Biology subjects

Mardamshina, M.

Publications and source records attributed to Mardamshina, M..

5 recordsLinked to original sources

bioRxiv2025

Latent plasticity of the human pancreas across development, health, and disease.

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.

genomics↗

bioRxiv2025

Streamlining Multiplexed Tissue Image Analysis with PIP{Sigma}X: An Integrated Automated Pipeline for Image Processing and EXploration for Diverse Tissue Types

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 PIP{Sigma}X (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. PIP{Sigma}X 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. PIP{Sigma}X 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 PIP{Sigma}X, 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. HighlightsO_LIPIP{Sigma}X offers a user-friendly "one-stop shop" pipeline for multiplexed tissue image analysis C_LIO_LIwithout coding C_LIO_LIWorks across diverse tissue types and imaging platforms at whole-slide scale C_LIO_LIIncludes membrane-aware segmentation and quality control features C_LIO_LISeamlessly integrates with visualization platforms like TissUUmaps and QuPath for data exploration C_LIO_LIEnables export for automated laser microdissection and spatial single-cell profiling C_LI

bioinformatics↗

bioRxiv2025

Multiplexed Deep Visual Proteomics Unveils Spatial Heterogeneity and Rare Endocrine States in Human Adult Pancreatic Islets

Pancreatic islets are highly specialized tissue compartments that regulate metabolism, and their dysfunction contributes to diseases such as prediabetes and diabetes. Characterizing islet morphology and cell plasticity is essential for understanding these pathophysiological states, yet high-resolution spatial proteomics remains challenging due to the islets small size and cellular complexity. Here, we present multiplexed deep visual proteomics (mxDVP), an approach that integrates high-plex imaging with ultra-sensitive mass spectrometry to achieve deep spatial proteome profiling of defined cell types in tissue sections. Enhanced segmentation, semi- automated annotation, and optimized laser microdissection enable the enrichment of rare endocrine populations that are often overlooked in single-cell analyses. mxDVP achieves deep proteome coverage of >6,000 proteins from as few as 100 cells, including low-abundance transcription factors critical for endocrine cell fate determination. By profiling over 864,000 human pancreatic cells, we identify 12 endocrine subtypes, including polyhormonal hybrids, revealing previously unrecognized islet heterogeneity, metabolic regulation, and cellular adaptability.

molecular biology↗

bioRxiv2024

High-parametric protein maps reveal the spatial organization in early-developing human lung

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 lungs and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.

developmental biology↗

bioRxiv2021

Proteomic landscape of multi-layered breast cancer internal tumor heterogeneity

Despite extensive research, internal tumor heterogeneity presents enormous challenges to achieve complete therapeutic responses. Changes in protein expression are central determinants of cancer phenotypes that reflect potential therapeutic targets. However, previous proteomic studies did not address internal heterogeneity, therefore, masked the necessary spatial resolution to achieve a comprehensive understanding of cancer complexity. Here we present the first large-scale multi-focal breast cancer proteomic study of 330 tumor regions which associated cancer cell function, pathological parameters, and spatial localization of each tumor region. We found marked internal proteomic heterogeneity even within tumors presenting homogeneous receptor expression. Additionally, analysis of the internal heterogeneity, based on coexisting receptor expression or histological patterns in single tumors, showed significant functional differences between homogeneous and heterogeneous tumors related to cancer metabolism, immunogenicity, and proliferation. We anticipate that this study will serve as a starting point towards the development of improved cancer therapy and diagnostics.

cancer biology↗