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  1. Article: Spatial Morphoproteomic Features Predict Uniqueness of Immune Microarchitectures and Responses in Lymphoid Follicles.

    Hu, Thomas / Allam, Mayar / Kaushik, Vikram / Goudy, Steven L / Xu, Qin / Mudd, Pamela / Manthiram, Kalpana / Coskun, Ahmet F

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Multiplex imaging technologies allow the characterization of single cells in their cellular environments. Understanding the organization of single cells within their microenvironment and quantifying disease-status related biomarkers is essential for ... ...

    Abstract Multiplex imaging technologies allow the characterization of single cells in their cellular environments. Understanding the organization of single cells within their microenvironment and quantifying disease-status related biomarkers is essential for multiplex datasets. Here we proposed SNOWFLAKE, a graph neural network framework pipeline for the prediction of disease-status from combined multiplex cell expression and morphology in human B-cell follicles. We applied SNOWFLAKE to a multiplex dataset related to COVID-19 infection in humans and showed better predictive power of the SNOWFLAKE pipeline compared to other machine learning and deep learning methods. Moreover, we combined morphological features inside graph edge features to utilize attribution methods for extracting disease-relevant motifs from single-cell spatial graphs. The underlying subgraphs were further analyzed and associated with disease status across the dataset. We showed that SNOWFLAKE successfully extracted significant low dimensional embedding from subgraphs with a clear separation between disease status and helped characterize unique cellular interactions in the subgraphs. SNOWFLAKE is a generalizable pipeline for the analysis of multiplex imaging data modality by extracting disease-relevant subgraphs guided by graph-level prediction.
    Language English
    Publishing date 2024-01-07
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.01.05.574186
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Multiplex Spatial Bioimaging for Combination Therapy Design.

    Cai, Shuangyi / Allam, Mayar / Coskun, Ahmet F

    Trends in cancer

    2020  Volume 6, Issue 10, Page(s) 813–818

    Abstract: Multiplex spatial analyses dissect the heterogeneous cellular abundances and interactions in tumors. Single-cell bioimaging profiles many disease-associated protein biomarkers in patient biopsies to inform the design of cancer therapies. Guided by the ... ...

    Abstract Multiplex spatial analyses dissect the heterogeneous cellular abundances and interactions in tumors. Single-cell bioimaging profiles many disease-associated protein biomarkers in patient biopsies to inform the design of cancer therapies. Guided by the mechanistic insights from spatial cellular maps, combination therapy can efficiently eliminate cancers with reduced off-targets, resistance, and relapse.
    MeSH term(s) Biomarkers, Tumor/analysis ; Combined Modality Therapy ; Diagnostic Imaging/methods ; Humans ; Neoplasms/diagnostic imaging ; Neoplasms/metabolism ; Neoplasms/therapy ; Single-Cell Analysis
    Chemical Substances Biomarkers, Tumor
    Language English
    Publishing date 2020-05-25
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
    ZDB-ID 2852626-0
    ISSN 2405-8025 ; 2405-8033 ; 2405-8033
    ISSN (online) 2405-8025 ; 2405-8033
    ISSN 2405-8033
    DOI 10.1016/j.trecan.2020.05.003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics.

    Allam, Mayar / Cai, Shuangyi / Coskun, Ahmet F

    NPJ precision oncology

    2020  Volume 4, Page(s) 11

    Abstract: Cancers exhibit functional and structural diversity in distinct patients. In this mass, normal and malignant cells create tumor microenvironment that is heterogeneous among patients. A residue from primary tumors leaks into the bloodstream as cell ... ...

    Abstract Cancers exhibit functional and structural diversity in distinct patients. In this mass, normal and malignant cells create tumor microenvironment that is heterogeneous among patients. A residue from primary tumors leaks into the bloodstream as cell clusters and single cells, providing clues about disease progression and therapeutic response. The complexity of these hierarchical microenvironments needs to be elucidated. Although tumors comprise ample cell types, the standard clinical technique is still the histology that is limited to a single marker. Multiplexed imaging technologies open new directions in pathology. Spatially resolved proteomic, genomic, and metabolic profiles of human cancers are now possible at the single-cell level. This perspective discusses spatial bioimaging methods to decipher the cascade of microenvironments in solid and liquid biopsies. A unique synthesis of top-down and bottom-up analysis methods is presented. Spatial multi-omics profiles can be tailored to precision oncology through artificial intelligence. Data-driven patient profiling enables personalized medicine and beyond.
    Language English
    Publishing date 2020-05-01
    Publishing country England
    Document type Journal Article ; Review
    ISSN 2397-768X
    ISSN 2397-768X
    DOI 10.1038/s41698-020-0114-1
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  4. Article ; Online: Single-Cell and Spatial Analysis of Emergent Organoid Platforms.

    Kumar, Aditi / Cai, Shuangyi / Allam, Mayar / Henderson, Samuel / Ozbeyler, Melissa / Saiontz, Lilly / Coskun, Ahmet F

    Methods in molecular biology (Clifton, N.J.)

    2023  Volume 2660, Page(s) 311–344

    Abstract: Organoids have emerged as a promising advancement of the two-dimensional (2D) culture systems to improve studies in organogenesis, drug discovery, precision medicine, and regenerative medicine applications. Organoids can self-organize as three- ... ...

    Abstract Organoids have emerged as a promising advancement of the two-dimensional (2D) culture systems to improve studies in organogenesis, drug discovery, precision medicine, and regenerative medicine applications. Organoids can self-organize as three-dimensional (3D) tissues derived from stem cells and patient tissues to resemble organs. This chapter presents growth strategies, molecular screening methods, and emerging issues of the organoid platforms. Single-cell and spatial analysis resolve organoid heterogeneity to obtain information about the structural and molecular cellular states. Culture media diversity and varying lab-to-lab practices have resulted in organoid-to-organoid variability in morphology and cell compositions. An essential resource is an organoid atlas that can catalog protocols and standardize data analysis for different organoid types. Molecular profiling of individual cells in organoids and data organization of the organoid landscape will impact biomedical applications from basic science to translational use.
    MeSH term(s) Humans ; Organoids ; Regenerative Medicine ; Stem Cells ; Organogenesis ; Spatial Analysis
    Language English
    Publishing date 2023-05-14
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-3163-8_22
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease

    Mayar Allam / Thomas Hu / Shuangyi Cai / Krishnan Laxminarayanan / Robert B. Hughley / Ahmet F. Coskun

    Communications Biology, Vol 4, Iss 1, Pp 1-

    2021  Volume 16

    Abstract: Allam, Hu, et al. present SpatialViz, a suite of algorithms to explore spatial relationships ...

    Abstract Allam, Hu, et al. present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. The authors employ SpatialViz on 20-plex protein data in tissue sections from normal and chronic tonsillitis cases and observe GrB and CD86 coexpression and CD3 + CD4+ enrichment in diseased tonsils compared to healthy tonsils, and demonstrate the utility of SpatialViz as a wide-application spatial visualization method.
    Keywords Biology (General) ; QH301-705.5
    Language English
    Publishing date 2021-05-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease.

    Allam, Mayar / Hu, Thomas / Cai, Shuangyi / Laxminarayanan, Krishnan / Hughley, Robert B / Coskun, Ahmet F

    Communications biology

    2021  Volume 4, Issue 1, Page(s) 632

    Abstract: Deep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present ... ...

    Abstract Deep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. Single-cell and spatial maps confirmed that CD68+ cells were correlated with the enhanced Granzyme B expression and CD3+ cells exhibited enrichment of CD4+ phenotype in chronic tonsillitis. SpatialViz revealed morphological distributions of cellular organizations in distinct anatomical areas, spatially resolved single-cell associations across anatomical categories, and distance maps between the markers. Spatial topographic maps showed the unique organization of different tissue layers. The spatial reference framework generated network-based comparisons of multiplex data from healthy and diseased tonsils. SpatialViz is broadly applicable to multiplexed tissue biology.
    MeSH term(s) Algorithms ; Humans ; Image Processing, Computer-Assisted/methods ; Proteomics/methods ; Single-Cell Analysis/methods ; Spatio-Temporal Analysis ; Tonsillitis/metabolism ; Tonsillitis/physiopathology
    Language English
    Publishing date 2021-05-27
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-021-02166-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Multiplexed protein profiling reveals spatial subcellular signaling networks.

    Cai, Shuangyi / Hu, Thomas / Venkatesan, Mythreye / Allam, Mayar / Schneider, Frank / Ramalingam, Suresh S / Sun, Shi-Yong / Coskun, Ahmet F

    iScience

    2022  Volume 25, Issue 9, Page(s) 104980

    Abstract: Protein-protein interaction networks are altered in multi-gene dysregulations in many disorders. Image-based protein multiplexing sheds light on signaling pathways to dissect cell-to-cell heterogeneity, previously masked by the bulk assays. Herein, we ... ...

    Abstract Protein-protein interaction networks are altered in multi-gene dysregulations in many disorders. Image-based protein multiplexing sheds light on signaling pathways to dissect cell-to-cell heterogeneity, previously masked by the bulk assays. Herein, we present a rapid multiplexed immunofluorescence (RapMIF) method measuring up to 25-plex spatial protein maps from cultures and tissues at subcellular resolution, providing combinatorial 272 pairwise and 1,360 tri-protein signaling states across 33 multiplexed pixel-level clusters. The RapMIF pipeline automated staining, bleaching, and imaging of the biospecimens in a single platform. RapMIF showed that WNT/β-catenin signaling upregulated upon the inhibition of the AKT/mTOR pathway. Subcellular protein images demonstrated translocation patterns, spatial receptor discontinuity, and subcellular signaling clusters in single cells. Signaling networks exhibited spatial redistribution of signaling proteins in drug-responsive cultures. Machine learning analysis predicted the phosphorylated β-catenin expression from interconnected signaling protein images. RapMIF is an ideal signaling discovery approach for precision therapy design.
    Language English
    Publishing date 2022-08-18
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2022.104980
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  8. Article ; Online: Multiplexed protein profiling reveals spatial subcellular signaling networks

    Shuangyi Cai / Thomas Hu / Mythreye Venkatesan / Mayar Allam / Frank Schneider / Suresh S. Ramalingam / Shi-Yong Sun / Ahmet F. Coskun

    iScience, Vol 25, Iss 9, Pp 104980- (2022)

    2022  

    Abstract: Summary: Protein-protein interaction networks are altered in multi-gene dysregulations in many disorders. Image-based protein multiplexing sheds light on signaling pathways to dissect cell-to-cell heterogeneity, previously masked by the bulk assays. ... ...

    Abstract Summary: Protein-protein interaction networks are altered in multi-gene dysregulations in many disorders. Image-based protein multiplexing sheds light on signaling pathways to dissect cell-to-cell heterogeneity, previously masked by the bulk assays. Herein, we present a rapid multiplexed immunofluorescence (RapMIF) method measuring up to 25-plex spatial protein maps from cultures and tissues at subcellular resolution, providing combinatorial 272 pairwise and 1,360 tri-protein signaling states across 33 multiplexed pixel-level clusters. The RapMIF pipeline automated staining, bleaching, and imaging of the biospecimens in a single platform. RapMIF showed that WNT/β-catenin signaling upregulated upon the inhibition of the AKT/mTOR pathway. Subcellular protein images demonstrated translocation patterns, spatial receptor discontinuity, and subcellular signaling clusters in single cells. Signaling networks exhibited spatial redistribution of signaling proteins in drug-responsive cultures. Machine learning analysis predicted the phosphorylated β-catenin expression from interconnected signaling protein images. RapMIF is an ideal signaling discovery approach for precision therapy design.
    Keywords Biological sciences ; Biotechnology ; Biological sciences research methodologies ; Biology experimental methods ; Science ; Q
    Language English
    Publishing date 2022-09-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology.

    Hu, Thomas / Allam, Mayar / Cai, Shuangyi / Henderson, Walter / Yueh, Brian / Garipcan, Aybuke / Ievlev, Anton V / Afkarian, Maryam / Beyaz, Semir / Coskun, Ahmet F

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 8260

    Abstract: Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single ... ...

    Abstract Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet's ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.
    MeSH term(s) Female ; Male ; Humans ; Metabolomics/methods ; Systems Biology ; Lung Neoplasms
    Language English
    Publishing date 2023-12-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-43917-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

    Thomas Hu / Mayar Allam / Shuangyi Cai / Walter Henderson / Brian Yueh / Aybuke Garipcan / Anton V. Ievlev / Maryam Afkarian / Semir Beyaz / Ahmet F. Coskun

    Nature Communications, Vol 14, Iss 1, Pp 1-

    2023  Volume 20

    Abstract: Abstract Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of ... ...

    Abstract Abstract Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet’s ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.
    Keywords Science ; Q
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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