翻译(生物学)
转录组
计算机科学
计算生物学
数据科学
生物
遗传学
信使核糖核酸
基因
基因表达
作者
Tancredi Massimo Pentimalli,Nikos Karaiskos,Nikolaus Rajewsky
出处
期刊:PubMed
日期:2024-10-30
标识
DOI:10.1146/annurev-pathmechdis-111523-023417
摘要
Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.
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