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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
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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
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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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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
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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
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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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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
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Article ; Online: Spatially resolved 3D metabolomic profiling in tissues.

Ganesh, Shambavi / Hu, Thomas / Woods, Eric / Allam, Mayar / Cai, Shuangyi / Henderson, Walter / Coskun, Ahmet F

Science advances

2021 Volume 7, Issue 5

Abstract: Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic ... ...

Abstract	Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic profiling framework (3D-SMF) to map out the spatial organization of metabolic fragments and protein signatures in immune cells of human tonsils. In this method, 3D metabolic profiles were acquired by time-of-flight secondary ion mass spectrometry to profile up to 189 compounds. Ion beams were used to measure sub-5-nanometer layers of tissue across 150 sections of a tonsil. To incorporate cell specificity, tonsil tissues were labeled by an isotope-tagged antibody library. To explore relations of metabolic and cellular features, we carried out data reduction, 3D spatial correlations and classifications, unsupervised K-means clustering, and network analyses. Immune cells exhibited spatially distinct lipidomic fragment distributions in lymphatic tissue. The 3D-SMF pipeline affects studying the immune cells in health and disease.
MeSH term(s)	Cluster Analysis ; Humans ; Metabolome ; Metabolomics/methods ; Spectrometry, Mass, Secondary Ion
Language	English
Publishing date	2021-01-27
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2810933-8
ISSN	2375-2548 ; 2375-2548
ISSN (online)	2375-2548
ISSN	2375-2548
DOI	10.1126/sciadv.abd0957
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Article: Spatially variant immune infiltration scoring in human cancer tissues.

Allam, Mayar / Hu, Thomas / Lee, Jeongjin / Aldrich, Jeffrey / Badve, Sunil S / Gökmen-Polar, Yesim / Bhave, Manali / Ramalingam, Suresh S / Schneider, Frank / Coskun, Ahmet F

NPJ precision oncology

2022 Volume 6, Issue 1, Page(s) 60

Abstract: The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients' tumor immunity. However, immune cells ... ...

Abstract	The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients' tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors' immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients' tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor's immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.
Language	English
Publishing date	2022-09-01
Publishing country	England
Document type	Journal Article
ISSN	2397-768X
ISSN	2397-768X
DOI	10.1038/s41698-022-00305-4
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Article: The role of tumor-stroma interactions on desmoplasia and tumorigenicity within a microengineered 3D platform

Saini, Harpinder / Rahmani Eliato, Kiarash / Veldhuizen, Jaimeson / Zare, Azadeh / Allam, Mayar / Silva, Casey / Kratz, Alex / Truong, Danh / Mouneimne, Ghassan / LaBaer, Joshua / Ros, Robert / Nikkhah, Mehdi

Biomaterials. 2020 July, v. 247

2020

Abstract: The tumor microenvironment has been demonstrated to play a crucial role in modulating cancer progression. Amongst various cell types within the tumor microenvironment, cancer associated fibroblasts (CAFs) are in abundance, serving to modulate the ... ...

Abstract	The tumor microenvironment has been demonstrated to play a crucial role in modulating cancer progression. Amongst various cell types within the tumor microenvironment, cancer associated fibroblasts (CAFs) are in abundance, serving to modulate the biophysical properties of the stromal matrix, through excessive deposition of extracellular matrix (ECM) proteins that leads to enhanced tumor progression. There is still a critical need to develop a fundamental framework on the role of tumor-stromal cell interactions on desmoplasia and tumorigenicity. Herein, we developed a 3D microengineered organotypic tumor-stroma model incorporated with breast cancer cells surrounded by CAF-embedded collagen matrix. We further integrated our platform with atomic force microscopy (AFM) to study the dynamic changes in stromal stiffness during active tumor invasion.Our findings primarily demonstrated enhanced tumor progression in the presence of CAFs. Furthermore, we highlighted the crucial role of crosstalk between tumor cells and CAFs on stromal desmoplasia, where we identified the role of tumor-secreted PDGF-AA/-BB on elevated matrix stiffness. Inhibition of the activity of PDGFRs in CAFs led to attenuation of stromal stiffness. Overall, our work presents a well-controlled tumor microenvironment model capable of dissecting specific biophysical and biochemical signaling cues which lead to stromal desmoplasia and tumor progression.
Keywords	atomic force microscopy ; biocompatible materials ; breast neoplasms ; collagen ; extracellular matrix ; fibroblasts ; models ; neoplasm progression
Language	English
Dates of publication	2020-07
Publishing place	Elsevier Ltd
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	603079-8
ISSN	0142-9612
ISSN	0142-9612
DOI	10.1016/j.biomaterials.2020.119975
Database	NAL-Catalogue (AGRICOLA)

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