Mariya Mardamshina

All Publications

• Integrated spatial proteomic analysis of breast cancer heterogeneity unravels cancer cell phenotypic plasticity. Nature communications Mardamshina, M., Karagach, S., Mohan, V., Arad, G., Necula, D., Golani, O., Fellus-Alyagor, L., Shenoy, A., Krol, K., Pirak, D., Itzhacky, N., Marin, I., Shalmon, B., Addadi, Y., Sharan, R., Gal-Yam, E., Barshack, I., Geiger, T. 2025; 16 (1): 10482

  Abstract

  Tumor heterogeneity drives drug resistance and relapse, influencing immune evasion and tumor progression. While intratumor heterogeneity has been extensively studied at the genomic level, its functional outcomes and interactions with the tumor microenvironment remain underexplored. In contrast, the functional outcome of heterogeneity and the interplay with the tumor microenvironment have not been addressed. In this study, we integrate multi-region spatial MS-based proteomics of 280 tumor regions, exome sequencing, and imaging to investigate spatial proteomic heterogeneity in breast cancer. Our findings reveal increased proteomic heterogeneity with tumor progression, independent of genomic heterogeneity but closely associated with microenvironmental differences. Integration with immune and stromal imaging highlighted a dynamic interplay where low-grade tumors exhibit constrained immune infiltration, and upon progression to higher grades, macrophages and Tcells infiltrate. However, anti-inflammatory pathways involving kynurenine and prostaglandins are more highly expressed in infiltrated regions, suggesting that anti-tumorigenic activities are inhibited. Integration with the global protein network provides potential targetable mediators of immune evasion in breast cancer that can serve as the basis for future development of personalized breast cancer therapies.

  View details for DOI 10.1038/s41467-025-65477-6

  View details for PubMedID 41290667

• Latent plasticity of the human pancreas across development, health, and disease. bioRxiv : the preprint server for biology Mereu, E., Balboa, D., Liebig, J., Gonzalez-Herrero, A., Martinez-Casals, A., Mardamshina, M., Mollandin, F., Schicktanz, F., Tosti, L., Vandenbempt, V., Avrahami, D., Bernardo, E., Björklund, F., Chua, R. L., Engelse, M., García-Hurtado, J., Groen, N., Hanegraaf, M., Iañez, P., Jechow, K., Konukiewitz, B., Lawerenz, C., Marchese, D., Muraro, M. J., Pellegrini, S., Sordi, V., Sudy, A., Taron, U., Ten, F. W., Trefzer, T., Twardziok, S., van Agen, M., Carlotti, F., de Koning, E., Ferrer, J., Glaser, B., Heyn, H., Lundberg, E., Piemonti, L., Steiger, K., van Oudenaarden, A., Weichert, W., Conrad, C., Eils, R. 2025

  Abstract

  The pancreas plays a central role in major human diseases, yet our understanding of its cellular diversity and plasticity remains incomplete. Here, we present a single-cell multiomics atlas of the human pancreas, profiling over four million cells and nuclei from 57 donors across fetal development, adult homeostasis, and type 2 diabetes (T2D). Integrating sc/snRNA-seq, snATAC-seq, VASA-seq, spatial transcriptomics (Xenium), and multiplexed proteomics (CODEX), we resolve gene expression, chromatin accessibility, and spatial organization at high resolution. We identify transcriptionally plastic centroacinar-like cells (pCACs) in adults with fetal-like features, delineate endocrine and exocrine lineage trajectories during development, and uncover HNF1A-defined beta cell epigenetic states. In T2D, we observe shifts in beta cell subtypes and altered regulatory programs. Glucose perturbation of healthy islets reveals cell-type-specific adaptation and stress responses. This atlas provides a foundational framework to understand pancreas biology and the role of cellular plasticity in regeneration and disease.

  View details for DOI 10.1101/2025.10.01.679230

  View details for PubMedID 41256699

  View details for PubMedCentralID PMC12622017

• Streamlining Multiplexed Tissue Image Analysis with PIPSigmaX: An Integrated Automated Pipeline for Image Processing and EXploration for Diverse Tissue Types. bioRxiv : the preprint server for biology Mardamshina, M., Navarro, F. B., Casals, A. M., Avenel, C., Wahlby, C., Lundberg, E. 2025

  Abstract

  Spatial proteomics via multiplexed tissue imaging is transforming how we study biology, enabling researchers to investigate dozens of markers in a single tissue section and explore how cells behave in their native habitat. While imaging technologies have advanced rapidly, data analyses remain a bottleneck. To address this, we developed PIPSigmaX (Pipeline for Image Processing and EXploration), a user-friendly, end-to-end open-source software designed to make complex image analysis approachable, even for those with little or no programming skills. PIPSigmaX combines robust automation with an intuitive graphical user interface, guiding users through each step of the analysis, from image preprocessing and membrane-aware cell segmentation to signal quantification and spatial data exploration. Each feature includes built-in explanations, recommendations, and quality controls to help users make confident choices throughout the process. PIPSigmaX is compatible with a wide range of multiplexed imaging platforms, and its outputs integrate seamlessly with visualization tools like TissUUmaps and QuPath. Also, it supports downstream applications by enabling direct export of selected cell coordinates for laser microdissection. This functionality facilitates precise isolation of target cell populations for deep proteomic or transcriptomic profiling. With PIPSigmaX, researchers can extract meaningful biological insights from multiplexed images more easily and robustly, helping to bridge the gap between powerful imaging technologies and real-world scientific discovery.

  View details for DOI 10.1101/2025.05.04.652145

  View details for PubMedID 40654620

• High-parametric protein maps reveal the spatial organization in early-developing human lung. Nature communications Sariyar, S., Sountoulidis, A., Hansen, J. N., Marco Salas, S., Mardamshina, M., Martinez Casals, A., Ballllosera Navarro, F., Andrusivova, Z., Li, X., Czarnewski, P., Lundeberg, J., Linnarsson, S., Nilsson, M., Sundström, E., Samakovlis, C., Lundberg, E., Ayoglu, B. 2024; 15 (1): 9381

  Abstract

  The respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lung and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.

  View details for DOI 10.1038/s41467-024-53752-x

  View details for PubMedID 39477961

  View details for PubMedCentralID PMC11525936

• Proteogenomics of glioblastoma associates molecular patterns with survival CELL REPORTS Yanovich-Arad, G., Ofek, P., Yeini, E., Mardamshina, M., Danilevsky, A., Shomron, N., Grossman, R., Satchi-Fainaro, R., Geiger, T. 2021; 34 (9): 108787

  Abstract

  Glioblastoma (GBM) is the most aggressive form of glioma, with poor prognosis exhibited by most patients, and a median survival time of less than 2 years. We assemble a cohort of 87 GBM patients whose survival ranges from less than 3 months and up to 10 years and perform both high-resolution mass spectrometry proteomics and RNA sequencing (RNA-seq). Integrative analysis of protein expression, RNA expression, and patient clinical information enables us to identify specific immune, metabolic, and developmental processes associated with survival as well as determine whether they are shared between expression layers or are layer specific. Our analyses reveal a stronger association between proteomic profiles and survival and identify unique protein-based classification, distinct from the established RNA-based classification. By integrating published single-cell RNA-seq data, we find a connection between subpopulations of GBM tumors and survival. Overall, our findings establish proteomic heterogeneity in GBM as a gateway to understanding poor survival.

  View details for DOI 10.1016/j.celrep.2021.108787

  View details for Web of Science ID 000625315900016

  View details for PubMedID 33657365

• Proteomics of Melanoma Response to Immunotherapy Reveals Mitochondrial Dependence CELL Harel, M., Ortenberg, R., Varanasi, S., Mangalhara, K., Mardamshina, M., Markovits, E., Baruch, E. N., Tripple, V., Arama-Chayoth, M., Greenberg, E., Shenoy, A., Ayasun, R., Knafo, N., Xu, S., Anafi, L., Yanovich-Arad, G., Barnabas, G. D., Ashkenazi, S., Besser, M. J., Schachter, J., Bosenberg, M., Shadel, G. S., Barshack, I., Kaech, S. M., Markel, G., Geiger, T. 2019; 179 (1): 236-+

  Abstract

  Immunotherapy has revolutionized cancer treatment, yet most patients do not respond. Here, we investigated mechanisms of response by profiling the proteome of clinical samples from advanced stage melanoma patients undergoing either tumor infiltrating lymphocyte (TIL)-based or anti- programmed death 1 (PD1) immunotherapy. Using high-resolution mass spectrometry, we quantified over 10,300 proteins in total and ∼4,500 proteins across most samples in each dataset. Statistical analyses revealed higher oxidative phosphorylation and lipid metabolism in responders than in non-responders in both treatments. To elucidate the effects of the metabolic state on the immune response, we examined melanoma cells upon metabolic perturbations or CRISPR-Cas9 knockouts. These experiments indicated lipid metabolism as a regulatory mechanism that increases melanoma immunogenicity by elevating antigen presentation, thereby increasing sensitivity to T cell mediated killing both in vitro and in vivo. Altogether, our proteomic analyses revealed association between the melanoma metabolic state and the response to immunotherapy, which can be the basis for future improvement of therapeutic response.

  View details for DOI 10.1016/j.cell.2019.08.012

  View details for Web of Science ID 000486618500026

  View details for PubMedID 31495571

  View details for PubMedCentralID PMC7993352