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Journal articles on the topic 'Spatial proteomcis'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Gatto, Laurent. "Data Management Plan for a Biotechnology and Biological Sciences Research Council (BBSRC) Tools and Resources Development Fund (TRDF) Grant." Research Ideas and Outcomes 3 (January 5, 2017): e11624. https://doi.org/10.3897/rio.3.e11624.

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This Data Management Plan (DMP) was created for Laurent Gatto's BBSRC Tools and Resources Development Fund award (BB/N023129/1). The DMP describes the management and sharing of all data and code associated with the grant, including software dissemination and release schedule, source code development and open source licensing, software documentation, reproducible framework and data annotation and dissemination.

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Hulahan, Taylor S., Laura Spruill, Elizabeth N. Wallace, et al. "Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics." International Journal of Molecular Sciences 25, no. 12 (2024): 6748. http://dx.doi.org/10.3390/ijms25126748.

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Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detai

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Ozorun, Gulsev, Alexander Eckersley, Eleanor Bradley, Rachel Watson, Michael Sherrat, and Joe Swift. "P28 Data-independent acquisition mass spectrometry improves spatially resolved analysis of the human skin proteome." British Journal of Dermatology 190, no. 6 (2024): e92-e92. http://dx.doi.org/10.1093/bjd/ljae105.050.

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Abstract Introduction and aims Proteomic analysis of the extracellular matrix (ECM) presents challenges because of the highly crosslinked and low-solubility nature of ECM proteins. Traditional homogenization and protein digestion approaches result in the loss of crucial information regarding protein localization and spatial relationships. To address this, spatially resolved proteomics emerges as a powerful tool for exploring heterogeneity within bulk tissues. This study aims to determine the minimum tissue volume required for comprehensive proteome coverage using data-independent acquisition m

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Dereli, Zeynep, Behnaz Bozorgui, Huamin Wang, John Weinstein, Michael Overman, and Anil Korkut. "Abstract 3646: A spatially resolved single cell proteomic atlas of small bowel adenocarcinoma." Cancer Research 84, no. 6_Supplement (2024): 3646. http://dx.doi.org/10.1158/1538-7445.am2024-3646.

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Abstract Small bowel adenocarcinoma (SBA) is a rare malignancy associated with poor prognosis. The cellular and proteomic heterogeneity within the tumor immune microenvironment (TIME) of SBA is a likely driver of prognosis, disease progression and response to therapy. There is, however, a major gap in our knowledge of tumor and immune interactions in SBA. Cyclic Immunofluorescence (CycIF), an antibody-based, highly multiplexed imaging technology for spatially resolved single cell level proteomic profiling, is well suited to map the proteomic heterogeneity and organization of TIME in SBA. Here,

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Smythers, Amanda L., and Leslie M. Hicks. "Mapping the plant proteome: tools for surveying coordinating pathways." Emerging Topics in Life Sciences 5, no. 2 (2021): 203–20. http://dx.doi.org/10.1042/etls20200270.

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Plants rapidly respond to environmental fluctuations through coordinated, multi-scalar regulation, enabling complex reactions despite their inherently sessile nature. In particular, protein post-translational signaling and protein–protein interactions combine to manipulate cellular responses and regulate plant homeostasis with precise temporal and spatial control. Understanding these proteomic networks are essential to addressing ongoing global crises, including those of food security, rising global temperatures, and the need for renewable materials and fuels. Technological advances in mass sp

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Rosenbloom, Alyssa, Shilah Bonnett, Mark Conner, et al. "Abstract 3649: A novel spatial multi-omic approach for biological discoveries in colonic diseased tissues using a comprehensive Immuno-Oncology Proteome Atlas and Whole Transcriptome Atlas." Cancer Research 84, no. 6_Supplement (2024): 3649. http://dx.doi.org/10.1158/1538-7445.am2024-3649.

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Abstract The advancement of spatially resolved, multiplex proteomic and transcriptomic technologies has revolutionized and redefined the approaches to complex biological questions pertaining to tissue heterogeneity, tumor microenvironments, cellular interactions, cellular diversity, and therapeutic response. While spatial transcriptomics has traditionally led the way in plex, multiple studies have demonstrated a poor correlation between RNA expression and protein abundance, owing to transcriptional and translational regulation, target turnover, and most critically, post-translational protein m

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Min, Jimin, Lisa Schweizer, Gijs Zonderland, et al. "Abstract 753: AI-powered deep visual proteomics (DVP) for early pancreatic cancer insights." Cancer Research 85, no. 8_Supplement_1 (2025): 753. https://doi.org/10.1158/1538-7445.am2025-753.

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Abstract Pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, presents a formidable clinical challenge. Patients experience 70-80% post-resection mortality due to recurrence. Identifying aberrant molecular pathways and drivers of pre-cancer progression is an unmet area of importance for developing effective biomarkers and enabling early detection. Pancreatic intraepithelial neoplasia (PanINs) represent the key precursor to PDAC. While genomic and transcriptomic profiling of these lesions has been performed at depth, the deep proteomic profiles of these precursors

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Jackson, Charles E., Matthew H. Ingalls, Kayla E. Cashion, et al. "Abstract 6483: Powerful end-to-end spatial analyses using precise spatial proteomics." Cancer Research 85, no. 8_Supplement_1 (2025): 6483. https://doi.org/10.1158/1538-7445.am2025-6483.

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Abstract Spatial biology supports our understanding of the progression and treatment of cancer by distilling complex cellular interactions into actionable spatial signatures. The most actionable targets to study with spatial methods are protein biomarkers, but non-destructive, high-plex spatial proteomics has been challenging to achieve due to technical limitations posted by available technologies. With the recent advent of enhanced photobleaching in cyclic immunofluorescence (EpicIF™) technology, multiplex immunofluorescence (mIF) can now be achieved more easily and inexpensively. EpicIF tech

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Heywood, Wendy E., Jon Searle, Richard Collis, et al. "A Proof of Principle 2D Spatial Proteome Mapping Analysis Reveals Distinct Regional Differences in the Cardiac Proteome." Life 14, no. 8 (2024): 970. http://dx.doi.org/10.3390/life14080970.

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Proteomics studies often explore phenotypic differences between whole organs and systems. Within the heart, more subtle variation exists. To date, differences in the underlying proteome are only described between whole cardiac chambers. This study, using the bovine heart as a model, investigates inter-regional differences and assesses the feasibility of measuring detailed, cross-tissue variance in the cardiac proteome. Using a bovine heart, we created a two-dimensional section through a plane going through two chambers. This plane was further sectioned into 4 × 4 mm cubes and analysed using la

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Bouamrani, Ali, Jessica Ternier, David Ratel, et al. "Direct-Tissue SELDI-TOF Mass Spectrometry Analysis: A New Application for Clinical Proteomics." Clinical Chemistry 52, no. 11 (2006): 2103–6. http://dx.doi.org/10.1373/clinchem.2006.070979.

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Abstract Background: New molecular profiling technologies can aid in analysis of small pathologic samples obtained by minimally invasive biopsy and may enable the discovery of key biomarkers synergistic with anatomopathologic analysis related to prognosis, therapeutic response, and innovative target validation. Thus proteomic analysis at the histologic level in healthy and pathologic settings is a major issue in the field of clinical proteomics. Methods: We used surface-enhanced laser desorption ionization-time-of-flight mass spectrometry (SELDI-TOF MS) technology with surface chromatographic

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Bozorgui, Behnaz, Zeynep Dereli, Guillaume Thibault, John N. Weinstein, and Anil Korkut. "Abstract 3765: Single cell spatial proteomics analysis and computational evaluation pipeline." Cancer Research 84, no. 6_Supplement (2024): 3765. http://dx.doi.org/10.1158/1538-7445.am2024-3765.

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Abstract Resolving tissue and proteomic heterogeneity is critical to decoding the structure and function of tumor-immune microenvironment (TIME). Such understanding requires profiling of tumor and immune cell proteomic features with spatial resolution at the single-cell level. Although such spatially resolved methods and data sets are becoming increasingly available, analytical and computational methods that can extract the highly complex features and interactions within TIME are lacking. To address that problem, we have developed a computational pipeline we call the Spatial Proteomics Analysi

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Wen, Hanson. "Identifying Biomarkers for Rheumatoid Arthritis and Spondyloarthritis by Machine Learning." Archives of Proteomics and Bioinformatics 4, no. 1 (2024): 6–23. https://doi.org/10.33696/proteomics.4.015.

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Background: Rheumatoid arthritis (RA) and Spondyloarthritis (SpA) are chronic inflammatory diseases characterized by joint inflammation and systemic involvement. Current diagnostic methods lack sufficient specificity and sensitivity, often leading to delayed or inaccurate diagnoses. Objective: This study aims to utilize spatial transcriptomics and machine learning to identify differentially expressed genes (DEGs) and potential biomarkers associated with RA and SpA, enhancing our understanding of their molecular mechanisms. Methods: High-dimensional spatial transcriptomics data and high-resolut

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Heck, Ashley, Hiromi Sato, Christine Kang, et al. "Abstract 1880: Advancing spatial discovery multiomics: Integration of a novel 1,000+ plex discovery proteome atlas with an 18,000+ plex whole transcriptome atlas for same-slide investigation of multiple cancer pathologies." Cancer Research 85, no. 8_Supplement_1 (2025): 1880. https://doi.org/10.1158/1538-7445.am2025-1880.

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Abstract Spatial multiomics at high plex represents a transformative approach to understanding complex biological systems. Whereas high plex spatial transcriptomics have transformed tissue analyses, spatial proteomics have been limited by low plex and lacking coverage of major biological pathways. Proteins, which represent the functional units of cellular response and activity, are essential for studying the heterogeneity of cancer and immune pathology. Furthermore, cellular responses to intrinsic and extrinsic stimuli are often driven by post-translational modifications of proteins. Enabling

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Bruns, Volker, Sonja Fritzsche, and Fabian Coscia. "Spatial Proteomics: Conquering Highplex Analysis of Spatial Proteomics Images." Trillium Pathology 4, no. 1 (2025): 26–30. https://doi.org/10.47184/tp.2025.01.04.

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Ren, Keyi, Yu Wang, Minmin Zhang, Ting Tao, and Zeyu Sun. "Unveiling Tumorigenesis Mechanisms and Drug Therapy in Neuroblastoma by Mass Spectrometry Based Proteomics." Children 11, no. 11 (2024): 1323. http://dx.doi.org/10.3390/children11111323.

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Neuroblastoma (NB) is the most common type of extracranial solid tumors in children. Despite the advancements in treatment strategies over the past years, the overall survival rate in patients within the high-risk NB group remains less than 50%. Therefore, new treatment options are urgently needed for this group of patients. Compared with genomic aberrations, proteomic alterations are more dynamic and complex, as well as more directly related to pathological phenotypes and external perturbations such as environmental changes and drug treatments. This review focuses on specific examples of prot

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Hajjaji, Nawale, Mira Abbouchi, Lan Anh Nguyen, et al. "A novel proteomic mass spectrometry-based approach to reveal functionally heterogeneous tumor clones in breast cancer metastases and identify clone-specific drug targets." Journal of Clinical Oncology 38, no. 15_suppl (2020): e13063-e13063. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e13063.

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e13063 Background: Breast cancer mortality is expected to rise by almost 30% by 2030 worldwide, mainly due to the occurrence of distant metastases. The development of drugs specifically targeted at tumor drivers has not yet curbed resistance to treatment, which prevents metastases curability. There is a need for new molecular approaches to tackle metastases complex biology, particularly tumor heterogeneity, a main determinant of resistance. The aim of this study was to use a proteomic mass spectrometry-based approach to reveal functionally heterogeneous’ tumor subpopulations in breast cancer m

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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 (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 transcriptomi

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Strack, Rita. "Subcellular spatial proteomics." Nature Methods 21, no. 12 (2024): 2227. https://doi.org/10.1038/s41592-024-02546-6.

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Wu, Yi-Chien, Elie Abi Khalil, Aditi Upadhye, et al. "Abstract 2081: Tissue-niche-based and cell-type-selective proteomics." Cancer Research 85, no. 8_Supplement_1 (2025): 2081. https://doi.org/10.1158/1538-7445.am2025-2081.

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Abstract Background: The spatial organization of cell populations within tissues is crucial for maintaining physiological function and understanding the mechanisms underlying various diseases. Proteins are the functional components that drive essential processes such as tissue homeostasis, disease progression, and therapeutic response. Spatial proteomics integrates molecular profiling with cellular location data, offering a powerful approach to decode these mechanisms. Here, we present an innovative spatial proteomics (SP) tool that combines widely adopted methodologies in biological research,

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Wu, Yi-Chien, Elie Abi Khalil, Samuel Weng, and Steve Lee. "Abstract 3772: Tissue-niche-based and cell-type-selective in-depth proteomics." Cancer Research 84, no. 6_Supplement (2024): 3772. http://dx.doi.org/10.1158/1538-7445.am2024-3772.

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Abstract Introduction: The development, physiology function, and pathogenesis of the tissues are intricately orchestrated by the spatial organization of diverse cell populations. Proteins are the fundamental components within individual cells that control every function. Therefore, by integrating both information on cellular location and molecular profiling, spatial proteomics has emerged as an indispensable field for understanding mechanisms governing tissue homeostasis, disease progression, and therapeutic responses. Here, I introduce a spatial proteomics method designed to target selective

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Liu, You-Pi, Weng Man Chong, Harry Huang, et al. "Abstract 3875: De novo spatial proteomic profiling of immune synapses using machine learning-guided microscoop." Cancer Research 82, no. 12_Supplement (2022): 3875. http://dx.doi.org/10.1158/1538-7445.am2022-3875.

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Abstract The spatial proteome of the immune synapse (IS) between a target cell and a lymphocyte is fundamentally important to understand the mechanism of cell-mediated immunity for both immuno-oncology and therapeutic applications. In this research, we used Microscoop, a fully-automatic microscope system integrated with a machine learning-based algorithm, to best determine ISs for proteomic mapping. We used Raji B cells as antigen-presenting cells (APCs) and induced the formation of ISs by incubating with Jurkat T cells. Multiple IS images were applied to train our algorithm using convolution

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Jain, Michael D., Hisao Nagaya, Annalyn Gilchrist, Miroslaw Cygler, and John J. M. Bergeron. "Spatial localization of unknown proteins in the endoplasmic reticulum predicts functions." Clinical & Investigative Medicine 30, no. 4 (2007): 84. http://dx.doi.org/10.25011/cim.v30i4.2858.

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Protein synthesis, folding and degradation functions are spatially segregated in the endoplasmic reticulum (ER) with respect to the membrane and the ribosome (rough and smooth ER). Interrogation of a proteomics resource characterizing rough and smooth ER membranes subfractionated into cytosolic, membrane, and soluble fractions gives a spatial map of known proteins involved in ER function. The spatial localization of 224 identified unknown proteins in the ER is predicted to give insight into their function. Here we provide evidence that the proteomics resource accurately predicts the function o

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Szeitz, Beáta, Tibor Glasz, Zoltán Herold, et al. "Spatially Resolved Proteomic and Transcriptomic Profiling of Anaplastic Lymphoma Kinase-Rearranged Pulmonary Adenocarcinomas Reveals Key Players in Inter- and Intratumoral Heterogeneity." International Journal of Molecular Sciences 24, no. 14 (2023): 11369. http://dx.doi.org/10.3390/ijms241411369.

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Pulmonary adenocarcinomas (pADCs) with an ALK rearrangement are a rare cancer subtype, necessitating comprehensive molecular investigations to unravel their heterogeneity and improve therapeutic strategies. In this pilot study, we employed spatial transcriptomic (NanoString GeoMx) and proteomic profiling to investigate seven treatment-naïve pADCs with an ALK rearrangement. On each FFPE tumor slide, 12 smaller and 2–6 larger histopathologically annotated regions were selected for transcriptomic and proteomic analysis, respectively. The correlation between proteomics and transcriptomics was mode

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adimi, yasmine, clement Levin, Emilia Puig Lombardi, Marion Classe, and Cecile Badoual. "Abstract 7483: A new spatial multi-omics approach to deeply characterize human cancer tissue using a single slide: a new spatial muti-omics approach to deeply characterize human cancer tissue using a single slide." Cancer Research 85, no. 8_Supplement_1 (2025): 7483. https://doi.org/10.1158/1538-7445.am2025-7483.

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Abstract Introduction: In digital pathology, extracting comprehensive data from a single tissue sample is critical for enhancing diagnostic precision and therapeutic strategies. Techniques like multiplex imaging and spatial transcriptomics offer unprecedented depth of information. This study introduces a novel and innovative approach combining for the first time Phenocycler® (with a 34-marker panel) and Visium® spatial transcriptomics on the same tissue section of squamous cell carcinoma of the upper aerodigestive tract. A single tissue section was analyzed using Phenocycler® (PP) for proteomi

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Gooding, Sarah, Chen-Yi Wang, Warren Baker, et al. "Development of a Spatial Multiomics Platform to Integrate Genomic, Transcriptomic and Proteomic Features for Translational Research in Multiple Myeloma." Blood 144, Supplement 1 (2024): 6848. https://doi.org/10.1182/blood-2024-205264.

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Multiple Myeloma (MM) is a genomically heterogeneous cancer of malignant plasma cells (PC) residing in the bone marrow. Despite recent therapeutic advances, it is incompletely understood why some patient populations remain largely therapy-refractory or relapse, even with targeted immunotherapies. Whilst MM classification based on genomic features and gene expression profiling (GEP) is well-described, the role of spatial interactions between tumour cells and the bone marrow tumour microenvironment (TME) in dictating treatment responses is insufficiently defined. Spatial heterogeneity in MM was

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Metousis, Andreas, Hilary Kenny, Lisa Schweizer, et al. "Abstract 760: Cell type resolved spatial proteomics defines the transition of precancerous fallopian tube lesions to invasive ovarian cancer." Cancer Research 85, no. 8_Supplement_1 (2025): 760. https://doi.org/10.1158/1538-7445.am2025-760.

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Serous epithelial ovarian cancer is the most common subtype of ovarian cancer, and it is thought to arise from dysplastic fallopian tube (FT) lesions. Depending on their histopathological characteristics and proliferative potential, precancerous lesions of the FT are categorized as p53 signatures, Serous Tubal Intraepithelial Lesions (STILs) or Serous Tubal Intraepithelial Carcinomas (STICs). In this study, we analyzed protein changes from precancerous lesions through invasive tumor development and metastasis to identify early molecular switches and druggable proteins for ovarian cancer treatm

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Phan-Everson, Tien, Zachary Lewis, Giang Ong, et al. "Abstract 4617: A complete pipeline for high-plex spatial proteomic profiling and analysis on the cosmxtm spatial molecular imager and atomtm spatial informatics platform." Cancer Research 83, no. 7_Supplement (2023): 4617. http://dx.doi.org/10.1158/1538-7445.am2023-4617.

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Abstract Detecting and analyzing large numbers of proteins using whole-slide imaging is critical for a comprehensive picture of immune response to cancer. Many existing approaches for high-plex proteomics face issues around simplicity, speed, scalability, and big data analysis. Here, we present an integrated workflow from sample preparation through downstream analysis that addresses many key concerns around high plex proteomics. The CosMx Spatial Molecular Imager (SMI) and AtoMx Spatial Informatics Platform (SIP) comprise of a turnkey, end-to-end workflow that efficiently handles highly multip

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Marx, Vivien. "Mapping proteins with spatial proteomics." Nature Methods 12, no. 9 (2015): 815–19. http://dx.doi.org/10.1038/nmeth.3555.

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Zhang, Jun, Ting Hu, Yi Wang, et al. "Investigating the Neurotoxic Impacts of Arsenic and the Neuroprotective Effects of Dictyophora Polysaccharide Using SWATH-MS-Based Proteomics." Molecules 27, no. 5 (2022): 1495. http://dx.doi.org/10.3390/molecules27051495.

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Arsenic (As) is one of the most important toxic elements in the natural environment. Currently, although the assessment of the potential health risks of chronic arsenic poisoning has received great attention, the research on the effects of arsenic on the brain is still limited. It has been reported that dictyophora polysaccharide (DIP), a common bioactive natural compound found in dietary plants, could reduce arsenic toxicity. Following behavioral research, comparative proteomics was performed to explore the molecular mechanism of arsenic toxicity to the hippocampi of SD (Sprague Dawley) rats

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Wang, Xue, Fei Wang, Archana S. Iyer, et al. "Integrative Spatial Proteomics and Single-Cell RNA Sequencing Unveil Molecular Complexity in Rheumatoid Arthritis for Novel Therapeutic Targeting." Proteomes 13, no. 2 (2025): 17. https://doi.org/10.3390/proteomes13020017.

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Understanding the heterogeneity of Rheumatoid Arthritis (RA) and identifying therapeutic targets remain challenging using traditional bulk transcriptomics alone, as it lacks the spatial and protein-level resolution needed to fully capture disease and tissue complexities. In this study, we applied Laser Capture Microdissection (LCM) coupled with mass spectrometry-based proteomics to analyze histopathological niches of the RA synovium, enabling the identification of protein expression profiles of the diseased synovial lining and sublining microenvironments compared to their healthy counterparts.

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Mao, Yiheng, Xi Wang, Peiwu Huang, and Ruijun Tian. "Spatial proteomics for understanding the tissue microenvironment." Analyst 146, no. 12 (2021): 3777–98. http://dx.doi.org/10.1039/d1an00472g.

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We outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted.

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Zheng, Xiang, Andreas Mund, and Matthias Mann. "Protocol for spatial proteomic profiling of tonsil cancer microenvironments using multiplexed imaging-powered deep visual proteomics." STAR Protocols 6, no. 3 (2025): 103901. https://doi.org/10.1016/j.xpro.2025.103901.

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Pulukkody, Aruni Chathurya, Yeni P. Yung, Fabrizio Donnarumma, Kermit K. Murray, Ross P. Carlson, and Luke Hanley. "Spatially resolved analysis of Pseudomonas aeruginosa biofilm proteomes measured by laser ablation sample transfer." PLOS ONE 16, no. 7 (2021): e0250911. http://dx.doi.org/10.1371/journal.pone.0250911.

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Heterogeneity in the distribution of nutrients and oxygen gradients during biofilm growth gives rise to changes in phenotype. There has been long term interest in identifying spatial differences during biofilm development including clues that identify chemical heterogeneity. Laser ablation sample transfer (LAST) allows site-specific sampling combined with label free proteomics to distinguish radially and axially resolved proteomes for Pseudomonas aeruginosa biofilms. Specifically, differential protein abundances on oxic vs. anoxic regions of a biofilm were observed by combining LAST with botto

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Cumberbatch, Marie, Geoffrey Ivison, Amy Lam, Aaron Mayer, and Milan Bhagat. "Abstract 4623: Single-cell spatial proteomic analysis of the tumor microenvironment in treatment-naive NSCLC samples with immunotherapy treatment and response data." Cancer Research 83, no. 7_Supplement (2023): 4623. http://dx.doi.org/10.1158/1538-7445.am2023-4623.

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Abstract The tumor microenvironment (TME) represents a complex network comprising a variety of cell types including immune, stromal, and extracellular elements in addition to malignant tumor cells. Growing evidence suggests that the spatial relationships of these components influence tumor progression and response/resistance to immunotherapeutic agents. However, the precise mechanisms driving this relationship to clinical outcomes remains poorly understood. This is in part because generating comprehensive spatial datasets has only recently been made possible due to innovations in instrumentati

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Burgess, Darren J. "Spatial characterization of proteomes." Nature Reviews Genetics 16, no. 3 (2015): 129. http://dx.doi.org/10.1038/nrg3910.

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Crook, Oliver M., Lisa M. Breckels, Kathryn S. Lilley, Paul D. W. Kirk, and Laurent Gatto. "A Bioconductor workflow for the Bayesian analysis of spatial proteomics." F1000Research 8 (April 11, 2019): 446. http://dx.doi.org/10.12688/f1000research.18636.1.

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Knowledge of the subcellular location of a protein gives valuable insight into its function. The field of spatial proteomics has become increasingly popular due to improved multiplexing capabilities in high-throughput mass spectrometry, which have made it possible to systematically localise thousands of proteins per experiment. In parallel with these experimental advances, improved methods for analysing spatial proteomics data have also been developed. In this workflow, we demonstrate using `pRoloc` for the Bayesian analysis of spatial proteomics data. We detail the software infrastructure and

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Borner, Georg H. H. "Organellar Maps Through Proteomic Profiling – A Conceptual Guide." Molecular & Cellular Proteomics 19, no. 7 (2020): 1076–87. http://dx.doi.org/10.1074/mcp.r120.001971.

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Protein subcellular localization is an essential and highly regulated determinant of protein function. Major advances in mass spectrometry and imaging have allowed the development of powerful spatial proteomics approaches for determining protein localization at the whole cell scale. Here, a brief overview of current methods is presented, followed by a detailed discussion of organellar mapping through proteomic profiling. This relatively simple yet flexible approach is rapidly gaining popularity, because of its ability to capture the localizations of thousands of proteins in a single experiment

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Rimm, David. "Abstract ED7-2: Multiplex spatial proteomic profiling." Cancer Research 83, no. 5_Supplement (2023): ED7–2—ED7–2. http://dx.doi.org/10.1158/1538-7445.sabcs22-ed7-2.

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Abstract Multiplex spatial analysis is touted as a potential solution for tissue biomarkers for prognostic and companion diagnostic applications. Spatial analysis infers maintaining the location of a biomolecule so that both its amount and context can contribute to the information obtained. Immunohistochemistry (IHC) is the archetypal method of spatial profiling and the only application that has made it to the clinic. In this session, we will discuss technologies based on IHC, but that allow assessment of at least two, and as many as 100 or more proteins within the cell or molecular compartmen

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Wu, Cheng-Han, and Yu-Chiao Chiu. "Spatial Profiles in Triple-negative Breast Cancer: Unraveling the Tumor Microenvironment and Biomarkers for Immune Checkpoint Inhibitors." Journal of Cancer Research and Practice 11, no. 2 (2024): 62–66. http://dx.doi.org/10.4103/ejcrp.ejcrp-d-23-00030.

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Abstract Objective: Immune checkpoint inhibitors (ICIs) have become an important treatment option for cancer. However, the predictive power of current biomarkers is limited for treatment response, especially in triple-negative breast cancer (TNBC). Investigation of the tumor microenvironment (TME) may provide biological insights into the response to ICIs by uncovering the interactions among tumor and immune cells. Emerging technologies of spatial transcriptomics (ST) and proteomics allow clinical researchers to better understand the TME. Data Sources and Study Selection: We reviewed the result

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Wang, Nan, Rongshui Wang, Xue Zhang, Xia Li, Yan Liang, and Zhiyong Ding. "Spatially-resolved proteomics and transcriptomics: An emerging digital spatial profiling approach for tumor microenvironment." Visualized Cancer Medicine 2 (2021): 1. http://dx.doi.org/10.1051/vcm/2020002.

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Digital spatial profiling (DSP) is an emerging powerful technology for proteomics and transcriptomics analyses in a spatially resolved manner for formalin-fixed paraffin-embedded (FFPE) samples developed by nanoString Technologies. DSP applies several advanced technologies, including high-throughput readout technologies (digital optical barcodes by nCounter instruments or next generation sequencing (NGS)), programmable digital micromirror device (DMD) technology, and microfluidic sampling technologies into traditional immunohistochemistry (IHC) and RNA in situ hybridization (ISH) approaches, c

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Breckels, Lisa M., Claire M. Mulvey, Kathryn S. Lilley, and Laurent Gatto. "A Bioconductor workflow for processing and analysing spatial proteomics data." F1000Research 5 (December 28, 2016): 2926. http://dx.doi.org/10.12688/f1000research.10411.1.

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Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive

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Breckels, Lisa M., Claire M. Mulvey, Kathryn S. Lilley, and Laurent Gatto. "A Bioconductor workflow for processing and analysing spatial proteomics data." F1000Research 5 (July 3, 2018): 2926. http://dx.doi.org/10.12688/f1000research.10411.2.

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Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive

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

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

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Nejo, Takahide, Darwin Kwok, Kevin Leung, et al. "EPCO-14. MULTIFACETED TRANSCRIPTOMIC AND PROTEOMIC ANALYSES IDENTIFIED PUTATIVE ALTERNATIVE SPLICING-DERIVED CELL SURFACE ANTIGENS IN GLIOMA." Neuro-Oncology 23, Supplement_6 (2021): vi4. http://dx.doi.org/10.1093/neuonc/noab196.013.

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Abstract BACKGROUND To develop effective immunotherapy for gliomas, it is crucial to expand the repertoire of targetable antigens. Recent studies have suggested that alternative splicing (AS), or its deriving tumor-specific junctions (“neojunctions”), could generate cryptic amino acid sequences that can be a source of neoantigens. In this study, we investigated neojunctions based on multifaceted transcriptomic and proteomic analyses, seeking the potential cell surface antigens that may be targeted by CAR. METHODS For screening, we analyzed bulk RNA-sequencing data of TCGA-GBM/LGG with high tum

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Brožová, Klára, Brigitte Hantusch, Lukas Kenner, and Klaus Kratochwill. "Spatial Proteomics for the Molecular Characterization of Breast Cancer." Proteomes 11, no. 2 (2023): 17. http://dx.doi.org/10.3390/proteomes11020017.

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Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease’s heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to ident

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Rosenberger, Florian A., Marvin Thielert, Maximilian T. Strauss, et al. "Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome." Nature Methods 20, no. 10 (2023): 1530–36. http://dx.doi.org/10.1038/s41592-023-02007-6.

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AbstractSingle-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Ha

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Xu, Yuanwei, T. Mamie Lih, Angelo M. De Marzo, Qing Kay Li, and Hui Zhang. "SPOT: spatial proteomics through on-site tissue-protein-labeling." Clinical Proteomics 21, no. 1 (2024). http://dx.doi.org/10.1186/s12014-024-09505-5.

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Abstract Background Spatial proteomics seeks to understand the spatial organization of proteins in tissues or at different subcellular localization in their native environment. However, capturing the spatial organization of proteins is challenging. Here, we present an innovative approach termed Spatial Proteomics through On-site Tissue-protein-labeling (SPOT), which combines the direct labeling of tissue proteins in situ on a slide and quantitative mass spectrometry for the profiling of spatially-resolved proteomics. Materials and Methods Efficacy of direct TMT labeling was investigated using

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Jiang, Ying, Jian Wang, Aihua Sun, et al. "The coming era of proteomics-driven precision medicine." National Science Review, July 14, 2025. https://doi.org/10.1093/nsr/nwaf278.

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Abstract The concept of ‘Proteomics-Driven Precision Medicine’ highlighted in 2019, emphasized the potential of proteomics to transform precision medicine by offering deeper insights into dynamic biological processes. Since then, this field has seen remarkable advancements, interlinking with key pillars such as protein expression profiling, post-translational modifications, protein-protein interactions, and spatial proteomics to transform healthcare. Technological progress has led to the creation of comprehensive reference maps of proteomes, identification of over 90% of human protein-coding g

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Fan, Linyuan, Yi Liu, Haichao Zhou, et al. "Spatially resolved proteomics surveys the chemo‐refractory proteins related to high‐grade serous ovarian cancer." Clinical and Translational Medicine 15, no. 7 (2025). https://doi.org/10.1002/ctm2.70422.

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AbstractHigh‐grade serous ovarian carcinoma (HGSC) is a lethal malignancy characterized by high incidence, mortality, and chemoresistance. However, its molecular drivers are unknown. In this study, spatially resolved proteomics was applied to 1144 formalin‐fixed paraffin‐embedded tissue spots obtained by laser capture microdissection from 10 patients with HGSC and divergent carboplatin‐paclitaxel (CP) responses. Specific sampling revealed stroma‐driven tumour heterogeneity, identifying 642 tumour‐specific and 180 stroma‐specific proteins, with 505 CP‐responsive therapeutic targets. Most of the

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Mund, Andreas, Fabian Coscia, András Kriston, et al. "Deep Visual Proteomics defines single-cell identity and heterogeneity." Nature Biotechnology, May 19, 2022. http://dx.doi.org/10.1038/s41587-022-01302-5.

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AbstractDespite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei fro

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