Log in

Relevant bibliographies by topics / Spatial multi-omics / Journal articles

To see the other types of publications on this topic, follow the link: Spatial multi-omics.

Journal articles on the topic 'Spatial multi-omics'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Spatial multi-omics.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

Fertig, Elana J. "Abstract IA14: Forecasting pancreatic carcinogenesis through spatial multi-omics." Cancer Research 84, no. 2_Supplement (2024): IA14. http://dx.doi.org/10.1158/1538-7445.panca2023-ia14.

Full text

Abstract:

Abstract Combining genomics with mathematical modeling provides a forecast system that can yield computational predictions to anticipate cancer progression and therapeutic response. High-throughput profiling technologies can indicate the molecular and cellular pathways of malignancies, but not the effect of targeting those pathways with therapy. Precision interception requires relating therapies to the cellular phenotypes underlying pancreatic carcinogenesis. This talk presents a hybrid computational and experimental strategy to uncover interactions between neoplastic cells and the microenviro

APA, Harvard, Vancouver, ISO, and other styles

12

Ma, Yanxia, Nhat Nguyen, Sanjay Singh, et al. "EPCO-07. INTEGRATING SPATIALLY RESOLVED MULTI-OMICS DATA TO UNCOVER DYSFUNCTIONAL METABOLISM DRIVEN NETWORKS THAT ENHANCE INFILTRATION OF DIFFUSE GLIOMAS." Neuro-Oncology 26, Supplement_8 (2024): viii2. http://dx.doi.org/10.1093/neuonc/noae165.0007.

Full text

Abstract:

Abstract BACKGROUND Diffuse infiltration is an aggressive feature of high-grade gliomas with survival implications. The contribution of crosstalk between non-neoplastic and neoplastic cells to tumor infiltration remains largely understudied due to the lack of profiling techniques that retain spatial information. Spatial multi-omic profiling is a promising approach to comprehensively analyze transcript-omics, prote-omics and metabol-omics on the same tissue section while preserving information about the spatial organization of cells. Integration of these spatial studies allows for inferring the

APA, Harvard, Vancouver, ISO, and other styles

13

Akhtar, Arshi, Rojina Khatun, Sudeshna Sengupta, and Malavika Bhattacharya. "Role of Multi-Omics in Disease Biology." Applied Sciences Research Periodicals 3, no. 03 (2025): 02–21. https://doi.org/10.63002/asrp.303.925.

Full text

Abstract:

Multi-omics is a cutting-edge approach that integrates multiple biological data types, such as genomics, proteomics, and metabolomics, to provide a comprehensive understanding of diseases. By analysing molecular interactions, researchers can uncover disease mechanisms, identify biomarkers, and develop targeted therapies. This approach is central to precision medicine, which tailors treatments based on an individual's molecular profile, improving outcomes and reducing side effects. Data integration is a crucial aspect of multi-omics, as it combines vast datasets from different omics layers. Adv

APA, Harvard, Vancouver, ISO, and other styles

14

Su, Graham, Xiaoyu Qin, Archibald Enninful, et al. "Spatial multi-omics sequencing for fixed tissue via DBiT-seq." STAR Protocols 2, no. 2 (2021): 100532. http://dx.doi.org/10.1016/j.xpro.2021.100532.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Du, Yanhua, Xinyu Ding, and Youqiong Ye. "The spatial multi-omics revolution in cancer therapy: Precision redefined." Cell Reports Medicine 5, no. 9 (2024): 101740. http://dx.doi.org/10.1016/j.xcrm.2024.101740.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Yip, Raymond K. H., Edwin D. Hawkins, Rory Bowden, and Kelly L. Rogers. "Towards deciphering the bone marrow microenvironment with spatial multi-omics." Seminars in Cell & Developmental Biology 167 (March 2025): 10–21. https://doi.org/10.1016/j.semcdb.2025.01.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Fertig, Elana Judith. "Abstract PL03-04: Forecasting pancreatic carcinogenesis through spatial multi-omics." Cancer Research 83, no. 7_Supplement (2023): PL03–04—PL03–04. http://dx.doi.org/10.1158/1538-7445.am2023-pl03-04.

Full text

Abstract:

Abstract This talk presents a hybrid computational and experimental strategy to uncover interactions between neoplastic cells and the microenvironment during pancreatic carcinogenesis. As pancreatic cancer develops, it forms a complex microenvironment of multiple interacting cells. The microenvironment of advanced pancreatic cancer includes a dense composition of cells, such as macrophages and fibroblasts, that are associated with immunosuppression. New single-cell and spatial molecular profiling technologies enable unprecedented characterization of the cellular and molecular composition of th

APA, Harvard, Vancouver, ISO, and other styles

18

Zhang, Yaqi, Qiangjun Chen, Lei Wang, et al. "Spatially-resolved characterization of the metabolic and N-glycan alterations in colorectal cancer using matrix-assisted laser desorption/ionization mass spectrometry imaging." RSC Advances 15, no. 3 (2025): 1838–45. https://doi.org/10.1039/d4ra08100e.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Fan, Rong. "SINGLE-CELL AND SPATIAL OMICS FOR MAPPING CELLULAR SENESCENCE IN HEALTH, AGING AND DISEASE." Innovation in Aging 7, Supplement_1 (2023): 473. http://dx.doi.org/10.1093/geroni/igad104.1555.

Full text

Abstract:

Abstract NIH SenNet consortium aims to dissect the heterogeneity of senescent cells (SnCs) and map their impact on the microenvironment at a single cell resolution and in the spatial tissue context, which requires the implementation of an array of omics technologies to comprehensively identify, characterize, and spatially profile SnCs across tissues in humans and mice. These technologies are broadly categorized into two groups –single cell omics and spatial mapping. To achieve single cell resolution and overcome the scarcity of SnCs, high-throughput single-cell and single-nucleus transcriptomi

APA, Harvard, Vancouver, ISO, and other styles

20

Neil, Emily, Dongju Park, Erica Lloyd, et al. "Abstract 5643: High-spatial-resolution multi-omics analysis of cancer tissues." Cancer Research 83, no. 7_Supplement (2023): 5643. http://dx.doi.org/10.1158/1538-7445.am2023-5643.

Full text

Abstract:

Abstract Recent technological breakthroughs in single cell sequencing have revealed that malignant cells and nonmalignant cells are both highly dynamic and in the case of tumor cells, that the intratumoral heterogeneity is quite remarkable. However, several questions remain, especially in a spatial context, and a rapidly growing need has been observed for imaging-based approaches with single-cell resolution to visualize and characterize the tumor microenvironment. In the current study, we used Miltenyi Biotec newly developed spatial gene expression technology and automated imaging platform, to

APA, Harvard, Vancouver, ISO, and other styles

21

Jin, Yuxin, Jing Qian, Michelle G. Webb, et al. "Abstract 5359: Multi-omics characterization of copy number variation in high-grade serous ovarian cancer." Cancer Research 85, no. 8_Supplement_1 (2025): 5359. https://doi.org/10.1158/1538-7445.am2025-5359.

Full text

Abstract:

Abstract Ovarian cancer is one of the most lethal gynecologic malignancies, primarily due to its late-stage diagnosis and intrinsic tumor heterogeneity. Histologically, epithelial ovarian cancer (EOC) accounts for approximately 70% of cases and exhibits distinct molecular and clinical characteristics. High-grade serous ovarian cancer (HGSOC), the most aggressive form of EOC, stands out for its widespread genomic instability and poor prognosis. Copy number variations (CNVs) play a critical role in tumor heterogeneity, treatment resistance, and disease progression. While CNVs have been extensive

APA, Harvard, Vancouver, ISO, and other styles

22

Toyama, Yumiko, Takashi Nirasawa, Maho Morishima, et al. "Integrated Spatial Multi-Omics Study of Postmortem Brains of Alzheimer’s Disease." ACTA HISTOCHEMICA ET CYTOCHEMICA 57, no. 3 (2024): 119–30. http://dx.doi.org/10.1267/ahc.24-00025.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Liu, Yang, Mingyu Yang, Yanxiang Deng, et al. "High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue." Cell 183, no. 6 (2020): 1665–81. http://dx.doi.org/10.1016/j.cell.2020.10.026.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Liapis, Evangelos, Annapurna Pamreddy, Kelly O’Neill, Allison Maas, Derek Hanson, and Claire Carter. "ATRT-18. ELUCIDATING THE ETMR MICROENVIRONMENT USING SPATIAL MULTI-OMICS TECHNOLOGIES." Neuro-Oncology 25, Supplement_1 (2023): i5. http://dx.doi.org/10.1093/neuonc/noad073.018.

Full text

Abstract:

Abstract Embryonal tumor with multilayered rosettes (ETMR) is a highly aggressive CNS neoplasm that occurs almost exclusively in infants under 4 years of age and is associated with an extremely poor prognosis. A bottleneck in the development of new and curative treatments for ETMR stems from our limited understanding of the spatiotemporal biological heterogeneity within patient tumor samples and a lack of preclinical models that adequately reflect the entire biological spectrum. Novel lipid-based therapeutics that encompass ganglioside-directed immunotherapy, targeting lipid metabolism and mem

APA, Harvard, Vancouver, ISO, and other styles

25

Rodríguez-Durán, Arlex, Vinícius Andrade-Silva, Muhammad Numan, et al. "Multi-Omics Technologies Applied to Improve Tick Research." Microorganisms 13, no. 4 (2025): 795. https://doi.org/10.3390/microorganisms13040795.

Full text

Abstract:

The advancement of multi-omics technologies is crucial to deepen knowledge on tick biology. These approaches, used to study diverse phenomena, are applied to experiments that aim to understand changes in gene transcription, protein function, cellular processes, and prediction of systems at global biological levels. This review addressed the application of omics data to investigate and elucidate tick physiological processes, such as feeding, digestion, reproduction, neuronal, endocrine systems, understanding population dynamics, transmitted pathogens, control, and identifying new vaccine target

APA, Harvard, Vancouver, ISO, and other styles

26

Ibekwe, Paul-Miki Raluchukwu, Elizabeth Anuoluwa Akintayo, Cecilia Ndiuwem Okuku, et al. "Decoding Tumor Heterogeneity through Multi Omics: Insights into Cancer Evolution, Microenvironment and Therapy Resistance." Journal of Cancer and Tumor International 15, no. 3 (2025): 91–112. https://doi.org/10.9734/jcti/2025/v15i3305.

Full text

Abstract:

Background: The existence of genetically, epigenetically, and phenotypically distinct cell populations inside and between tumors is known as tumor heterogeneity, and it is one of the main barriers to effective cancer treatment. This intricacy affects the likelihood of metastasis, therapeutic resistance, and disease recurrence, rendering single-omics methods and conventional diagnostics inadequate for whole-tumor profiling. As a result, multi-omics methods, which incorporate data from multiple biological layers, such as transcriptomics, proteomics, metabolomics, genomes, and epigenomics, have e

APA, Harvard, Vancouver, ISO, and other styles

27

Cai, Guangchang, Fuqun Chen, Kepei Wen, Ying Li, and Le Ou-Yang. "Identifying spatial domains from spatial multi-omics data using consistent and specific deep subspace learning." Information Fusion 125 (January 2026): 103428. https://doi.org/10.1016/j.inffus.2025.103428.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Bowie, William, Stacy Wang, Benjamin Strope, and Qian Zhu. "Abstract 2335: MultiNMF: multiview factorization for joint modeling of spatial multi-omics and histology images." Cancer Research 84, no. 6_Supplement (2024): 2335. http://dx.doi.org/10.1158/1538-7445.am2024-2335.

Full text

Abstract:

Abstract An increasing number of cancer research studies employ spatially resolved transcriptomics (SRT) to investigate the composition of tumor microenvironment in a cancer type of interest. These studies have defined tumor microenvironment (TME) states and spatial domains based on clustering spatial gene expression patterns in SRT in an unbiased manner, yet a more thorough delineation of TME states requires the incorporation of the tumor’s histology image. Here, we develop MultiNMF, a multiview factorization approach that is suitable for cancer research studies where joint profiles of spatia

APA, Harvard, Vancouver, ISO, and other styles

29

Chong, Li Yen, Craig Ryan Joseph, Felicia Wee, et al. "A universal pipeline to combine spatial transcriptomics, proteomics, and diagnostic H&E assays on a single tissue section to study tissue microenvironment." Journal of Clinical Oncology 42, no. 16_suppl (2024): e14657-e14657. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e14657.

Full text

Abstract:

e14657 Background: Spatial multi-omics approaches, including spatial transcriptomics (ST) and spatial proteomics (SP), that elucidate the complex tissue microenvironment (TME) are gaining traction in cancer research studies. However, combining these approaches onto one tissue section remains challenging. ST methods visualize, locate and quantify RNA targets, evaluating the transcriptome of the TME at the subcellular level while SP methods stain and visualize protein biomarkers at the cellular level. Here we show a pipeline that combines these ST and SP methods with Hematoxylin and Eosin (H&amp

APA, Harvard, Vancouver, ISO, and other styles

30

Krishnan, Santhoshi N., Sunjong Ji, Ahmed M. Elhossiny, Achyutha Rao, Timothy L. Frankel, and Arvind Rao. "Proximogram—A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and spatial profiling." Computers in Biology and Medicine 182 (November 2024): 109082. http://dx.doi.org/10.1016/j.compbiomed.2024.109082.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Wang, Le, and Bo Jin. "Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease." Biology 13, no. 10 (2024): 783. http://dx.doi.org/10.3390/biology13100783.

Full text

Abstract:

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will in

APA, Harvard, Vancouver, ISO, and other styles

32

Wang, Yuhan. "High-Throughput Analysis of Core Pathways in HeLa Cells: Single-Cell Sequencing and AI-driven Modeling of Multi-Pathway Interaction Networks." Applied and Computational Engineering 178, no. 1 (2025): 47–57. https://doi.org/10.54254/2755-2721/2025.po25414.

Full text

Abstract:

HeLa cells, the first successfully cultured human cancer cell line, are pivotal in cancer research, virology, and drug screening. However, their multi-omics heterogeneity and complex cancer-related cascades challenge traditional bulk sequencing, which fails to capture dynamic cell-cell interactions and resolve pathway crosstalk. This review systematically examines single-cell multi-omics technologies (transcriptomics, proteomics, and data integration) and AI-driven network modeling (graph neural networks, deep learning) for decoding HeLa cells' core pathways and metastasis mechanisms. It revea

APA, Harvard, Vancouver, ISO, and other styles

33

Lukowski, Jessica K., Byoung‐Kyu Cho, Antonia Zamacona Calderon, et al. "Advances in Spatial Multi‐Omics: A Review of Multi‐Modal Mass Spectrometry Imaging and Laser Capture Microdissection‐LCMS Integration." PROTEOMICS, May 12, 2025. https://doi.org/10.1002/pmic.202400378.

Full text

Abstract:

ABSTRACTMass spectrometry has long been utilized to characterize a variety of biomolecules such as proteins, metabolites, and lipids. Most MS‐based omics studies rely on bulk analysis; however, bulk approaches often overlook low‐abundance molecules that may exert critical biological effects. Recently, multi‐omics analyses have been driving an explosion of knowledge about how biomolecules interact within biological systems. In particular, spatial multi‐omics has emerged as a groundbreaking approach for implementing multi‐omic and multi‐modal analyses. Broadly defined, spatial omics has the abil

APA, Harvard, Vancouver, ISO, and other styles

34

Lv, Tongxuan, Yong Zhang, Junlin Liu, Qiang Kang, and Lin Liu. "Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder." Briefings in Bioinformatics 25, no. 5 (2024). http://dx.doi.org/10.1093/bib/bbae450.

Full text

Abstract:

Abstract Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more comprehensive downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration of spatial information and still have room for performance improvement. A reliable multi-omics integration method designed for both single-cell and spatially resolved data is

APA, Harvard, Vancouver, ISO, and other styles

35

Vickovic, S., B. Lötstedt, J. Klughammer, et al. "SM-Omics is an automated platform for high-throughput spatial multi-omics." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-28445-y.

Full text

Abstract:

AbstractThe spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial transcriptomics has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of spatial transcriptomics at scale, by presenting Spatial Multi-Omics (SM-Omics) as a fully automated, high-throughput all-sequencing based platform for combined and spatially resolved transcriptomics and antibody-based protein measurements. SM-Omics uses DNA-barcoded antibodies, immunofluorescence or a combination the

APA, Harvard, Vancouver, ISO, and other styles

36

Long, Yahui, Kok Siong Ang, Raman Sethi, et al. "Deciphering spatial domains from spatial multi-omics with SpatialGlue." Nature Methods, June 21, 2024. http://dx.doi.org/10.1038/s41592-024-02316-4.

Full text

Abstract:

AbstractAdvances in spatial omics technologies now allow multiple types of data to be acquired from the same tissue slice. To realize the full potential of such data, we need spatially informed methods for data integration. Here, we introduce SpatialGlue, a graph neural network model with a dual-attention mechanism that deciphers spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrated SpatialGlue on data acquired from different tissue types using different technologies, including spatial epigenome–transcriptome a

APA, Harvard, Vancouver, ISO, and other styles

37

Wess, Maximilian, Maria K. Andersen, Elise Midtbust, et al. "Spatial integration of multi-omics data from serial sections using the novel Multi-Omics Imaging Integration Toolset." GigaScience 14 (2025). https://doi.org/10.1093/gigascience/giaf035.

Full text

Abstract:

Abstract Background Truly understanding the cancer biology of heterogeneous tumors in precision medicine requires capturing the complexities of multiple omics levels and the spatial heterogeneity of cancer tissue. Techniques like mass spectrometry imaging (MSI) and spatial transcriptomics (ST) achieve this by spatially detecting metabolites and RNA but are often applied to serial sections. To fully leverage the advantage of such multi-omics data, the individual measurements need to be integrated into 1 dataset. Results We present the Multi-Omics Imaging Integration Toolset (MIIT), a Python fra

APA, Harvard, Vancouver, ISO, and other styles

38

Zhang, Wenyi, Xingye Huang, Liyong He, and Xiangwei Zhao. "Advances in Spatial Multi-Omics Technologies." Chinese Science Bulletin, May 1, 2025. https://doi.org/10.1360/tb-2024-1403.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Yao, Jianing, Jinglun Yu, Brian Caffo, Stephanie C. Page, Keri Martinowich, and Stephanie C. Hicks. "Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies." Genome Research, May 20, 2025, gr.279380.124. https://doi.org/10.1101/gr.279380.124.

Full text

Abstract:

Recent advances in spatially-resolved single-omics and multi-omics technologies have led to the emergence of computational tools to detect or predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins and cell types provide multiple perspectives and a more complete understanding of tissue architecture. Here, we introduce Proust, a scalable tool to predict discrete domains using spatial multi-omics data by combining the low-dimensional representation of biological profiles based on graph-based contrastive self-supervised learning. Our scalable me

APA, Harvard, Vancouver, ISO, and other styles

40

Shao, Yanping, Xiuyan Lv, Shuangwei Ying, and Qunyi Guo. "Artificial Intelligence-Driven Precision Medicine: Multi-Omics and Spatial Multi-Omics Approaches in Diffuse Large B-Cell Lymphoma (DLBCL)." Frontiers in Bioscience-Landmark 29, no. 12 (2024). https://doi.org/10.31083/j.fbl2912404.

Full text

Abstract:

In this comprehensive review, we delve into the transformative role of artificial intelligence (AI) in refining the application of multi-omics and spatial multi-omics within the realm of diffuse large B-cell lymphoma (DLBCL) research. We scrutinized the current landscape of multi-omics and spatial multi-omics technologies, accentuating their combined potential with AI to provide unparalleled insights into the molecular intricacies and spatial heterogeneity inherent to DLBCL. Despite current progress, we acknowledge the hurdles that impede the full utilization of these technologies, such as the

APA, Harvard, Vancouver, ISO, and other styles

41

Li, Longyi, Liyan Dong, Hao Zhang, Dong Xu, and Yongli Li. "spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration." Briefings in Bioinformatics 26, no. 4 (2025). https://doi.org/10.1093/bib/bbaf304.

Full text

Abstract:

Abstract Spatial multi-omics technologies provide valuable data on gene expression from various omics in the same tissue section while preserving spatial information. However, deciphering spatial domains within spatial omics data remains challenging due to the sparse gene expression. We propose spaLLM, the first multi-omics spatial domain analysis method that integrates large language models to enhance data representation. Our method combines a pre-trained single-cell language model (scGPT) with graph neural networks and multi-view attention mechanisms to compensate for limited gene expression

APA, Harvard, Vancouver, ISO, and other styles

42

Zuo, Chunman, Junchao Zhu, Jiawei Zou, and Luonan Chen. "Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data." Clinical and Translational Medicine 15, no. 5 (2025). https://doi.org/10.1002/ctm2.70331.

Full text

Abstract:

AbstractAnalysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi‐omics technologies now reveal complex molecular interactions shaping cellular behaviour and tissue dynamics. This review highlights key technologies and computational methods that have advanced spatial domain identification and their pseudo‐relations, as well as inference of intra‐ and inter‐cellular molecular networks that drive disease progression. We also discuss strategies to

APA, Harvard, Vancouver, ISO, and other styles

43

Kiessling, Paul, and Christoph Kuppe. "Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases." Genome Medicine 16, no. 1 (2024). http://dx.doi.org/10.1186/s13073-024-01282-y.

Full text

Abstract:

AbstractSpatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applyi

APA, Harvard, Vancouver, ISO, and other styles

44

Wang, Junyan, Ahmad Alhaskawi, Yanzhao Dong, Tu Tian, Sahar Ahmed Abdalbary, and Hui Lu. "Advances in spatial multi-omics in tumors." Tumori Journal, August 26, 2024. http://dx.doi.org/10.1177/03008916241271458.

Full text

Abstract:

Single-cell techniques have convincingly demonstrated that tumor tissue usually contains multiple genetically defined cell subclones with different gene mutation sets as well as various transcriptional profiles, but the spatial heterogeneity of the microenvironment and the macrobiological characteristics of the tumor ecosystem have not been described. For the past few years, spatial multi-omics technologies have revealed the cellular interactions, microenvironment, and even systemic tumor-host interactions in the tumor ecosystem at the spatial level, which can not only improve classical therap

APA, Harvard, Vancouver, ISO, and other styles

45

Liu, Xiaojie, Ting Peng, Miaochun Xu, et al. "Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications." Journal of Hematology & Oncology 17, no. 1 (2024). http://dx.doi.org/10.1186/s13045-024-01596-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Sokač, Mateo, Asbjørn Kjær, Lars Dyrskjøt, Benjamin Haibe-Kains, Hugo JWL Aerts, and Nicolai J. Birkbak. "Spatial transformation of multi-omics data unlocks novel insights into cancer biology." eLife 12 (September 5, 2023). http://dx.doi.org/10.7554/elife.87133.3.

Full text

Abstract:

The application of next-generation sequencing (NGS) has transformed cancer research. As costs have decreased, NGS has increasingly been applied to generate multiple layers of molecular data from the same samples, covering genomics, transcriptomics, and methylomics. Integrating these types of multi-omics data in a combined analysis is now becoming a common issue with no obvious solution, often handled on an ad hoc basis, with multi-omics data arriving in a tabular format and analyzed using computationally intensive statistical methods. These methods particularly ignore the spatial orientation o

APA, Harvard, Vancouver, ISO, and other styles

47

Wang, Mingcheng, Shuqiao Zhang, Rui Li, and Qi Zhao. "Unraveling the specialized metabolic pathways in medicinal plant genomes: a review." Frontiers in Plant Science 15 (December 24, 2024). https://doi.org/10.3389/fpls.2024.1459533.

Full text

Abstract:

Medicinal plants are important sources of bioactive specialized metabolites with significant therapeutic potential. Advances in multi-omics have accelerated the understanding of specialized metabolite biosynthesis and regulation. Genomics, transcriptomics, proteomics, and metabolomics have each contributed new insights into biosynthetic gene clusters (BGCs), metabolic pathways, and stress responses. However, single-omics approaches often fail to fully address these complex processes. Integrated multi-omics provides a holistic perspective on key regulatory networks. High-throughput sequencing a

APA, Harvard, Vancouver, ISO, and other styles

48

Yu, Xudong, Ruijia Liu, Wenfeng Gao, Xuyun Wang, and Yaosheng Zhang. "Single-cell omics traces the heterogeneity of prostate cancer cells and the tumor microenvironment." Cellular & Molecular Biology Letters 28, no. 1 (2023). http://dx.doi.org/10.1186/s11658-023-00450-z.

Full text

Abstract:

AbstractProstate cancer is one of the more heterogeneous tumour types. In recent years, with the rapid development of single-cell sequencing and spatial transcriptome technologies, researchers have gained a more intuitive and comprehensive understanding of the heterogeneity of prostate cancer. Tumour-associated epithelial cells; cancer-associated fibroblasts; the complexity of the immune microenvironment, and the heterogeneity of the spatial distribution of tumour cells and other cancer-promoting molecules play a crucial role in the growth, invasion, and metastasis of prostate cancer. Single-c

APA, Harvard, Vancouver, ISO, and other styles

49

"A machine learning tool for spatial multi-omics." Nature Methods, July 5, 2024. http://dx.doi.org/10.1038/s41592-024-02358-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Deng, Yanxiang, Zhiliang Bai, and Rong Fan. "Microtechnologies for single-cell and spatial multi-omics." Nature Reviews Bioengineering, June 27, 2023. http://dx.doi.org/10.1038/s44222-023-00084-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!