte infiltration in the CS3 subtype and revealed a positive relationship between the expression of LAMC2-CD44 and immune resistance. LAMC2 and CD44 are epithelial-mesenchymal transition-associated genes that modulate tumor proliferation, and multicolor immunofluorescence validated the negative relationship between the expression of LAMC2-CD44 and immune infiltration. Thus, we identified a lymphocyte-infiltrated subtype (CS3) in patients with LUSC that exhibited resistance to immune checkpoint blockade therapy, and the co-hyperexpression of LAMC2-CD44 contributed to immune resistance, which could potentially improve immunological efficacy by targeting this molecule pair in combination with immunotherapy.
Collapse
Affiliation(s)
- Tingting Song
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ying Yang
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yilong Wang
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yinyun Ni
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yongfeng Yang
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Li Zhang
- Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, China
| |
Collapse
|
24
|
Xu K, Dong M, Wu Z, Luo L, Xie F, Li F, Huang H, Wang F, Xiong X, Wen Z. Single-Cell RNA Sequencing Identifies Crucial Genes Influencing the Polarization of Tumor-Associated Macrophages in Liver Cancer. Int J Genomics 2024; 2024:7263358. [PMID: 38938448 PMCID: PMC11208785 DOI: 10.1155/2024/7263358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 04/15/2024] [Accepted: 05/06/2024] [Indexed: 06/29/2024] Open
Abstract
Background In the context of hepatocellular carcinoma (HCC), tumor-associated macrophages (TAMs) are pivotal for the immunosuppressive nature of the tumor microenvironment (TME). This investigation delves into the functional transformations of TAMs within the TME by leveraging single-cell transcriptomics to pinpoint critical genes influencing TAM subset polarization. Methods We procured single-cell and bulk transcriptomic data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), implementing quality assurance, dimensional reduction, clustering, and annotation on the single-cell sequencing data. To examine cellular interactions, CellChat was utilized, while single-cell regulatory network inference and clustering (SCENIC) was applied to deduce transcription factors (TFs) and their associated targets. Through gene enrichment, survival, and immune infiltration correlation analyses, we sought to pinpoint and validate influential genes. A TAM model under HCC conditions was then established to confirm the expression levels of these key genes. Results Our analysis encompassed 74,742 cells and 23,110 genes. Through postdimensional reduction and clustering, we identified seven distinct cell types and nine TAM subtypes. Analysis via CellChat highlighted a predominance of M2-phenotype-inclined TAM subsets within the tumor's core. SCENIC pinpointed the transcription factor PRDM1 and its target genes as pivotal in this region. Further analysis indicated these genes' involvement in macrophage polarization. Employing trajectory analysis, survival analysis, and immune infiltration correlation, we scrutinized and validated genes likely directing M2 polarization. Experimental validation confirmed PRDM1's heightened expression in TAMs conditioned by HCC. Conclusions Our findings suggest the PRDM1 gene is a key regulator of M2 macrophage polarization, contributing to the immunosuppressive TME in HCC.
Collapse
Affiliation(s)
- Kedong Xu
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Mingyi Dong
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Zhengqiang Wu
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Linfei Luo
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Fei Xie
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Fan Li
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Hongyan Huang
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Fenfen Wang
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xiaofeng Xiong
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Zhili Wen
- Department of GastroenterologyThe Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| |
Collapse
|
25
|
Ruan X, Cheng Y, Ye Y, Wang Y, Chen X, Yang Y, Liu T, Yan F. PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data. Brief Bioinform 2024; 25:bbae260. [PMID: 38819254 PMCID: PMC11141296 DOI: 10.1093/bib/bbae260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/11/2024] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.
Collapse
Affiliation(s)
- Xinjia Ruan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yu Cheng
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuqing Ye
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuhang Wang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Xinyi Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuqing Yang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Tiantian Liu
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| |
Collapse
|
26
|
Chen Z, Ou Y, Ye F, Li W, Jiang H, Liu S. Machine learning identifies the role of SMAD6 in the prognosis and drug susceptibility in bladder cancer. J Cancer Res Clin Oncol 2024; 150:264. [PMID: 38767747 PMCID: PMC11106122 DOI: 10.1007/s00432-024-05798-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/10/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND Bladder cancer (BCa) is among the most prevalent malignant tumors affecting the urinary system. Due to its highly recurrent nature, standard treatments such as surgery often fail to significantly improve patient prognosis. Our research aims to predict prognosis and identify precise therapeutic targets for novel treatment interventions. METHODS We collected and screened genes related to the TGF-β signaling pathway and performed unsupervised clustering analysis on TCGA-BLCA samples based on these genes. Our analysis revealed two novel subtypes of bladder cancer with completely different biological characteristics, including immune microenvironment, drug sensitivity, and more. Using machine learning classifiers, we identified SMAD6 as a hub gene contributing to these differences and further investigated the role of SMAD6 in bladder cancer in the single-cell transcriptome data. Additionally, we analyzed the relationship between SMAD6 and immune checkpoint genes. Finally, we performed a series of in vitro assays to verify the function of SMAD6 in bladder cancer cell lines. RESULTS We have revealed two novel subtypes of bladder cancer, among which C1 exhibits a worse prognosis, lower drug sensitivity, a more complex tumor microenvironment, and a 'colder' immune microenvironment compared to C2. We identified SMAD6 as a key gene responsible for the differences and further explored its impact on the molecular characteristics of bladder cancer. Through in vitro experiments, we found that SMAD6 promoted the prognosis of BCa patients by inhibiting the proliferation and migration of BCa cells. CONCLUSION Our study reveals two novel subtypes of BCa and identifies SMAD6 as a highly promising therapeutic target.
Collapse
Affiliation(s)
- Ziang Chen
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxi Ou
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fangdie Ye
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Weijian Li
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Haowen Jiang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
| | - Shenghua Liu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.
| |
Collapse
|
27
|
Ko KP, Zhang S, Huang Y, Kim B, Zou G, Jun S, Zhang J, Zhao Y, Martin C, Dunbar KJ, Efe G, Rustgi AK, Nakagawa H, Zhang H, Liu Z, Park JI. Tumor niche network-defined subtypes predict immunotherapy response of esophageal squamous cell cancer. iScience 2024; 27:109795. [PMID: 38741711 PMCID: PMC11089351 DOI: 10.1016/j.isci.2024.109795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/16/2024] [Accepted: 04/17/2024] [Indexed: 05/16/2024] Open
Abstract
Despite the promising outcomes of immune checkpoint inhibitors (ICIs), resistance to ICI presents a new challenge. Therefore, selecting patients for specific ICI applications is crucial for maximizing therapeutic efficacy. Herein, we curated 69 human esophageal squamous cell cancer (ESCC) patients' tumor microenvironment (TME) single-cell transcriptomic datasets to subtype ESCC. Integrative analyses of the cellular network and transcriptional signatures of T cells and myeloid cells define distinct ESCC subtypes characterized by T cell exhaustion, and interleukin (IL) and interferon (IFN) signaling. Furthermore, this approach classifies ESCC patients into ICI responders and non-responders, as validated by whole tumor transcriptomes and liquid biopsy-based single-cell transcriptomes of anti-PD-1 ICI responders and non-responders. Our study stratifies ESCC patients based on TME transcriptional network, providing novel insights into tumor niche remodeling and potentially predicting ICI responses in ESCC patients.
Collapse
Affiliation(s)
- Kyung-Pil Ko
- Department of Experimental Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shengzhe Zhang
- Department of Experimental Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yuanjian Huang
- Department of Experimental Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bongjun Kim
- Department of Experimental Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Gengyi Zou
- Department of Experimental Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sohee Jun
- Department of Experimental Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jie Zhang
- Department of Experimental Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yahui Zhao
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Cecilia Martin
- Division of Digestive and Liver Diseases, Department of Medicine, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Karen J. Dunbar
- Division of Digestive and Liver Diseases, Department of Medicine, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Gizem Efe
- Division of Digestive and Liver Diseases, Department of Medicine, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Anil K. Rustgi
- Division of Digestive and Liver Diseases, Department of Medicine, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hiroshi Nakagawa
- Division of Digestive and Liver Diseases, Department of Medicine, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Haiyang Zhang
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zhihua Liu
- State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jae-Il Park
- Department of Experimental Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Program in Genetics and Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| |
Collapse
|
28
|
Jin P, Wang X, Jin Q, Zhang Y, Shen J, Jiang G, Zhu H, Zhao M, Wang D, Li Z, Zhou Y, Li W, Zhang W, Liu Y, Wang S, Jin W, Cao Y, Sheng G, Dong F, Wu S, Li X, Jin Z, He M, Liu X, Chen L, Zhang Y, Wang K, Li J. Mutant U2AF1-Induced Mis-Splicing of mRNA Translation Genes Confers Resistance to Chemotherapy in Acute Myeloid Leukemia. Cancer Res 2024; 84:1583-1596. [PMID: 38417135 DOI: 10.1158/0008-5472.can-23-2543] [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: 08/23/2023] [Revised: 01/07/2024] [Accepted: 02/21/2024] [Indexed: 03/01/2024]
Abstract
Patients with primary refractory acute myeloid leukemia (AML) have a dismal long-term prognosis. Elucidating the resistance mechanisms to induction chemotherapy could help identify strategies to improve AML patient outcomes. Herein, we retrospectively analyzed the multiomics data of more than 1,500 AML cases and found that patients with spliceosome mutations had a higher risk of developing refractory disease. RNA splicing analysis revealed that the mis-spliced genes in refractory patients converged on translation-associated pathways, promoted mainly by U2AF1 mutations. Integrative analyses of binding and splicing in AML cell lines substantiated that the splicing perturbations of mRNA translation genes originated from both the loss and gain of mutant U2AF1 binding. In particular, the U2AF1S34F and U2AF1Q157R mutants orchestrated the inclusion of exon 11 (encoding a premature termination codon) in the eukaryotic translation initiation factor 4A2 (EIF4A2). This aberrant inclusion led to reduced eIF4A2 protein expression via nonsense-mediated mRNA decay. Consequently, U2AF1 mutations caused a net decrease in global mRNA translation that induced the integrated stress response (ISR) in AML cells, which was confirmed by single-cell RNA sequencing. The induction of ISR enhanced the ability of AML cells to respond and adapt to stress, contributing to chemoresistance. A pharmacologic inhibitor of ISR, ISRIB, sensitized U2AF1 mutant cells to chemotherapy. These findings highlight a resistance mechanism by which U2AF1 mutations drive chemoresistance and provide a therapeutic approach for AML through targeting the ISR pathway. SIGNIFICANCE U2AF1 mutations induce the integrated stress response by disrupting splicing of mRNA translation genes that improves AML cell fitness to enable resistance to chemotherapy, which can be targeted to improve AML treatment.
Collapse
Affiliation(s)
- Peng Jin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoling Wang
- Department of Reproductive Medical Center, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiqi Jin
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
| | - Yi Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Shen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ge Jiang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongming Zhu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ming Zhao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Dan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zeyi Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Zhou
- Department of Reproductive Medical Center, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenzhu Li
- Department of Reproductive Medical Center, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yabin Liu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Siyang Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Wen Jin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- CNRS-LIA Hematology and Cancer, Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuncan Cao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guangying Sheng
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fangyi Dong
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shishuang Wu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoyang Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhen Jin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengke He
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaxin Liu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Yunxiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kankan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- CNRS-LIA Hematology and Cancer, Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junmin Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
29
|
Zhang L, Zhang X, Guan M, Zeng J, Yu F, Lai F. Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response. Respir Res 2024; 25:206. [PMID: 38745285 PMCID: PMC11092068 DOI: 10.1186/s12931-024-02839-6] [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: 11/08/2023] [Accepted: 05/06/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures. METHODS This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD. RESULTS Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients. CONCLUSIONS ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.
Collapse
Affiliation(s)
- Liangyu Zhang
- Department of Thoracic Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Xun Zhang
- Department of Thoracic Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Maohao Guan
- Department of Thoracic Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Jianshen Zeng
- Department of Thoracic Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Fengqiang Yu
- Department of Thoracic Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
| | - Fancai Lai
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
| |
Collapse
|
30
|
Gan D, Zhu Y, Lu X, Li J. SCIPAC: quantitative estimation of cell-phenotype associations. Genome Biol 2024; 25:119. [PMID: 38741183 PMCID: PMC11089691 DOI: 10.1186/s13059-024-03263-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/30/2024] [Indexed: 05/16/2024] Open
Abstract
Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.
Collapse
Affiliation(s)
- Dailin Gan
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556, IN, USA
| | - Yini Zhu
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, 46556, IN, USA
| | - Xin Lu
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, 46556, IN, USA
- Tumor Microenvironment and Metastasis Program, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, 46202, IN, USA
| | - Jun Li
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556, IN, USA.
| |
Collapse
|
31
|
Jiang Z, Wu Y, Miao Y, Deng K, Yang F, Xu S, Wang Y, You R, Zhang L, Fan Y, Guo W, Lian Q, Chen L, Zhang X, Zheng Y, Gu J. HCCDB v2.0: Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae011. [PMID: 38886186 DOI: 10.1093/gpbjnl/qzae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/18/2023] [Accepted: 10/01/2023] [Indexed: 06/20/2024]
Abstract
Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 transcriptomic datasets of HCC clinical samples, the first version of HCC database (HCCDB v1.0) was released in 2018. Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets, it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. With four years having passed, the database now needs integration of recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.
Collapse
Affiliation(s)
- Ziming Jiang
- Eight-Year Program of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100006, China
| | - Yanhong Wu
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Institute for Precision Medicine & Department of Automation, Tsinghua University, Beijing 100084, China
| | - Yuxin Miao
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Institute for Precision Medicine & Department of Automation, Tsinghua University, Beijing 100084, China
| | - Kaige Deng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Fan Yang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Shuhuan Xu
- Fuzhou Institute for Data Technology, Fuzhou 350207, China
| | - Yupeng Wang
- Fuzhou Institute for Data Technology, Fuzhou 350207, China
| | - Renke You
- Fuzhou Institute for Data Technology, Fuzhou 350207, China
| | - Lei Zhang
- Fuzhou Institute for Data Technology, Fuzhou 350207, China
| | - Yuhan Fan
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Institute for Precision Medicine & Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wenbo Guo
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Institute for Precision Medicine & Department of Automation, Tsinghua University, Beijing 100084, China
| | - Qiuyu Lian
- University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lei Chen
- International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai 200438, China
- National Center for Liver Cancer, Shanghai 201805, China
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Institute for Precision Medicine & Department of Automation, Tsinghua University, Beijing 100084, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Jin Gu
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Institute for Precision Medicine & Department of Automation, Tsinghua University, Beijing 100084, China
| |
Collapse
|
32
|
Gong X, He W, Jin W, Ma H, Wang G, Li J, Xiao Y, Zhao Y, Chen Q, Guo H, Yang J, Qi Y, Dong W, Fu M, Li X, Liu J, Liu X, Yin A, Zhang Y, Wei Y. Disruption of maternal vascular remodeling by a fetal endoretrovirus-derived gene in preeclampsia. Genome Biol 2024; 25:117. [PMID: 38715110 PMCID: PMC11075363 DOI: 10.1186/s13059-024-03265-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/30/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Preeclampsia, one of the most lethal pregnancy-related diseases, is associated with the disruption of uterine spiral artery remodeling during placentation. However, the early molecular events leading to preeclampsia remain unknown. RESULTS By analyzing placentas from preeclampsia, non-preeclampsia, and twin pregnancies with selective intrauterine growth restriction, we show that the pathogenesis of preeclampsia is attributed to immature trophoblast and maldeveloped endothelial cells. Delayed epigenetic reprogramming during early extraembryonic tissue development leads to generation of excessive immature trophoblast cells. We find reduction of de novo DNA methylation in these trophoblast cells results in selective overexpression of maternally imprinted genes, including the endoretrovirus-derived gene PEG10 (paternally expressed gene 10). PEG10 forms virus-like particles, which are transferred from the trophoblast to the closely proximate endothelial cells. In normal pregnancy, only a low amount of PEG10 is transferred to maternal cells; however, in preeclampsia, excessive PEG10 disrupts maternal vascular development by inhibiting TGF-beta signaling. CONCLUSIONS Our study reveals the intricate epigenetic mechanisms that regulate trans-generational genetic conflict and ultimately ensure proper maternal-fetal interface formation.
Collapse
Affiliation(s)
- Xiaoli Gong
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
| | - Wei He
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Wan Jin
- Euler Technology, Beijing, China
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hongwei Ma
- Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, China
- Department Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Gang Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
- Human Genetic Resources Preservation Center of Hubei Province, Wuhan, China
- Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jiaxin Li
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
| | - Yu Xiao
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
- Human Genetic Resources Preservation Center of Hubei Province, Wuhan, China
- Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yangyu Zhao
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
| | | | | | - Jiexia Yang
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Yiming Qi
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Wei Dong
- Maternity Ward, Haidian Maternal and Child Health Hospital, Beijing, China
| | - Meng Fu
- Department of Obstetrics and Gynecology, Haidian Maternal and Child Health Hospital, Beijing, China
| | - Xiaojuan Li
- Euler Technology, Beijing, China
- Present Address: International Max Planck Research School for Genome Science, and University of Göttingen, Göttingen Center for Molecular Biosciences, Göttingen, Germany
| | | | - Xinghui Liu
- Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, China.
- Department Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
| | - Aihua Yin
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China.
| | - Yi Zhang
- Euler Technology, Beijing, China.
| | - Yuan Wei
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
| |
Collapse
|
33
|
Zhang L, Zhang X, Guan M, Zeng J, Yu F, Lai F. Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma. Inflamm Res 2024; 73:841-866. [PMID: 38507067 DOI: 10.1007/s00011-024-01871-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures. METHODS In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells. RESULTS Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients. CONCLUSIONS ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.
Collapse
Affiliation(s)
- Liangyu Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China
| | - Xun Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China
| | - Maohao Guan
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China
| | - Jianshen Zeng
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China
| | - Fengqiang Yu
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China.
| | - Fancai Lai
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China.
| |
Collapse
|
34
|
Wang L, Ma L, Song Z, Zhou L, Chen K, Wang X, Liu Z, Wang B, Shen C, Guo X, Jia X. Single-cell transcriptome analysis profiling lymphatic invasion-related TME in colorectal cancer. Sci Rep 2024; 14:8911. [PMID: 38632387 PMCID: PMC11024122 DOI: 10.1038/s41598-024-59656-6] [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: 01/18/2024] [Accepted: 04/12/2024] [Indexed: 04/19/2024] Open
Abstract
Lymphatic invasion (LI) is extremely aggressive and induces worse prognosis among patients with colorectal cancer (CRC). Thus, it is critical to characterize the cellular and molecular mechanisms underlying LI in order to establish novel and efficacious therapeutic targets that enhance the prognosis of CRC patients. RNA-seq data, clinical and survival information of colon adenocarcinoma (COAD) patients were obtained from the TCGA database. In addition, three scRNA-seq datasets of CRC patients were acquired from the GEO database. Data analyses were conducted with the R packages. We assessed the tumor microenvironment (TME) differences between LI+ and LI- based scRNA-seq data, LI+ cells exhibited augmented abundance of immunosuppression and invasive subset. Marked extracellular matrix network activation was also observed in LI+ cells within SPP1+ macrophages. We revealed that an immunosuppressive and pro-angiogenic TME strongly enhanced LI, as was evidenced by the CD4+ Tregs, CD8+ GZMK+, SPP1+ macrophages, e-myCAFs, and w-myCAFs subcluster infiltrations. Furthermore, we identified potential LI targets that influenced tumor development, metastasis, and immunotherapeutic response. Finally, a novel LIRS model was established based on the expression of 14 LI-related signatures, and in the two testing cohorts, LIRS was also proved to have accurate prognostic predictive ability. In this report, we provided a valuable resource and extensive insights into the LI of CRC. Our conclusions can potentially benefit the establishment of highly efficacious therapeutic targets as well as diagnostic biomarkers that improve patient outcomes.
Collapse
Affiliation(s)
- Liping Wang
- Department of Geriatrics, Liaocheng People's Hospital, Liaocheng, 252000, Shandong, China
| | - Liming Ma
- Harbin Inji Technology Co., Ltd., Harbin, 150060, Heilongjiang, China
| | - Zhaona Song
- Joint Laboratory for Translational Medicine Research, Liaocheng People's Hospital, Liaocheng, 252000, Shandong, China
| | - Li Zhou
- Beijing Easyresearch Technology Limited, Beijing, 100049, China
| | - Kexin Chen
- Joint Laboratory for Translational Medicine Research, Liaocheng People's Hospital, Liaocheng, 252000, Shandong, China
| | - Xizi Wang
- Joint Laboratory for Translational Medicine Research, Liaocheng People's Hospital, Liaocheng, 252000, Shandong, China
| | - Zhen Liu
- Harbin Inji Technology Co., Ltd., Harbin, 150060, Heilongjiang, China
| | - Baozhong Wang
- Department of Oncology, Liaocheng People's Hospital, Liaocheng, 252000, Shandong, China
| | - Chen Shen
- Department of Data and Information, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, Zhejiang, China
| | - Xianchao Guo
- Harbin Inji Technology Co., Ltd., Harbin, 150060, Heilongjiang, China.
| | - Xiaodong Jia
- Joint Laboratory for Translational Medicine Research, Liaocheng People's Hospital, Liaocheng, 252000, Shandong, China.
| |
Collapse
|
35
|
Mao S, Wang Y, Chao N, Zeng L, Zhang L. Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals immune suppression subtypes and establishes a novel signature for determining the prognosis in lung adenocarcinoma. Cell Oncol (Dordr) 2024:10.1007/s13402-024-00948-4. [PMID: 38616208 DOI: 10.1007/s13402-024-00948-4] [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] [Accepted: 04/07/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with lower survival rates. Recent advancements in targeted therapies and immunotherapies targeting immune checkpoints have achieved remarkable success, there is still a large percentage of LUAD that lacks available therapeutic options. Due to tumor heterogeneity, the diagnosis and treatment of LUAD are challenging. Exploring the biology of LUAD and identifying new biomarker and therapeutic targets options are essential. METHOD We performed single-cell RNA sequencing (scRNA-seq) of 6 paired primary and adjacent LUAD tissues, and integrative omics analysis of the scRNA-seq, bulk RNA-seq and whole-exome sequencing data revealed molecular subtype characteristics. Our experimental results confirm that CDC25C gene can serve as a potential marker for poor prognosis in LUAD. RESULTS We investigated aberrant gene expression in diverse cell types in LUAD via the scRNA-seq data. Moreover, multi-omics clustering revealed four subgroups defined by transcriptional profile and molecular subtype 4 (MS4) with poor survival probability, and immune cell infiltration signatures revealed that MS4 tended to be the immunosuppressive subtype. Our study revealed that the CDC25C gene can be a distinct prognostic biomarker that indicates immune infiltration levels and response to immunotherapy in LUAD patients. Our experimental results concluded that CDC25C expression affects lung cancer cell invasion and migration, might play a key role in regulating Epithelial-Mesenchymal Transition (EMT) pathways. CONCLUSIONS Our multi-omics result revealed a comprehensive set of molecular attributes associated with prognosis-related genes in LUAD at the cellular and tissue level. Identification of a subtype of immunosuppressive TME and prognostic signature for LUAD. We identified the cell cycle regulation gene CDC25C affects lung cancer cell invasion and migration, which can be used as a potential biomarker for LUAD.
Collapse
Affiliation(s)
- Shengqiang Mao
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Yilong Wang
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Ningning Chao
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Lingyan Zeng
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Li Zhang
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
| |
Collapse
|
36
|
Cheng M, Xiong J, Liu Q, Zhang C, Li K, Wang X, Chen S. Integrating bulk and single-cell sequencing data to construct a Scissor + dendritic cells prognostic model for predicting prognosis and immune responses in ESCC. Cancer Immunol Immunother 2024; 73:97. [PMID: 38619620 PMCID: PMC11018588 DOI: 10.1007/s00262-024-03683-9] [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: 02/05/2024] [Accepted: 03/18/2024] [Indexed: 04/16/2024]
Abstract
Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC.
Collapse
Affiliation(s)
- Maosheng Cheng
- Department of Medical Oncology; Institute of Precision Medicine; Center for Translational Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jianqi Xiong
- Department of Medical Oncology; Institute of Precision Medicine; Center for Translational Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Qianwen Liu
- State Key Laboratory of Oncology in South China, Department of Thoracic Surgery, Collaborative Innovation Center for Cancer Medicine, Guangdong Esophageal Cancer Institute, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Caihua Zhang
- Department of Medical Oncology; Institute of Precision Medicine; Center for Translational Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Kang Li
- Department of Medical Oncology; Institute of Precision Medicine; Center for Translational Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Xinyuan Wang
- The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Shuang Chen
- Department of Medical Oncology; Institute of Precision Medicine; Center for Translational Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
| |
Collapse
|
37
|
Lai W, Xie R, Chen C, Lou W, Yang H, Deng L, Lu Q, Tang X. Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC. Heliyon 2024; 10:e28490. [PMID: 38590858 PMCID: PMC10999934 DOI: 10.1016/j.heliyon.2024.e28490] [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] [Received: 11/19/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/10/2024] Open
Abstract
Background High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice. Methods We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments. Results We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment. Conclusions We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.
Collapse
Affiliation(s)
- Wenwen Lai
- Department of Organ Transplantation, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi, China
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Ruixiang Xie
- School of Life Science, Nanchang University, Nanchang University, Nanchang, China
| | - Chen Chen
- College of Basic Medical Science, Nanchang University, Nanchang, China
| | - Weiming Lou
- Academic Affairs Office, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Haiyan Yang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi, China
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Libin Deng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi, China
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Quqin Lu
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi, China
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Xiaoli Tang
- College of Basic Medical Science, Nanchang University, Nanchang, China
| |
Collapse
|
38
|
Tan Y, Song L, Ma J, Pan M, Niu S, Yue X, Li Y, Gu L, Liu S, Chang J. Single-cell analysis identified POSTN + cells associated with the aggressive phenotype and risk of esophageal squamous cell carcinoma. HGG ADVANCES 2024; 5:100278. [PMID: 38369754 PMCID: PMC10924139 DOI: 10.1016/j.xhgg.2024.100278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 02/20/2024] Open
Abstract
Tumors are intricate and heterogeneous systems characterized by mosaic cancer cell populations with diverse expression profiles. Leveraging single-cell technologies, we employed the Scissor algorithm to delineate an epithelial subpopulation associated with the aggressive phenotype in esophageal squamous cell carcinoma (ESCC). This identified subpopulation exhibited elevated expression of genes involved in critical pathways, such as epithelial-mesenchymal transition and PI3K-Akt. Key signature genes within this subpopulation, namely CAV1, COL3A1, COL6A1, POSTN, and TAGLN, demonstrated significant upregulation concomitant with both tumorigenesis and tumor progression across independent single-cell datasets. Furthermore, we selected 1,450 expression quantitative trait loci of the top 62 signature genes of this cell subpopulation to investigate their potential in predicting ESCC risk. The results showed that the POSTN loci were predominantly associated with ESCC susceptibility. Through functional annotation and replication analyses, we identified that the rs1028728 in the POSTN promoter was significantly associated with increased ESCC risk in 7,049 ESCC cases and 8,063 controls (odds ratio = 1.29, 95% confidence interval: 1.18-1.42, p = 4.03 × 10-8). Subsequent biochemical experiments showed that the rs1028728[T] allele enhanced POSTN expression by affecting the binding of PRRX1 in the POSTN promoter. In summary, our meticulous single-cell analysis delineates an invasive epithelial subpopulation in ESCC, with POSTN emerging as an important marker for the aggressive phenotype. These findings offer more insights into potential strategies for the prevention and intervention of ESCC, enriching our understanding of this complex cancer landscape.
Collapse
Affiliation(s)
- Yuqian Tan
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Lina Song
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jialing Ma
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Miaoxin Pan
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Siyuan Niu
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xinying Yue
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yueping Li
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Linglong Gu
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shasha Liu
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiang Chang
- Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| |
Collapse
|
39
|
Zou G, Huang Y, Zhang S, Ko KP, Kim B, Zhang J, Venkatesan V, Pizzi MP, Fan Y, Jun S, Niu N, Wang H, Song S, Ajani JA, Park JI. E-cadherin loss drives diffuse-type gastric tumorigenesis via EZH2-mediated reprogramming. J Exp Med 2024; 221:e20230561. [PMID: 38411616 PMCID: PMC10899090 DOI: 10.1084/jem.20230561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/27/2023] [Accepted: 01/29/2024] [Indexed: 02/28/2024] Open
Abstract
Diffuse-type gastric adenocarcinoma (DGAC) is a deadly cancer often diagnosed late and resistant to treatment. While hereditary DGAC is linked to CDH1 mutations, the role of CDH1/E-cadherin inactivation in sporadic DGAC tumorigenesis remains elusive. We discovered CDH1 inactivation in a subset of DGAC patient tumors. Analyzing single-cell transcriptomes in malignant ascites, we identified two DGAC subtypes: DGAC1 (CDH1 loss) and DGAC2 (lacking immune response). DGAC1 displayed distinct molecular signatures, activated DGAC-related pathways, and an abundance of exhausted T cells in ascites. Genetically engineered murine gastric organoids showed that Cdh1 knock-out (KO), KrasG12D, Trp53 KO (EKP) accelerates tumorigenesis with immune evasion compared with KrasG12D, Trp53 KO (KP). We also identified EZH2 as a key mediator promoting CDH1 loss-associated DGAC tumorigenesis. These findings highlight DGAC's molecular diversity and potential for personalized treatment in CDH1-inactivated patients.
Collapse
Affiliation(s)
- Gengyi Zou
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yuanjian Huang
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shengzhe Zhang
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kyung-Pil Ko
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bongjun Kim
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jie Zhang
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vishwa Venkatesan
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Melissa P. Pizzi
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yibo Fan
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sohee Jun
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Na Niu
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Huamin Wang
- Division of Pathology/Lab Medicine, Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shumei Song
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jaffer A. Ajani
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jae-Il Park
- Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Program in Genetics and Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| |
Collapse
|
40
|
Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [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] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
Collapse
Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| |
Collapse
|
41
|
Pang Q, Chen L, An C, Zhou J, Xiao H. Single-cell and bulk RNA sequencing highlights the role of M1-like infiltrating macrophages in antibody-mediated rejection after kidney transplantation. Heliyon 2024; 10:e27865. [PMID: 38524599 PMCID: PMC10958716 DOI: 10.1016/j.heliyon.2024.e27865] [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] [Received: 02/28/2024] [Accepted: 03/07/2024] [Indexed: 03/26/2024] Open
Abstract
Background Antibody-mediated rejection (ABMR) significantly affects transplanted kidney survival, yet the macrophage phenotype, ontogeny, and mechanisms in ABMR remain unclear. Method We analyzed post-transplant sequencing and clinical data from GEO and ArrayExpress. Using dimensionality reduction and clustering on scRNA-seq data, we identified macrophage subpopulations and compared their infiltration in ABMR and non-rejection cases. Cibersort quantified these subpopulations in bulk samples. Cellchat, SCENIC, monocle2, and monocle3 helped explore intercellular interactions, predict transcription factors, and simulate differentiation of cell subsets. The Scissor method linked macrophage subgroups with transplant prognosis. Furthermore, hdWGCNA, nichnet, and lasso regression identified key genes associated with core transcription factors in selected macrophages, validated by external datasets. Results Six macrophage subgroups were identified in five post-transplant kidney biopsies. M1-like infiltrating macrophages, prevalent in ABMR, correlated with pathological injury severity. MIF acted as a primary intercellular signal in these macrophages. STAT1 regulated monocyte-to-M1-like phenotype transformation, impacting transplant prognosis via the IFNγ pathway. The prognostic models built on the upstream and downstream genes of STAT1 effectively predicted transplant survival. The TLR4-STAT1-PARP9 axis may regulate the pro-inflammatory phenotype of M1-like infiltrating macrophages, identifying PARP9 as a potential target for mitigating ABMR inflammation. Conclusion Our study delineates the macrophage landscape in ABMR post-kidney transplantation, underscoring the detrimental impact of M1-like infiltrating macrophages on ABMR pathology and prognosis.
Collapse
Affiliation(s)
- Qidan Pang
- Department of Nephrology, Bishan Hospital of Chongqing Medical University, Chongqing, 402760, China
| | - Liang Chen
- Department of General Surgery/Gastrointestinal Surgery, Bishan Hospital of Chongqing Medical University, Chongqing, 402760, China
| | - Changyong An
- Department of General Surgery/Gastrointestinal Surgery, Bishan Hospital of Chongqing Medical University, Chongqing, 402760, China
| | - Juan Zhou
- Department of Nephrology, Bishan Hospital of Chongqing Medical University, Chongqing, 402760, China
| | - Hanyu Xiao
- Department of General Surgery/Gastrointestinal Surgery, Bishan Hospital of Chongqing Medical University, Chongqing, 402760, China
| |
Collapse
|
42
|
Liu X, Chen Z, Zhang L. Identification of estrogen response-associated STRA6+ granulosa cells within high-grade serous ovarian carcinoma by single-cell sequencing. Heliyon 2024; 10:e27790. [PMID: 38509903 PMCID: PMC10950672 DOI: 10.1016/j.heliyon.2024.e27790] [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] [Received: 12/13/2023] [Revised: 01/31/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024] Open
Abstract
Background High-grade serous ovarian carcinoma (HGSOC) is a pathologic subtype of ovarian cancer (OC) with a more lethal prognosis. Extensive heterogeneity results in HGSOC being more susceptible to treatment resistance and adverse treatment effects. Revealing the heterogeneity involved is crucial. Methods We downloaded the single-cell RNA-seq (scRNA) data from GEO database and performed a scRNA analysis for cell landscape of HGSOC by using the Seurat package. The highly expressed genes were uploaded into the DAVID and KEGG database for enrichment analysis, and the AUCell package was used to calculate cancer-associated hallmark score. The SCENIC analysis was used for key regulons, the estrogen response enrichment scores in TCGA-OV RNA-seq dataset were calculated by using the GSVA package. Besides, the expression of STRA6 and IRF1 and the cell invasion and migration in si-STRA6 OC cells were detected by using the quantitative reverse transcription (qRT)-PCR method and Transwell assay respectively. Results We successfully constructed a single-cell atlas of HGSOC and delineated the heterogeneity of epithelial cells therein. There were five epithelial cell subpopulations, GLDC + Epithelial cells, PEG3+ leydig cells, STRA6+ granulosa cells, POLE2+ Epithelial cells, and AURKA + Epithelial cells. STRA6+ granulosa cells have the potential to promote tumor growth as well as the highest estrogen response early activity through the biological pathways analysis of highly expressed genes and estrogen response score of ssGSEA. We found that IRF1 and STRA6 expression was remarkably upregulated in the OC cancer cell line HEY. Silencing of STRA6 markedly decreased the invasion and migration ability of the OC cancer cell line HEY. Conclusion There is extreme heterogeneity of epithelial cells in HGSOC, and STRA6+ granulosa cells may be able to promote cancer progression. Our findings are benefit to the heterogeneity identification of HGSOC and develop targeted therapy strategy for HGSOC patients.
Collapse
Affiliation(s)
- Xiaoting Liu
- Medical College, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zhaojun Chen
- Laboratory Department, Hangzhou Third People's Hospital, Hangzhou, 310009, China
| | - Lahong Zhang
- Laboratory Department, Hangzhou Normal University Affiliated Hospital, Hangzhou, 310015, China
| |
Collapse
|
43
|
Wang W, Chen G, Zhang W, Zhang X, Huang M, Li C, Wang L, Lu Z, Xia J. The crucial prognostic signaling pathways of pancreatic ductal adenocarcinoma were identified by single-cell and bulk RNA sequencing data. Hum Genet 2024:10.1007/s00439-024-02663-4. [PMID: 38526745 DOI: 10.1007/s00439-024-02663-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 02/24/2024] [Indexed: 03/27/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with poor prognosis and high mortality. Although a large number of studies have explored its potential prognostic markers using traditional RNA sequencing (RNA-Seq) data, they have not achieved good prediction effect. In order to explore the possible prognostic signaling pathways leading to the difference in prognosis, we identified differentially expressed genes from one scRNA-seq cohort and four GEO cohorts, respectively. Then Cox and Lasso regression analysis showed that 12 genes were independent prognostic factors for PDAC. AUC and calibration curve analysis showed that the prognostic model had good discrimination and calibration. Compared with the low-risk group, the high-risk group had a higher proportion of gene mutations than the low-risk group. Immune infiltration analysis revealed differences in macrophages and monocytes between the two groups. Prognosis related genes were mainly distributed in fibroblasts, macrophages and type 2 ducts. The results of cell communication analysis showed that there was a strong communication between cancer-associated fibroblasts (CAF) and type 2 ductal cells, and collagen formation was the main interaction pathway.
Collapse
Affiliation(s)
- Wenwen Wang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China.
| | - Guo Chen
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Wenli Zhang
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Xihua Zhang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Manli Huang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Chen Li
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Ling Wang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Zifan Lu
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Jielai Xia
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| |
Collapse
|
44
|
Buckenmeyer MJ, Brooks EA, Taylor MS, Yang L, Holewinski RJ, Meyer TJ, Galloux M, Garmendia-Cedillos M, Pohida TJ, Andresson T, Croix B, Wolf MT. Engineering Tumor Stroma Morphogenesis Using Dynamic Cell-Matrix Spheroid Assembly. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585805. [PMID: 38903106 PMCID: PMC11188064 DOI: 10.1101/2024.03.19.585805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
The tumor microenvironment consists of resident tumor cells organized within a compositionally diverse, three-dimensional (3D) extracellular matrix (ECM) network that cannot be replicated in vitro using bottom-up synthesis. We report a new self-assembly system to engineer ECM-rich 3D MatriSpheres wherein tumor cells actively organize and concentrate microgram quantities of decellularized ECM dispersions which modulate cell phenotype. 3D colorectal cancer (CRC) MatriSpheres were created using decellularized small intestine submucosa (SIS) as an orthotopic ECM source that had greater proteomic homology to CRC tumor ECM than traditional ECM formulations such as Matrigel. SIS ECM was rapidly concentrated from its environment and assembled into ECM-rich 3D stroma-like regions by mouse and human CRC cell lines within 4-5 days via a mechanism that was rheologically distinct from bulk hydrogel formation. Both ECM organization and transcriptional regulation by 3D ECM cues affected programs of malignancy, lipid metabolism, and immunoregulation that corresponded with an in vivo MC38 tumor cell subpopulation identified via single cell RNA sequencing. This 3D modeling approach stimulates tumor specific tissue morphogenesis that incorporates the complexities of both cancer cell and ECM compartments in a scalable, spontaneous assembly process that may further facilitate precision medicine.
Collapse
Affiliation(s)
- Michael J. Buckenmeyer
- Cancer Biomaterials Engineering Laboratory, Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Elizabeth A. Brooks
- Cancer Biomaterials Engineering Laboratory, Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Madison S. Taylor
- Cancer Biomaterials Engineering Laboratory, Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Liping Yang
- Tumor Angiogenesis Unit, Mouse Cancer Genetics Program, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Ronald J. Holewinski
- Protein Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21701, USA
| | - Thomas J. Meyer
- CCR Collaborative Bioinformatics Resource, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mélissa Galloux
- Independent Bioinformatician, Marseille, Provence-Alpes-Côte d’Azur, France
| | - Marcial Garmendia-Cedillos
- Instrumentation Development and Engineering Application Solutions, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Thomas J. Pohida
- Instrumentation Development and Engineering Application Solutions, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Thorkell Andresson
- Protein Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21701, USA
| | - Brad Croix
- Tumor Angiogenesis Unit, Mouse Cancer Genetics Program, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Matthew T. Wolf
- Cancer Biomaterials Engineering Laboratory, Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| |
Collapse
|
45
|
Wang X, Yang C, Ma X, Li X, Qi Y, Bai Z, Xu Y, Ma K, Luo Y, Song J, Jia W, He Z, Liu Z. A division-of-labor mode contributes to the cardioprotective potential of mesenchymal stem/stromal cells in heart failure post myocardial infarction. Front Immunol 2024; 15:1363517. [PMID: 38562923 PMCID: PMC10982400 DOI: 10.3389/fimmu.2024.1363517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Background Treatment of heart failure post myocardial infarction (post-MI HF) with mesenchymal stem/stromal cells (MSCs) holds great promise. Nevertheless, 2-dimensional (2D) GMP-grade MSCs from different labs and donor sources have different therapeutic efficacy and still in a low yield. Therefore, it is crucial to increase the production and find novel ways to assess the therapeutic efficacy of MSCs. Materials and methods hUC-MSCs were cultured in 3-dimensional (3D) expansion system for obtaining enough cells for clinical use, named as 3D MSCs. A post-MI HF mouse model was employed to conduct in vivo and in vitro experiments. Single-cell and bulk RNA-seq analyses were performed on 3D MSCs. A total of 125 combination algorithms were leveraged to screen for core ligand genes. Shinyapp and shinycell workflows were used for deploying web-server. Result 3D GMP-grade MSCs can significantly and stably reduce the extent of post-MI HF. To understand the stable potential cardioprotective mechanism, scRNA-seq revealed the heterogeneity and division-of-labor mode of 3D MSCs at the cellular level. Specifically, scissor phenotypic analysis identified a reported wound-healing CD142+ MSCs subpopulation that is also associated with cardiac protection ability and CD142- MSCs that is in proliferative state, contributing to the cardioprotective function and self-renewal, respectively. Differential expression analysis was conducted on CD142+ MSCs and CD142- MSCs and the differentially expressed ligand-related model was achieved by employing 125 combination algorithms. The present study developed a machine learning predictive model based on 13 ligands. Further analysis using CellChat demonstrated that CD142+ MSCs have a stronger secretion capacity compared to CD142- MSCs and Flow cytometry sorting of the CD142+ MSCs and qRT-PCR validation confirmed the significant upregulation of these 13 ligand factors in CD142+ MSCs. Conclusion Clinical GMP-grade 3D MSCs could serve as a stable cardioprotective cell product. Using scissor analysis on scRNA-seq data, we have clarified the potential functional and proliferative subpopulation, which cooperatively contributed to self-renewal and functional maintenance for 3D MSCs, named as "division of labor" mode of MSCs. Moreover, a ligand model was robustly developed for predicting the secretory efficacy of MSCs. A user-friendly web-server and a predictive model were constructed and available (https://wangxc.shinyapps.io/3D_MSCs/).
Collapse
Affiliation(s)
- Xicheng Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Chao Yang
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Xiaoxue Ma
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Xiuhua Li
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Yiyao Qi
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Zhihui Bai
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Ying Xu
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Keming Ma
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Yi Luo
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Jiyang Song
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Wenwen Jia
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Zhiying He
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Zhongmin Liu
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| |
Collapse
|
46
|
Gao J, Zhao Y, Wang Z, Liu F, Chen X, Mo J, Jiang Y, Liu Y, Tian P, Li Y, Deng K, Qi X, Han D, Liu Z, Yang Z, Chen Y, Tang Y, Li C, Liu H, Li J, Jiang T. Single-cell transcriptomic sequencing identifies subcutaneous patient-derived xenograft recapitulated medulloblastoma. Animal Model Exp Med 2024. [PMID: 38477441 DOI: 10.1002/ame2.12399] [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: 11/08/2023] [Accepted: 12/08/2023] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Medulloblastoma (MB) is one of the most common malignant brain tumors that mainly affect children. Various approaches have been used to model MB to facilitate investigating tumorigenesis. This study aims to compare the recapitulation of MB between subcutaneous patient-derived xenograft (sPDX), intracranial patient-derived xenograft (iPDX), and genetically engineered mouse models (GEMM) at the single-cell level. METHODS We obtained primary human sonic hedgehog (SHH) and group 3 (G3) MB samples from six patients. For each patient specimen, we developed two sPDX and iPDX models, respectively. Three Patch+/- GEMM models were also included for sequencing. Single-cell RNA sequencing was performed to compare gene expression profiles, cellular composition, and functional pathway enrichment. Bulk RNA-seq deconvolution was performed to compare cellular composition across models and human samples. RESULTS Our results showed that the sPDX tumor model demonstrated the highest correlation to the overall transcriptomic profiles of primary human tumors at the single-cell level within the SHH and G3 subgroups, followed by the GEMM model and iPDX. The GEMM tumor model was able to recapitulate all subpopulations of tumor microenvironment (TME) cells that can be clustered in human SHH tumors, including a higher proportion of tumor-associated astrocytes and immune cells, and an additional cluster of vascular endothelia when compared to human SHH tumors. CONCLUSIONS This study was the first to compare experimental models for MB at the single-cell level, providing value insights into model selection for different research purposes. sPDX and iPDX are suitable for drug testing and personalized therapy screenings, whereas GEMM models are valuable for investigating the interaction between tumor and TME cells.
Collapse
Affiliation(s)
- Jiayu Gao
- BGI-Shenzhen, Shenzhen, China
- Yidu Central Hospital of Weifang, Weifang, China
| | - Yahui Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ziwei Wang
- BGI-Shenzhen, Shenzhen, China
- BGI-Wuhan, Wuhan, China
| | - Fei Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiotherapy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuan Chen
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jialin Mo
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yifei Jiang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Yongqiang Liu
- Research Center of Chinese Herbal Resources Science and Engineering, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Peiyi Tian
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yanong Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiotherapy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kaiwen Deng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiotherapy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xueling Qi
- Department of NeuroPathology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Dongming Han
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Zijia Liu
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Zhengtao Yang
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yixi Chen
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yujie Tang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunde Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hailong Liu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiotherapy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Chinese Institute for Medical Research, Beijing, China
| | | | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
47
|
Strobl EV, Gamazon ER. Discovering Root Causal Genes with High Throughput Perturbations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.13.574491. [PMID: 38260506 PMCID: PMC10802597 DOI: 10.1101/2024.01.13.574491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Root causal gene expression levels - or root causal genes for short - correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high throughput perturbations with single cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.
Collapse
Affiliation(s)
- Eric V Strobl
- Vanderbilt University Medical Center, Nashville, United States of America
| | - Eric R Gamazon
- Vanderbilt University Medical Center, Nashville, United States of America
| |
Collapse
|
48
|
Wang W, Ji Y, Dong Z, Liu Z, Chen S, Dai L, Su X, Jiang Q, Deng H. Characterizing neuroinflammation and identifying prenatal diagnostic markers for neural tube defects through integrated multi-omics analysis. J Transl Med 2024; 22:257. [PMID: 38461288 PMCID: PMC10924416 DOI: 10.1186/s12967-024-05051-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/29/2024] [Indexed: 03/11/2024] Open
Abstract
BACKGROUND Neural Tube Defects (NTDs) are congenital malformations of the central nervous system resulting from the incomplete closure of the neural tube during early embryonic development. Neuroinflammation refers to the inflammatory response in the nervous system, typically resulting from damage to neural tissue. Immune-related processes have been identified in NTDs, however, the detailed relationship and underlying mechanisms between neuroinflammation and NTDs remain largely unclear. In this study, we utilized integrated multi-omics analysis to explore the role of neuroinflammation in NTDs and identify potential prenatal diagnostic markers using a murine model. METHODS Nine public datasets from Gene Expression Omnibus (GEO) and ArrayExpress were mined using integrated multi-omics analysis to characterize the molecular landscape associated with neuroinflammation in NTDs. Special attention was given to the involvement of macrophages in neuroinflammation within amniotic fluid, as well as the dynamics of macrophage polarization and their interactions with neural cells at single-cell resolution. We also used qPCR assay to validate the key TFs and candidate prenatal diagnostic genes identified through the integrated analysis in a retinoic acid-induced NTDs mouse model. RESULTS Our analysis indicated that neuroinflammation is a critical pathological feature of NTDs, regulated both transcriptionally and epigenetically within central nervous system tissues. Key alterations in gene expression and pathways highlighted the crucial role of STATs molecules in the JAK-STAT signaling pathway in regulating NTDs-associated neuroinflammation. Furthermore, single-cell resolution analysis revealed significant polarization of macrophages and their interaction with neural cells in amniotic fluid, underscoring their central role in mediating neuroinflammation associated with NTDs. Finally, we identified a set of six potential prenatal diagnostic genes, including FABP7, CRMP1, SCG3, SLC16A10, RNASE6 and RNASE1, which were subsequently validated in a murine NTDs model, indicating their promise as prospective markers for prenatal diagnosis of NTDs. CONCLUSIONS Our study emphasizes the pivotal role of neuroinflammation in the progression of NTDs and underlines the potential of specific inflammatory and neural markers as novel prenatal diagnostic tools. These findings provide important clues for further understanding the underlying mechanisms between neuroinflammation and NTDs, and offer valuable insights for the future development of prenatal diagnostics.
Collapse
Affiliation(s)
- Wenshuang Wang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yanhong Ji
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Zhexu Dong
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Zheran Liu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Shuang Chen
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Lei Dai
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaolan Su
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Qingyuan Jiang
- Department of Obstetrics, Sichuan Provincial Hospital for Women and Children, Chengdu, China.
| | - Hongxin Deng
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
49
|
Zhang J, Li Y, Dai W, Tang F, Wang L, Wang Z, Li S, Ji Q, Zhang J, Liao Z, Yu J, Xu Y, Gong J, Hu J, Li J, Guo X, He F, Han L, Gong Y, Ouyang W, Wang Z, Xie C. Molecular classification reveals the sensitivity of lung adenocarcinoma to radiotherapy and immunotherapy: multi-omics clustering based on similarity network fusion. Cancer Immunol Immunother 2024; 73:71. [PMID: 38430394 PMCID: PMC10908647 DOI: 10.1007/s00262-024-03657-x] [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: 12/01/2023] [Accepted: 02/19/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND Due to individual differences in tumors and immune systems, the response rate to immunotherapy is low in lung adenocarcinoma (LUAD) patients. Combinations with other therapeutic strategies improve the efficacy of immunotherapy in LUAD patients. Although radioimmunotherapy has been demonstrated to effectively suppress tumors, the underlying mechanisms still need to be investigated. METHODS Total RNA from LUAD cells was sequenced before and after radiotherapy to identify differentially expressed radiation-associated genes. The similarity network fusion (SNF) algorithm was applied for molecular classification based on radiation-related genes, immune-related genes, methylation data, and somatic mutation data. The changes in gene expression, prognosis, immune cell infiltration, radiosensitivity, chemosensitivity, and sensitivity to immunotherapy were assessed for each subtype. RESULTS We used the SNF algorithm and multi-omics data to divide TCGA-LUAD patients into three subtypes. Patients with the CS3 subtype had the best prognosis, while those with the CS1 and CS2 subtypes had poorer prognoses. Among the strains tested, CS2 exhibited the most elevated immune cell infiltration and expression of immune checkpoint genes, while CS1 exhibited the least. Patients in the CS2 subgroup were more likely to respond to PD-1 immunotherapy. The CS2 patients were most sensitive to docetaxel and cisplatin, while the CS1 patients were most sensitive to paclitaxel. Experimental validation of signature genes in the CS2 subtype showed that inhibiting the expression of RHCG and TRPA1 could enhance the sensitivity of lung cancer cells to radiation. CONCLUSIONS In summary, this study identified a risk classifier based on multi-omics data that can guide treatment selection for LUAD patients.
Collapse
Affiliation(s)
- Jianguo Zhang
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Yangyi Li
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Weijing Dai
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Fang Tang
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Lanqing Wang
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Zhiying Wang
- Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, 266000, Shandong, China
| | - Siqi Li
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Qian Ji
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Junhong Zhang
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Zhengkai Liao
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Jing Yu
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Yu Xu
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Jun Gong
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Jing Hu
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Jie Li
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Xiuli Guo
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Fajian He
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Linzhi Han
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Yan Gong
- Tumor Precision Diagnosis and Treatment Technology and Translational Medicine, Hubei Engineering Research Center, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
- Human Genetics Resource Reservation Center, Wuhan University, Wuhan, 430071, Hubei, China
| | - Wen Ouyang
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
- Hubei Key Laboratory of Tumour Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
| | - Zhihao Wang
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
- Hubei Key Laboratory of Tumour Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
| | - Conghua Xie
- Department of Pulmonary Oncology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
- Hubei Key Laboratory of Tumour Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
| |
Collapse
|
50
|
Li F, Zhang H, Huang Y, Li D, Zheng Z, Xie K, Cao C, Wang Q, Zhao X, Huang Z, Chen S, Chen H, Fan Q, Deng F, Hou L, Deng X, Tan W. Single-cell transcriptome analysis reveals the association between histone lactylation and cisplatin resistance in bladder cancer. Drug Resist Updat 2024; 73:101059. [PMID: 38295753 DOI: 10.1016/j.drup.2024.101059] [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: 11/16/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 03/08/2024]
Abstract
Patients with bladder cancer (BCa) frequently acquires resistance to platinum-based chemotherapy, particularly cisplatin. This study centered on the mechanism of cisplatin resistance in BCa and highlighted the pivotal role of lactylation in driving this phenomenon. Utilizing single-cell RNA sequencing, we delineated the single-cell landscape of Bca, pinpointing a distinctive subset of BCa cells that exhibit marked resistance to cisplatin with association with glycolysis metabolism. Notably, we observed that H3 lysine 18 lactylation (H3K18la) plays a crucial role in activating the transcription of target genes by enriching in their promoter regions. Targeted inhibition of H3K18la effectively restored cisplatin sensitivity in these cisplatin-resistant epithelial cells. Furthermore, H3K18la-driven key transcription factors YBX1 and YY1 promote cisplatin resistance in BCa. These findings enhance our understanding of the mechanisms underlying cisplatin resistance, offering valuable insights for identifying novel intervention targets to overcome drug resistance in Bca.
Collapse
Affiliation(s)
- Fei Li
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Henghui Zhang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Yuan Huang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Dongqing Li
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Zaosong Zheng
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Kunfeng Xie
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Chun Cao
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Qiong Wang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Xinlei Zhao
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Zehai Huang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Shijun Chen
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Haiyong Chen
- School of Chinese Medicine, LKS Faculty of Medicine, The University of Hong Kong R619, 3 Sassoon Road, Pokfulam, Hong Kong, SAR China
| | - Qin Fan
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, PR China
| | - Fan Deng
- Department of Cell Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, PR China
| | - Lina Hou
- Department of Healthy Management, Nanfang Hospital, Southern Medical University, Guangzhou, PR China.
| | - Xiaolin Deng
- Department of Urology, Ganzhou People's Hospital, Ganzhou, PR China.
| | - Wanlong Tan
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China.
| |
Collapse
|