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Laser capture microdissection
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AI
DOI：10.1038/s43586-025-00453-4.png)
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Abstract
En 中文
The tissue microenvironment was once viewed as an impossibly complex heterogeneous ecosystem. Today, tools and data analysis methods probe tissues at multiple resolution levels, from subpopulations to individual cells and individual molecules. Laser capture microdissection (LCM) was one of the first technologies enabling spatial molecular interrogation in tissues, enabling the extraction of precise regions of tissue for downstream acquisition and analysis of multiple classes of molecular analytes — including nucleic acids, proteins, metabolites and lipids. By physically isolating targeted cells or tissue compartments under microscopic visualization, LCM preserves spatial context for downstream multiomic profiling. The approach is agnostic to tissue type, molecular target and analytical tool, providing a powerful platform for spatially resolved analysis of tissue heterogeneity, microenvironment interactions and region-specific signatures. The attributes of LCM have drawn a large and diverse community of users who have imaginatively elevated the technology and its applications far beyond the early versions of LCM. In this Primer, we provide examples of how and when to use LCM and discuss its use with other spatial profiling platforms, providing an essential component of large-scale clinical research trials, revealing disease mechanisms and identifying predictors of therapeutic efficacy. Finally, we explore how the next generation of LCM and companion technologies will help to shape the personalized, designer therapies of the future. Laser capture microdissection is a technique that uses a highly precise laser to isolate and collect specific cells from a tissue sample. In this Primer, Liotta et al. discuss the use of laser capture microdissection in clinical research and its potential in future personalized therapies.
Keywords:
Laser capture microdissection
tissue microenvironment
spatial profiling
multiomic analysis
personalized therapy
Journal
IF:
56
Papers:
92
Citations:
9.3K
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