Wednesday, December 4 2024
15:30 - 16:30

Alladi Ramakrishnan Hall

Unravelling Cellular States in Health and Disease through Multi-Omics Approaches

Ankit Agrawal

University of Würzburg

Cellular states in tissue are characterized by morphology, function, molecular composition, and spatial organization. Since different experimental techniques measure these aspects independently, integrating data across multiple layers is essential to thoroughly understand cellular states and their covariation in various tissue states. Cell states are shaped by both intrinsic factors, such as gene expression, and extrinsic factors, such as signals from the tissue microenvironment. Combining these intrinsic and extrinsic influences is critical for understanding cell fate regulation during development, tissue homeostasis, and disease progression. Observing cellular states solely based on morphology and spatial locations has revealed key regulatory mechanisms in tissue such as bone, where cells undergo a distinct morphological transformation during differentiation. Moreover, tracing cellular states along a temporal scale gives insight into bone tissue growth strategies. Observing cellular states simultaneously from RNA and spatial coordinates further enhances our understanding of cell type covariation in the formation of tissue niches and in cell-cell communication in different tissue states. In this context, we introduce NiCo (Niche Covariation), a novel method that integrates spatial transcriptomics modality with scRNAseq modality. NiCo's unique ability to analyze the colocalization of cellular states from single-cell spatial resolution is a significant leap in the field. The integration approach transfers the cell type label from sequencing modality to spatial and identifies the cell type interactions in the tissue niche. NiCo further infers spatial covariation of latent factors, capturing cell state variability and interpreting these factors by leveraging transcriptome-wide information from scRNAseq reference. Applying NiCo to a mouse liver dataset, we were able to predict novel niche interactions that contribute to cell state variation. For instance, NiCo predicted a feedback mechanism between Kupffer cells and neighboring stellate cells that limits stellate cell activation in the normal liver. This application of NiCo not only demonstrates its potential as a valuable tool for understanding omics data but also its role in providing deeper insights into the dynamics of healthy, regenerative, and diseased tissue states. By unraveling these intricate cellular interactions, NiCo and its future version promises to significantly enhance our understanding of the factors that influence cellular states in health and disease, offering hope for more effective treatments and interventions and enlightening us about the complex nature of cellular states.



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