Bio

Mariya Mardamshina, MD, PhD, is a postdoctoral fellow in the Department of Bioengineering, working in Prof. Emma Lundberg's lab. She earned her medical degree from Semey State Medical University and completed her PhD at Tel Aviv University, where her research focused on spatial inter- and intratumoral heterogeneity in breast cancer using mass spectrometry-based proteomics. Currently, her work in the Lundberg lab centers on deciphering cell-to-cell proteomic variability within a spatial framework. Her research involves developing integrated pipelines that combine automated multiplexed staining, high-resolution microscopy, artificial intelligence, and ultra-high sensitivity mass spectrometry to achieve comprehensive proteomic analyses.

Stanford Advisors

Contact

  • Academic
    mmariya@stanford.edu
    University - Scholar Department: Bioengineering Position: Postdoctoral Scholar

Additional Info

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All Publications

  • 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

https://orcid.org/0000-0003-0066-0861