1
|
He R, Lu J, Feng J, Lu Z, Shen K, Xu K, Luo H, Yang G, Chi H, Huang S. Advancing immunotherapy for melanoma: the critical role of single-cell analysis in identifying predictive biomarkers. Front Immunol 2024; 15:1435187. [PMID: 39026661 PMCID: PMC11254669 DOI: 10.3389/fimmu.2024.1435187] [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: 05/19/2024] [Accepted: 06/21/2024] [Indexed: 07/20/2024] Open
Abstract
Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes.
Collapse
Affiliation(s)
- Ru He
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jiaan Lu
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jianglong Feng
- Department of Pathology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Ziqing Lu
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Kaixin Shen
- Department of Art and Design, Shanghai Institute of Technology, Shanghai, China
| | - Ke Xu
- Department of Oncology, Chongqing General Hospital, Chongqing University, Chongqing, China
| | - Huiyan Luo
- Department of Oncology, Chongqing General Hospital, Chongqing University, Chongqing, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
| | - Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Shangke Huang
- Department of Oncology, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan, China
| |
Collapse
|
2
|
Guo N, Vargas J, Reynoso S, Fritz D, Krishna R, Wang C, Zhang F. Uncover spatially informed variations for single-cell spatial transcriptomics with STew. BIOINFORMATICS ADVANCES 2024; 4:vbae064. [PMID: 38827413 PMCID: PMC11142628 DOI: 10.1093/bioadv/vbae064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/06/2024] [Accepted: 05/01/2024] [Indexed: 06/04/2024]
Abstract
Motivation The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge. Results We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues. Availability and implementation Source code and the R software tool STew are available from github.com/fanzhanglab/STew.
Collapse
Affiliation(s)
- Nanxi Guo
- Biostatistics and Informatics PhD Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Juan Vargas
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- MPH Biostatistics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Samantha Reynoso
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Computational Bioscience PhD Program, University of Colorado School of Medicine, Aurora, CO 80045, United States
| | - Douglas Fritz
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Medical Scientist Training Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Revanth Krishna
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Division of Rheumatology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Chuangqi Wang
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Fan Zhang
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Division of Rheumatology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| |
Collapse
|
3
|
Ahn S, Lee HS. Applicability of Spatial Technology in Cancer Research. Cancer Res Treat 2024; 56:343-356. [PMID: 38291743 PMCID: PMC11016655 DOI: 10.4143/crt.2023.1302] [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: 12/10/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.
Collapse
Affiliation(s)
- Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Artificial Intelligence Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| |
Collapse
|
4
|
Danishuddin, Khan S, Kim JJ. Spatial transcriptomics data and analytical methods: An updated perspective. Drug Discov Today 2024; 29:103889. [PMID: 38244672 DOI: 10.1016/j.drudis.2024.103889] [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: 10/31/2023] [Revised: 01/01/2024] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.
Collapse
Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
| | - Shawez Khan
- National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
| |
Collapse
|
5
|
Zhang C, Liu L, Zhang Y, Li M, Fang S, Kang Q, Chen A, Xu X, Zhang Y, Li Y. spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics. Gigascience 2024; 13:giae042. [PMID: 39028588 PMCID: PMC11258913 DOI: 10.1093/gigascience/giae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/08/2024] [Accepted: 06/21/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. FINDINGS We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. CONCLUSIONS In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.
Collapse
Affiliation(s)
| | - Lin Liu
- BGI Research, Shenzhen 518083, China
| | | | - Mei Li
- BGI Research, Shenzhen 518083, China
| | - Shuangsang Fang
- BGI Research, Shenzhen 518083, China
- BGI Research, Beijing 102601, China
| | | | - Ao Chen
- BGI Research, Shenzhen 518083, China
- BGI Research, Chongqing 401329, China
| | - Xun Xu
- BGI Research, Wuhan 430074, China
| | - Yong Zhang
- BGI Research, Shenzhen 518083, China
- BGI Research, Wuhan 430074, China
- Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen 518083, China
| | - Yuxiang Li
- BGI Research, Shenzhen 518083, China
- BGI Research, Wuhan 430074, China
- Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen 518083, China
| |
Collapse
|