Zeitschriftenartikel: „Spatial omics“

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Schueder, Florian, und Joerg Bewersdorf. „Omics goes spatial epigenomics“. Cell 185, Nr. 23 (November 2022): 4253–55. http://dx.doi.org/10.1016/j.cell.2022.10.014.

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Lee, Sumin, Amos C. Lee und Sunghoon Kwon. „Abstract 5639: High throughput spatially resolved laser-activated cell sorting links the genomic molecules with its spatial information“. Cancer Research 83, Nr. 7_Supplement (04.04.2023): 5639. http://dx.doi.org/10.1158/1538-7445.am2023-5639.

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Abstract Spatial omics profiling technologies have been recognized recently for its ability to decipher the genetic molecules that are structurally relevant in pathology. Especially, in tumor biology, tumor is not the group of malignant tumor cells, but rather group of various cells such as tumor cells, immune cells, fibroblasts, etc. gathers together, constructing the tumor microenvironments. Technologies to analyze such microstructures have evolved from bulk sequencing, single cell sequencing to spatial omics profiling technologies. Spatial omics profiling technologies have highly influenced in decoding cancerous mechanisms by questioning the tumor heterogeneity, tumor microenvironment and spatial biomarkers. Most of the spatial omics technologies focus on mapping the spatial omics landscape in a large scale. They rather introduces the spatially-barcoded capture probes or fluorescence labeled target probes to spatially locate the genetic molecules. The information depth and the scalability of the techniques varies according to the purpose of the spatial assay techniques. Such technologies are capable of discovering the spatial heterogeneity and the spatial landscape of the consisting cell types due to relatively low depth of the omics information. To effectively address the target molecules for therapeutics or diagnostics, higher depth of the omics information are required. To meet the needs, region of interest (ROI)-based spatial technologies isolated the target regions and applies chemistries for higher coverage omics data. Conventional cell sorters utilizes microfluidic channels to sort cells of interest which requires cell dissociation in a solution phase. For instance, Fluorescence activated cell sorter (FACS) or Magnetic-activated cell sorting (MACS) uses fluorescence or magnetic particles, respectively, to designate the cells of interest in dissociated cell solutions. Spatially isolating techniques such as laser capture microdissection (LCM) is able to sort out the ROIs while preserving the spatial context, but it approximately takes an hour for isolating the targets. Also, it uses rather UV laser to dissect out cells or IR-activated melting of polymers to stick out cells which might cause damage to cells. Here, I developed the automated spatially resolved laser activated cell sorter that isolates the cells in target per second while preserving the spatial context of the cells. Specific region of indium tin oxide (ITO) coated slide glass evaporates when illuminated by IR laser pulse, plunging the cells into the desired reservoir. The applicability of the suggested cell sorter are demonstrated in omics profiling chemistries such as DNA sequencing, RNA sequencing, mass spectrometry, etc. Citation Format: Sumin Lee, Amos C. Lee, Sunghoon Kwon. High throughput spatially resolved laser-activated cell sorting links the genomic molecules with its spatial information. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5639.

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Xu, Tinghui, und Kris Sankaran. „Interactive visualization of spatial omics neighborhoods“. F1000Research 11 (18.07.2022): 799. http://dx.doi.org/10.12688/f1000research.122113.1.

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Dimensionality reduction of spatial omic data can reveal shared, spatially structured patterns of expression across a collection of genomic features. We studied strategies for discovering and interactively visualizing low-dimensional structure in spatial omic data based on the construction of neighborhood features. We designed quantile and network-based spatial features that result in spatially consistent embeddings. A simulation compares embeddings made with and without neighborhood-based featurization, and a re-analysis of Keren et al., 2019 illustrates the overall workflow. We provide an R package, NBFvis, to support computation and interactive visualization for the proposed dimensionality reduction approach. Code and data for reproducing experiments and analysis are available on GitHub.

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LeMieux, Julianna. „Spatial The Next Omics Frontier“. Genetic Engineering & Biotechnology News 40, Nr. 10 (01.10.2020): 18–20. http://dx.doi.org/10.1089/gen.40.10.07.

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Moses, Lambda. „From Geospatial to Spatial -Omics“. XRDS: Crossroads, The ACM Magazine for Students 30, Nr. 2 (Dezember 2023): 16–19. http://dx.doi.org/10.1145/3637459.

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When Los Angeles is mentioned, cycling is usually not the first thing that comes to mind. However, during my past 10 years in LA studying molecular biology and bioinformatics, my bike trips through the geographical space of LA have inspired many ideas in my research in spatial data analysis in bioinformatics. I have written software to bring decades of research in geospatial data analysis to spatial -omics, as my trips make me ponder on spatial phenomena in general.

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Kim, Meeri. „Mapping Biology with Spatial Omics“. Optics and Photonics News 35, Nr. 4 (01.04.2024): 26. http://dx.doi.org/10.1364/opn.35.4.000026.

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Ma, Yanxia, Nhat Nguyen, Sanjay Singh, Akshay Basi, Duncan Mak, Javier Gomez, Jared Burks, Erin Seely, Frederick Lang und Chibawanye Ene. „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 (01.11.2024): viii2. http://dx.doi.org/10.1093/neuonc/noae165.0007.

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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 consequences of complex cell-cell communication underlying tumor infiltration. We hypothesize that the glioma edge is enriched with pro-infiltration ligands-receptors driving tumor infiltration. Strategies that disrupt these ligand-receptor networks may suppress glioma infiltration and improve clinical outcomes. METHODS We utilized a glioma tissue micro-array (TMA) to establish the neoplastic and non-neoplastic heterogeneity in the GBM infiltration edge. Each TMA slide consists of pathologist annotated tumor core and edge samples of Glioblastoma (IDH Wildtype, WHO Grade 4; n=10), Diffuse Astrocytoma (IDH Mutant, WHO Grade 3; n=3), Oligodendroglioma (IDH mutant, WHO Grade 2; n=5) and 2 non-brain control samples. We performed spatial multi-omics profiling on adjacent sections of the TMA using 10x Xenium spatial transcriptomics, imaging mass cytometry (IMC) and mass spectrometry imaging (MSI). Integration, visualization, and quantification of the spatial data was done on VisioPharm. RESULTS In GBM, we identified candidate mRNA transcripts and proteins for ligands enriched at the infiltrating edge (compared to tumor core; p<0.0001) that correlated with a poor progression free survival (PFS; r2=0.22). These candidate ligands were also significantly enriched at the edge of oligodendroglioma WHO Grade 2 and astrocytoma WHO Grade 3. CONCLUSIONS Spatial multi-omics profiling on a TMA consisting of Glioma WHO grades 2-4 identified differentially expressed and targetable pro-tumor infiltration ligands associated with lower PFS in GBM. Functional studies to uncover the role of metabolites enriched at the glioma edge for the expression of the identified pro-infiltration ligands are ongoing.

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Palla, Giovanni, Hannah Spitzer, Michal Klein, David Fischer, Anna Christina Schaar, Louis Benedikt Kuemmerle, Sergei Rybakov et al. „Squidpy: a scalable framework for spatial omics analysis“. Nature Methods 19, Nr. 2 (31.01.2022): 171–78. http://dx.doi.org/10.1038/s41592-021-01358-2.

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AbstractSpatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.

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Fan, Rong, und Omer Bayraktar. „Special Issue: Spatial Omics“. GEN Biotechnology 2, Nr. 1 (01.02.2023): 3–4. http://dx.doi.org/10.1089/genbio.2023.29076.cfp.

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Fan, Rong, und Omer Bayraktar. „Special Issue: Spatial Omics“. GEN Biotechnology 2, Nr. 2 (01.04.2023): 61–62. http://dx.doi.org/10.1089/genbio.2023.29076.cfp2.

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Zhang, Nicholas, Denis Ohlstrom, Sicheng Pang, Nivik Sanjay Bharadwaj, Aaron Qu, Hans Grossniklaus und Ahmet F. Coskun. „Tissue Spatial Omics Dissects Organoid Biomimicry“. GEN Biotechnology 2, Nr. 5 (01.10.2023): 372–83. http://dx.doi.org/10.1089/genbio.2023.0039.

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Fan, Rong. „SINGLE-CELL AND SPATIAL OMICS FOR MAPPING CELLULAR SENESCENCE IN HEALTH, AGING AND DISEASE“. Innovation in Aging 7, Supplement_1 (01.12.2023): 473. http://dx.doi.org/10.1093/geroni/igad104.1555.

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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 transcriptomic techniques have become a mainstay tool for surveying tens of thousands of cells to identify transcriptional signatures in rare cell populations, enabling discovery of potential new SnC biomarkers. Novel single cell mass spectrometry methods are developed for unbiased discovery of proteomic signatures of SnCs. A hallmark of SnCs is the senescence-associated secretory phenotype (SASP), which requires the use of proteomics, secretomics, metabolomics and lipidomics, especially SASP-associated extracellular vesicles, for comprehensive characterization of SAPS. High resolution molecular and cellular imaging of gene expression (e.g., MERFISH) or protein markers (e.g., CODEX) is critical for the study of SnCs in the large-scale tissue context. NGS-based spatial omics sequencing is poised to bridge the gap to realize both genome scale and cellular resolution in mapping SnCs in tissue. Novel technologies such as Seq-Scope and Pixel-Seq developed within SenNet further enabled subcellular resolution. SenNet investigators also developed spatially resolved epigenome and multi-omics sequencing techniques to link transcriptional or proteomic phenotype of SnCs to epigenetic mechanism. Further integration with high-resolution imaging makes spatial omics the crucial linchpin in connecting mechanistic underpinnings and molecular signatures with morphological features and spatial distribution. All these are critical for the construction of a map of SnCs and associated niches in the native tissue environment implicated in human health, aging, and disease, which is one of the main goals of the SenNet consortium.

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Mulholland, EJ, und SJ Leedham. „Redefining clinical practice through spatial profiling: a revolution in tissue analysis“. Annals of The Royal College of Surgeons of England 106, Nr. 4 (April 2024): 305–12. http://dx.doi.org/10.1308/rcsann.2023.0091.

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Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

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Ma, Yixiao, Wenting Shi, Yahong Dong, Yingjie Sun und Qiguan Jin. „Spatial Multi-Omics in Alzheimer’s Disease: A Multi-Dimensional Approach to Understanding Pathology and Progression“. Current Issues in Molecular Biology 46, Nr. 5 (20.05.2024): 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 understanding the pathological processes of AD. In contrast, spatial multi-omics research emerges as a promising and comprehensive approach to analyzing tissue cells, potentially better suited for addressing these issues in AD. This article focuses on the latest advancements in spatial multi-omics technology and compares various techniques. Additionally, we provide an overview of current spatial omics-based research results in AD. These technologies play a crucial role in facilitating new discoveries and advancing translational AD research in the future. Despite challenges such as balancing resolution, increasing throughput, and data analysis, the application of spatial multi-omics holds immense potential in revolutionizing our understanding of human disease processes and identifying new biomarkers and therapeutic targets, thereby potentially contributing to the advancement of AD research.

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Vermeulen, I., T. Dankcer, G. Hoogland, K. Rijkers, O. Schijns, B. Balluff, E. Cuypers und B. Cillero-Pastor. „Multimodal spatial omics in human focal epilepsy“. Brain and Spine 2 (2022): 101583. http://dx.doi.org/10.1016/j.bas.2022.101583.

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Schueder, Florian, Eduard M. Unterauer, Mahipal Ganji und Ralf Jungmann. „DNA‐Barcoded Fluorescence Microscopy for Spatial Omics“. PROTEOMICS 20, Nr. 23 (26.10.2020): 1900368. http://dx.doi.org/10.1002/pmic.201900368.

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Goodwin, Richard J. A., Stefan J. Platz, Jorge S. Reis-Filho und Simon T. Barry. „Accelerating Drug Development Using Spatial Multi-omics“. Cancer Discovery 14, Nr. 4 (04.04.2024): 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, Nr. 12 (Dezember 2024): 2223–25. https://doi.org/10.1038/s41592-024-02533-x.

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Liang, Weizheng, Zhenpeng Zhu, Dandan Xu, Peng Wang, Fei Guo, Haoshan Xiao, Chenyang Hou, Jun Xue, Xuejun Zhi und Rensen Ran. „The burgeoning spatial multi-omics in human gastrointestinal cancers“. PeerJ 12 (13.09.2024): 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 multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

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Wang, Le, und Bo Jin. „Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease“. Biology 13, Nr. 10 (30.09.2024): 783. http://dx.doi.org/10.3390/biology13100783.

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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 introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

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Shi, Jianyu, Yating Pan, Xudong Liu, Wenjian Cao, Ying Mu und Qiangyuan Zhu. „Spatial Omics Sequencing Based on Microfluidic Array Chips“. Biosensors 13, Nr. 7 (06.07.2023): 712. http://dx.doi.org/10.3390/bios13070712.

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Spatial profiling technologies fill the gap left by the loss of spatial information in traditional single-cell sequencing, showing great application prospects. After just a few years of quick development, spatial profiling technologies have made great progress in resolution and simplicity. This review introduces the development of spatial omics sequencing based on microfluidic array chips and describes barcoding strategies using various microfluidic designs with simplicity and efficiency. At the same time, the pros and cons of each strategy are compared. Moreover, commercialized solutions for spatial profiling are also introduced. In the end, the future perspective of spatial omics sequencing and research directions are discussed.

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Ali, Mayar, Merel Kuijs, Soroor Hediyeh-zadeh, Tim Treis, Karin Hrovatin, Giovanni Palla, Anna C. Schaar und Fabian J. Theis. „GraphCompass: spatial metrics for differential analyses of cell organization across conditions“. Bioinformatics 40, Supplement_1 (28.06.2024): i548—i557. http://dx.doi.org/10.1093/bioinformatics/btae242.

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Abstract Summary Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions. GraphCompass builds upon the Squidpy spatial omics toolbox and encompasses various statistical approaches to perform cross-condition analyses at the level of individual cell types, niches, and samples. Additionally, GraphCompass provides custom visualization functions that enable effective communication of results. We demonstrate how GraphCompass can be used to address key biological questions, such as how cellular organization and tissue architecture differ across various disease states and which spatial patterns correlate with a given pathological condition. GraphCompass can be applied to various popular omics techniques, including, but not limited to, spatial proteomics (e.g. MIBI-TOF), spot-based transcriptomics (e.g. 10× Genomics Visium), and single-cell resolved transcriptomics (e.g. Stereo-seq). In this work, we showcase the capabilities of GraphCompass through its application to three different studies that may also serve as benchmark datasets for further method development. With its easy-to-use implementation, extensive documentation, and comprehensive tutorials, GraphCompass is accessible to biologists with varying levels of computational expertise. By facilitating comparative analyses of cell spatial organization, GraphCompass promises to be a valuable asset in advancing our understanding of tissue function in health and disease.

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Rittel, Miriam F., Stefan Schmidt, Cleo-Aron Weis, Emrullah Birgin, Björn van Marwick, Matthias Rädle, Steffen J. Diehl, Nuh N. Rahbari, Alexander Marx und Carsten Hopf. „Spatial Omics Imaging of Fresh-Frozen Tissue and Routine FFPE Histopathology of a Single Cancer Needle Core Biopsy: A Freezing Device and Multimodal Workflow“. Cancers 15, Nr. 10 (10.05.2023): 2676. http://dx.doi.org/10.3390/cancers15102676.

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The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy.

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Lehar, Joseph, Elo Madissoon, Jerome Chevallier, Jean Baptiste Schiratti, Atanas Kamburov, Rodrigo Barnes, Carla Haignere et al. „MOSAIC: Multi-Omic Spatial Atlas in Cancer, effect on precision oncology.“ Journal of Clinical Oncology 41, Nr. 16_suppl (01.06.2023): e15076-e15076. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e15076.

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e15076 Background: Precision oncology aims to first break diagnoses into biologically distinct subtypes, then pursue personalized therapies for each group of patients ( Garraway et al, J Clin Onc, 2013). This strategy is enabled by recent advances in technologies for medical imaging, molecular profiling, and artificial intelligence. Spatially resolved molecular profiling (“spatial omics”) is an emerging technology that harnesses all three of these advances ( Navarro et al, Science, 2016; Rao et al, Nature, 2021). Cancer is driven by localized interactions between tumor cells, immune and non-immune stromal components. Methods: Spatial omics studies have revealed specific cell types and pathways that control those tumor-microenvironment crosstalks ( Hunter et al, Nat Comm, 2021; Alon et al, Science, 2021), involving both innate and adaptive immunity ( Binnewies et al, Nat Med, 2018). Identifying new cancer subtypes based on the spatially localized tumor-host interactions across many patients have the potential to revolutionize immuno-oncology. The advances in utilizing AI-based methods have so far been unattainable due to a small number of patients in spatial omics studies. Results: Here we present the MOSAIC project - a collaborative initiative across industry and top oncology hospitals to build the largest collection of spatial omics data in cancer. We combine comprehensive clinical annotation with new deep profiling methods to both discover cancer subtypes and identify drug targets and biomarkers within them. MOSAIC aims to generate multimodal data for a total of 7,000 patients in seven cancer indications. Data modalities will include spatial and single cell transcriptomics and proteomics, bulk molecular profiling, pathology images, and curated clinical information. This collaboration harnesses the strengths of academia and industry to provide patient samples, generate high-quality data, develop AI-based analytical tools, and compile a resource that will eventually be made widely available for medical research. Conclusions: Here we present the workflow of the MOSAIC study, demonstrate the data processing pipelines and show spatial distributions of transcripts within the tumor microenvironment. Building upon these initial datasets, we discuss how the MOSAIC parties will collaborate to build an unprecedented database for research and discovery of new immuno-oncology therapeutic approaches.

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Wehrle, E. „SPATIAL TRANSCRIPTOMICS OF FRACTURE HEALING“. Orthopaedic Proceedings 106-B, SUPP_1 (02.01.2024): 46. http://dx.doi.org/10.1302/1358-992x.2024.1.046.

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Fracture healing is a spatially controlled process involving crosstalk of multiple tissues. To precisely capture and understand molecular mechanism underlying impaired healing, there is a need to integrate spatially-resolved molecular analyses into preclinical fracture healing models. I will present our recent data obtained by spatial transcriptomics of musculoskeletal samples from fracture healing studies in mice. Subsequently, I will show how spatial transcriptomics can be integrated into multimodal approaches in preclinical fracture healing models. In combination with established in vivo imaging and emerging omics techniques, spatially-resolved analyses have the potential to elucidate the molecular mechanisms underlying impaired healing with optimization of treatments.

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Ahmed, Rashid, Robin Augustine, Enrique Valera, Anurup Ganguli, Nasrin Mesaeli, Irfan S. Ahmad, Rashid Bashir und Anwarul Hasan. „Spatial mapping of cancer tissues by OMICS technologies“. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1877, Nr. 1 (Januar 2022): 188663. http://dx.doi.org/10.1016/j.bbcan.2021.188663.

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Eggeling, Ferdinand, und Franziska Hoffmann. „Microdissection—An Essential Prerequisite for Spatial Cancer Omics“. PROTEOMICS 20, Nr. 17-18 (06.07.2020): 2000077. http://dx.doi.org/10.1002/pmic.202000077.

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Hua, Hui. „A new member of the spatial omics family“. Nature Methods 20, Nr. 5 (Mai 2023): 633. http://dx.doi.org/10.1038/s41592-023-01889-w.

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Gagna, Claude. „Abstract 1411 Structural Spatial Transcriptomics: Novel "Omics" Technology“. Journal of Biological Chemistry 300, Nr. 3 (März 2024): 106081. http://dx.doi.org/10.1016/j.jbc.2024.106081.

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Tessem, M.-B., E. Midtbust, T. S. Høiem, C. A. D. Pedersen, M. Wess, E. Telumyan, M. B. Rye, S. Krossa und M. K. Andersen. „Spatial multi-omics to uncover prostate cancer heterogeneity“. European Urology Open Science 56 (Oktober 2023): S43. http://dx.doi.org/10.1016/s2666-1683(23)01127-8.

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Coleman, Kyle, Amelia Schroeder und Mingyao Li. „Unlocking the power of spatial omics with AI“. Nature Methods 21, Nr. 8 (August 2024): 1378–81. http://dx.doi.org/10.1038/s41592-024-02363-x.

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Tisi, Annamaria, Sakthimala Palaniappan und Mauro Maccarrone. „Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease“. Biomolecules 13, Nr. 10 (17.10.2023): 1534. http://dx.doi.org/10.3390/biom13101534.

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Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

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Khatib, Tala O., Angelica M. Amanso, Christina M. Knippler, Brian Pedro, Emily R. Summerbell, Najdat M. Zohbi, Jessica M. Konen, Janna K. Mouw und Adam I. Marcus. „A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling“. PLOS ONE 18, Nr. 10 (11.10.2023): e0292554. http://dx.doi.org/10.1371/journal.pone.0292554.

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Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype—such as invasiveness, cell:cell interactions, and changes in spatial positioning—with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations.

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Langston, Jordan C., Michael T. Rossi, Qingliang Yang, William Ohley, Edwin Perez, Laurie E. Kilpatrick, Balabhaskar Prabhakarpandian und Mohammad F. Kiani. „Omics of endothelial cell dysfunction in sepsis“. Vascular Biology 4, Nr. 1 (01.05.2022): R15—R34. http://dx.doi.org/10.1530/vb-22-0003.

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During sepsis, defined as life-threatening organ dysfunction due to dysregulated host response to infection, systemic inflammation activates endothelial cells and initiates a multifaceted cascade of pro-inflammatory signaling events, resulting in increased permeability and excessive recruitment of leukocytes. Vascular endothelial cells share many common properties but have organ-specific phenotypes with unique structure and function. Thus, therapies directed against endothelial cell phenotypes are needed to address organ-specific endothelial cell dysfunction. Omics allow for the study of expressed genes, proteins and/or metabolites in biological systems and provide insight on temporal and spatial evolution of signals during normal and diseased conditions. Proteomics quantifies protein expression, identifies protein–protein interactions and can reveal mechanistic changes in endothelial cells that would not be possible to study via reductionist methods alone. In this review, we provide an overview of how sepsis pathophysiology impacts omics with a focus on proteomic analysis of mouse endothelial cells during sepsis/inflammation and its relationship with the more clinically relevant omics of human endothelial cells. We discuss how omics has been used to define septic endotype signatures in different populations with a focus on proteomic analysis in organ-specific microvascular endothelial cells during sepsis or septic-like inflammation. We believe that studies defining septic endotypes based on proteomic expression in endothelial cell phenotypes are urgently needed to complement omic profiling of whole blood and better define sepsis subphenotypes. Lastly, we provide a discussion of how in silico modeling can be used to leverage the large volume of omics data to map response pathways in sepsis.

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Dong, Xianjun, Chunyu Liu und Mikhail Dozmorov. „Review of multi-omics data resources and integrative analysis for human brain disorders“. Briefings in Functional Genomics 20, Nr. 4 (08.05.2021): 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 further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.

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„Spatial Omics DataBase (SODB): increasing accessibility to spatial omics data“. Nature Methods, 16.02.2023. http://dx.doi.org/10.1038/s41592-023-01772-8.

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Long, Yahui, Kok Siong Ang, Raman Sethi, Sha Liao, Yang Heng, Lynn van Olst, Shuchen Ye et al. „Deciphering spatial domains from spatial multi-omics with SpatialGlue“. Nature Methods, 21.06.2024. http://dx.doi.org/10.1038/s41592-024-02316-4.

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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 and transcriptome–proteome modalities. Compared to other methods, SpatialGlue captured more anatomical details and more accurately resolved spatial domains such as the cortex layers of the brain. Our method also identified cell types like spleen macrophage subsets located at three different zones that were not available in the original data annotations. SpatialGlue scales well with data size and can be used to integrate three modalities. Our spatial multi-omics analysis tool combines the information from complementary omics modalities to obtain a holistic view of cellular and tissue properties.

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Kiessling, Paul, und Christoph Kuppe. „Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases“. Genome Medicine 16, Nr. 1 (18.01.2024). http://dx.doi.org/10.1186/s13073-024-01282-y.

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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 applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

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Vickovic, S., B. Lötstedt, J. Klughammer, S. Mages, Å. Segerstolpe, O. Rozenblatt-Rosen und A. Regev. „SM-Omics is an automated platform for high-throughput spatial multi-omics“. Nature Communications 13, Nr. 1 (10.02.2022). http://dx.doi.org/10.1038/s41467-022-28445-y.

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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 thereof, to scale and combine spatial transcriptomics and spatial antibody-based multiplex protein detection. SM-Omics allows processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. We demonstrate SM-Omics in the mouse brain, spleen and colorectal cancer model, showing its broad utility as a high-throughput platform for spatial multi-omics.

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Aihara, Gohta, Kalen Clifton, Mayling Chen, Zhuoyan Li, Lyla Atta, Brendan F. Miller, Rahul Satija, John W. Hickey und Jean Fan. „SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis“. Bioinformatics, 20.06.2024. http://dx.doi.org/10.1093/bioinformatics/btae412.

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Abstract Motivation Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. Results To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell-types that recapitulate expected organ structures. Availability SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster. Supplementary information Supplementary data are available at Bioinformatics online.

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Alexandrov, Theodore, Julio Saez‐Rodriguez und Sinem K. Saka. „Enablers and challenges of spatial omics, a melting pot of technologies“. Molecular Systems Biology, 16.10.2023. http://dx.doi.org/10.15252/msb.202110571.

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AbstractSpatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.

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Lv, Tongxuan, Yong Zhang, Junlin Liu, Qiang Kang und 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, Nr. 5 (25.07.2024). http://dx.doi.org/10.1093/bib/bbae450.

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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 necessary and significant. We propose a multi-omics integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct the neighborhood graphs based on single-cell expression profiles or spatial coordinates, enabling it to process single-cell data and utilize spatial information from spatially resolved data. It can also perform self-supervised learning for integration through the graph attention auto-encoders from two paths. SSGATE is applied to integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.

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„Spatial Omics for Everyone“. Genetic Engineering & Biotechnology News 42, Nr. 1 (01.01.2022): 7. http://dx.doi.org/10.1089/gen.42.01.01.

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Carstens, Julienne L., Santhoshi N. Krishnan, Arvind Rao, Anna G. Sorace, Erin H. Seeley, Sammy Ferri-Borgogno und Jared K. Burks. „Spatial multiplexing and omics“. Nature Reviews Methods Primers 4, Nr. 1 (01.08.2024). http://dx.doi.org/10.1038/s43586-024-00330-6.

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„Spatial multiplexing and omics“. Nature Reviews Methods Primers 4, Nr. 1 (01.08.2024). http://dx.doi.org/10.1038/s43586-024-00342-2.

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Bressan, Dario, Giorgia Battistoni und Gregory J. Hannon. „The dawn of spatial omics“. Science 381, Nr. 6657 (04.08.2023). http://dx.doi.org/10.1126/science.abq4964.

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Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful—but still hard to navigate—landscape.

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Dezem, Felipe Segato, Maycon Marção, Bassem Ben-Cheikh, Nadya Nikulina, Ayodele Omotoso, Destiny Burnett, Priscila Coelho et al. „A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics“. BMC Genomics 24, Nr. 1 (28.11.2023). http://dx.doi.org/10.1186/s12864-023-09722-6.

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AbstractCell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.

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Zhou, Weiwei, Minghai Su, Tiantongfei Jiang, Qingyi Yang, Qisen Sun, Kang Xu, Jingyi Shi et al. „SORC: an integrated spatial omics resource in cancer“. Nucleic Acids Research, 09.10.2023. http://dx.doi.org/10.1093/nar/gkad820.

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Abstract The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell–cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722 899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencing data in the majority of spatial transcriptomics datasets, involving 334 379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell–cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.

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Zheng, Yimin, Yitian Chen, Xianting Ding, Koon Ho Wong und Edwin Cheung. „Aquila: a spatial omics database and analysis platform“. Nucleic Acids Research, 16.10.2022. http://dx.doi.org/10.1093/nar/gkac874.

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Abstract Spatial omics is a rapidly evolving approach for exploring tissue microenvironment and cellular networks by integrating spatial knowledge with transcript or protein expression information. However, there is a lack of databases for users to access and analyze spatial omics data. To address this limitation, we developed Aquila, a comprehensive platform for managing and analyzing spatial omics data. Aquila contains 107 datasets from 30 diseases, including 6500+ regions of interest, and 15.7 million cells. The database covers studies from spatial transcriptome and proteome analyses, 2D and 3D experiments, and different technologies. Aquila provides visualization of spatial omics data in multiple formats such as spatial cell distribution, spatial expression and co-localization of markers. Aquila also lets users perform many basic and advanced spatial analyses on any dataset. In addition, users can submit their own spatial omics data for visualization and analysis in a safe and secure environment. Finally, Aquila can be installed as an individual app on a desktop and offers the RESTful API service for power users to access the database. Overall, Aquila provides a detailed insight into transcript and protein expression in tissues from a spatial perspective. Aquila is available at https://aquila.cheunglab.org.

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Yuan, Zhiyuan, Yisi Li, Minglei Shi, Fan Yang, Juntao Gao, Jianhua Yao und Michael Q. Zhang. „SOTIP is a versatile method for microenvironment modeling with spatial omics data“. Nature Communications 13, Nr. 1 (28.11.2022). http://dx.doi.org/10.1038/s41467-022-34867-5.

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AbstractThe rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module’s accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.

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