Synchronous differentiated thyroid carcinoma (DTC) and anaplastic thyroid carcinoma (ATC) can coexist within the same patient, providing a unique opportunity to study tumor dedifferentiation and progression within a shared genetic and microenvironmental background. However, the spatial and cellular mechanisms underlying anaplastic transformation remain poorly understood. In this project, we will analyze four thyroid cancer cases containing both differentiated and anaplastic tumor components using an integrated spatial and single-cell transcriptomics approach. High-resolution spatial gene expression profiling has been performed using the 10x Genomics Visium HD platform on tissue sections, generating detailed transcriptomic maps across entire tumors. In parallel, single-cell RNA sequencing (scRNA-seq) will be performed on cells isolated from formalin-fixed paraffin-embedded (FFPE) tissue from the same samples, enabling cell-type–resolved transcriptional profiling. The main objectives are to (i) characterize intratumoral heterogeneity across differentiated, anaplastic, and transitional tumor regions, (ii) identify transcriptional programs and cell states associated with loss of differentiation and aggressive tumor behavior, and (iii) map tumor–microenvironment interactions driving anaplastic transformation. Integration of scRNA-seq data with Visium HD spatial transcriptomics will allow accurate cell-type annotation of spatial features and the deconvolution of complex tissue regions, providing insights that cannot be obtained from either modality alone. The computational workflow will include processing and quality control of large-scale Visium HD datasets and FFPE-derived scRNA-seq data, normalization and batch correction, dimensionality reduction, clustering, differential expression analysis, pathway enrichment, and spatial neighborhood and interaction analyses. Cross-modality data integration and visualization will be performed to map single-cell–defined states back to spatial contexts. Due to the high resolution and data volume of Visium HD, the size of associated imaging and HDF5 files, and the complexity of multi-modal integration, these analyses are computationally intensive and require substantial memory, fast storage, and parallel computing resources. This study will provide novel insights into the spatial and cellular mechanisms underlying thyroid cancer progression and anaplastic transformation. Access to dedicated high-performance computing infrastructure is essential to efficiently process, integrate, and analyze these high-dimensional datasets and to ensure robust and reproducible results.