Скачать или смотреть Spatial Transcriptomics Basics Part 2/2

Basics of Spatial Transcriptomics.
Mohammad Mobashir presented a comparative study differentiating spatial genomics/transcriptomics from normal NGS techniques, emphasizing spatial genomics' ability to preserve spatial information of genes and mutations within tissue. They also discussed the diverse applications of both techniques, with spatial genomics being beneficial for understanding tissue architecture and tumor microenvironments, and normal genomics for gene expression profiling and mutational analysis. Mohammad Mobashir further elaborated on the analytical capabilities of spatial genomics, including cell clustering and identifying differentially expressed genes across tissue regions to classify samples and identify driver mutations.
Spatial Genomics vs. Normal Genomics Mohammad Mobashir presented a comparative study highlighting the differences and applications of spatial genomics/transcriptomics and normal NGS techniques. They explained that spatial genomics focuses on the spatial location of genes and mutations within a tissue, preserving spatial information, unlike normal genomics which analyzes overall gene expression or mutations without specifying location. Spatial techniques combine RNA-seq with spatial barcoding or imaging, mapping gene expression patterns to specific tissue locations, while normal genomics methods like RNA-seq or whole-genome sequencing analyze gene expression or DNA sequences without spatial context.
Applications of Spatial and Normal Genomics Mohammad Mobashir outlined the diverse applications of both techniques. Spatial genomics is beneficial for understanding tissue architecture, tumor microenvironments, developmental biology, neurological research, and infectious diseases, by visualizing gene expression variations across tissue regions. Normal genomics applications include gene expression profiling, differential gene expression analysis, single-cell RNA-seq, and mutational/variant detection for cancer research, immunology, neuroscience, and personalized medicine. Mohammad Mobashir also presented case studies demonstrating spatial transcriptomics' utility in tumor microenvironment analysis, brain mapping, and inflammation studies.
Detailed Analysis and Classification Mohammad Mobashir elaborated on the analytical capabilities of spatial genomics, including clustering cells, identifying differentially expressed genes, and performing pathway enrichment analysis in different tissue regions. They demonstrated how these analyses could classify samples into cancerous, healthy, and adjacent tissues based on mutation profiles and identified dominant mutation types across samples. Furthermore, Mohammad Mobashir explained how spatial genomics helps identify driver mutations, which are critical for transforming normal cells into tumor cells, by comparing common and distinct mutations across samples.
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