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Relevant bibliographies by topics / Spatial multi-omics

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Academic literature on the topic 'Spatial multi-omics'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "Spatial multi-omics"

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Lee, Yoonji, Mingyu Lee, Yoojin Shin, Kyuri Kim, and Taejung Kim. "Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications." International Journal of Molecular Sciences 26, no. 9 (2025): 3949. https://doi.org/10.3390/ijms26093949.

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Spatial omics integrates molecular profiling with spatial tissue context, enabling high-resolution analysis of gene expression, protein interactions, and epigenetic modifications. This approach provides critical insights into disease mechanisms and therapeutic responses, with applications in cancer, neurology, and immunology. Spatial omics technologies, including spatial transcriptomics, proteomics, and epigenomics, facilitate the study of cellular heterogeneity, tissue organization, and cell–cell interactions within their native environments. Despite challenges in data complexity and integrat

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Huang, Xinlei, Zhiqi Ma, Dian Meng, et al. "PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 326–33. https://doi.org/10.1609/aaai.v39i1.32010.

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Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of sem

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Ma, Yixiao, Wenting Shi, Yahong Dong, Yingjie Sun, and Qiguan Jin. "Spatial Multi-Omics in Alzheimer’s Disease: A Multi-Dimensional Approach to Understanding Pathology and Progression." Current Issues in Molecular Biology 46, no. 5 (2024): 4968–90. http://dx.doi.org/10.3390/cimb46050298.

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Alzheimer’s Disease (AD) presents a complex neuropathological landscape characterized by hallmark amyloid plaques and neurofibrillary tangles, leading to progressive cognitive decline. Despite extensive research, the molecular intricacies contributing to AD pathogenesis are inadequately understood. While single-cell omics technology holds great promise for application in AD, particularly in deciphering the understanding of different cell types and analyzing rare cell types and transcriptomic expression changes, it is unable to provide spatial distribution information, which is crucial for unde

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Goodwin, Richard J. A., Stefan J. Platz, Jorge S. Reis-Filho, and Simon T. Barry. "Accelerating Drug Development Using Spatial Multi-omics." Cancer Discovery 14, no. 4 (2024): 620–24. http://dx.doi.org/10.1158/2159-8290.cd-24-0101.

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Summary: Spatial biology approaches enabled by innovations in imaging biomarker platforms and artificial intelligence–enabled data integration and analysis provide an assessment of patient and disease heterogeneity at ever-increasing resolution. The utility of spatial biology data in accelerating drug programs, however, requires balancing exploratory discovery investigations against scalable and clinically applicable spatial biomarker analysis.

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Fan, Rong. "Integrative spatial protein profiling with multi-omics." Nature Methods 21, no. 12 (2024): 2223–25. https://doi.org/10.1038/s41592-024-02533-x.

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Liang, Weizheng, Zhenpeng Zhu, Dandan Xu, et al. "The burgeoning spatial multi-omics in human gastrointestinal cancers." PeerJ 12 (September 13, 2024): e17860. http://dx.doi.org/10.7717/peerj.17860.

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The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body’s three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell mul

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Sun, Yueqiu, Nianzuo Yu, Junhu Zhang, and Bai Yang. "Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics." Micromachines 16, no. 4 (2025): 426. https://doi.org/10.3390/mi16040426.

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The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis an

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Tessem, M.-B., E. Midtbust, T. S. Høiem, et al. "Spatial multi-omics to uncover prostate cancer heterogeneity." European Urology Open Science 56 (October 2023): S43. http://dx.doi.org/10.1016/s2666-1683(23)01127-8.

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R. Tjandrawinata, Raymond, Catherine Rebeca, and Agustina Nurcahyanti. "Spatiotemporal Omics: Integrating Multi-Omics Data for Translational Research and Drug Development." Asian Journal of Engineering, Social and Health 4, no. 3 (2025): 686–705. https://doi.org/10.46799/ajesh.v4i3.561.

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Spatiotemporal omics is an innovative approach that integrates various multi-omics data—such as genomics, transcriptomics, proteomics, epigenomics, and metabolomics—within spatial and temporal contexts to provide a comprehensive understanding of biological systems. This approach aims to uncover cellular dynamics, molecular interactions, and disease mechanisms across diverse fields, including neuroscience, developmental biology, cancer research, and precision medicine. Cutting-edge technologies such as Stereo-seq, Slide-seq, DBiT-seq, and MISAR-seq enable high-resolution mapping of gene and pro

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Dong, Xianjun, Chunyu Liu, and Mikhail Dozmorov. "Review of multi-omics data resources and integrative analysis for human brain disorders." Briefings in Functional Genomics 20, no. 4 (2021): 223–34. http://dx.doi.org/10.1093/bfgp/elab024.

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Abstract In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer’s disease, Parkinson’s disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We fu

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Book chapters on the topic "Spatial multi-omics"

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Coronado, Michael, Emily Neag, Nikhil Gandikota, et al. "An overview of single-cell omics, spatial omics, and omics integration in axon regeneration." In Proteomics, Multi-Omics and Systems Biology in Optic Nerve Regeneration. Elsevier, 2025. https://doi.org/10.1016/b978-0-443-15580-2.00031-x.

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Ernawati, Kholis, Hamzah Hasyim, Ahmad Rusdan Handoyo Utomo, Dicky Budiman, and Dian Widiyanti. "Digital Exposome." In Cancer Exposomics and Environmental Influences on Carcinogenesis. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-2165-3.ch008.

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The digital exposome integrates digital technologies, multi-omics analysis, and artificial intelligence to holistically assess environmental exposures across human life, particularly their contribution to cancer risk. Chemical, physical, biological, social, and behavioral factors are critically examined, emphasizing prenatal vulnerability. Wearable sensors, geographic information systems (GIS), and spatial mapping technologies enable real-time biomonitoring and precise visualization of exposures. Multi-omics and epigenetic modeling further clarify biological mechanisms linking exposure to cancer, while machine learning supports big data-driven risk classification and identification of vulnerable populations. This approach enhances early detection and targeted prevention strategies and supports inclusive, evidence-based public health policies.

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Biradar, Shantagoud, Chaaya Suresh, Nagashri Nanjundeshwara, and Ramya Raghavan. "Spatially Variable Genes." In Advances in Medical Diagnosis, Treatment, and Care. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7728-4.ch001.

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The clinical translation of spatial transcriptomics represents cancer diagnosis and therapy based on the role and heterogeneity of cancer-associated fibroblasts (CAFs) within the tumor microenvironment (TME). Recent developments in spatial transcriptomics have enabled a detailed characterization of the spatial organization and cellular interactions within tumors. The data integration, multi-omics approaches, along with developing standardized protocols is essential for effective clinical translation. The experimental selection regimes and factorial designs reveals novel insights into the biomarkers and prognostic value of CAFs. The incorporation of optogenetics in cancer therapy and advancements in bio-engineered gene circuits, cellular therapeutics and tissue engineering further underscores the potential to refine patient stratification and improve treatment responsiveness. By integrating spatial transcriptomics into clinical workflows, this work aims to advance personalized cancer therapies and cancer biology.

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Conference papers on the topic "Spatial multi-omics"

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Wang, Bo, Wei Liu, Jiawei Luo, Xiangtao Chen, and Chee Keong Kwoh. "SMMGCL: a novel multi-level graph contrastive learning framework for integrating spatial multi-omics data." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822097.

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Mehrabian, Hatef, Sangeetha Mahadevan, Michael Sharpnack, Christina Moon, and Lauri Diehl. "Integration of spatial transcriptomics and immunofluorescence staining to enable colocalized multi-omics analysis in chronic liver disease." In Digital and Computational Pathology, edited by John E. Tomaszewski and Aaron D. Ward. SPIE, 2025. https://doi.org/10.1117/12.3047319.

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Nilges, Benedikt S., Paul Kießling, Mar MMuniz Moreno, Niklas Klümper, Markus Eckstein, and Christoph Kuppe. "214 Decoding ADC-response in urothelial cancer with spatial multi-omics." In SITC 39th Annual Meeting (SITC 2024) Abstracts. BMJ Publishing Group Ltd, 2024. http://dx.doi.org/10.1136/jitc-2024-sitc2024.0214.

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Eng, Christine L., Joe P. Yeong, Andy Nguyen, et al. "Abstract 3872: Spatial and multi-omics characterization of the tumor microenvironment in colorectal cancer." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-3872.

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Bordignon, Pino, Alice Comberlato, Saska Brajkovic, and Diego Dupouy. "38 Fully automated spatial multi-omics analysis to map the tumor microenvironment with single-cell resolution." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.0038.

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Madissoon, Elo, Amanda Oliver, Vitalii Kleshchevnikov, et al. "The multi-omics spatial lung atlas reveales new cell states and their functions in airway mesenchyme." In ERS Lung Science Conference 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/23120541.lsc-2022.260.

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Wacker, Marcel, Gioele Medici, Marissa Dubbelaar, et al. "1463 The intra-tumoral spatial heterogeneity of T cell antigens in glioblastoma: An integrated multi-omics approach." In SITC 38th Annual Meeting (SITC 2023) Abstracts. BMJ Publishing Group Ltd, 2023. http://dx.doi.org/10.1136/jitc-2023-sitc2023.1463.

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Lau, Mai Chan, Lisda Suteja, Lawrence Cheung, et al. "867 Deciphering of radiotherapy-induced immunomodulation effect synergized with immunotherapy in hepatocellular carcinoma by spatial multi-omics profiling." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.0867.

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Christians, Arne, Jannik Boog, Thore Boettke, et al. "78 Spatial multi-omics analysis of the tumor microenvironment utilizing high-plex HCR™ RNA-FISH and high-resolution immunofluorescence." In SITC 39th Annual Meeting (SITC 2024) Abstracts. BMJ Publishing Group Ltd, 2024. http://dx.doi.org/10.1136/jitc-2024-sitc2024.0078.

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Sidiropoulos, Dimitrios N., Sarah M. Shin, Alexander Girgis, et al. "209 Spatial multi-omics reveal humoral immunity niches associated with tertiary lymphoid structures in pancreatic cancer pathologic responders to neoadjuvant immunotherapy." In SITC 39th Annual Meeting (SITC 2024) Abstracts. BMJ Publishing Group Ltd, 2024. http://dx.doi.org/10.1136/jitc-2024-sitc2024.0209.

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