Spectroscopy Assisted Laser Microdissection

点击翻译按钮获取中文摘要

Abstract Molecular understanding of tumors relies greatly on appropriate samples to be prepared from epithelial cells in tissues. Epithelial cells, however, are often surrounded by other cell types and extracting pure populations of these cells is crucial for correct biospecimen preparation and resulting accuracy of molecular assays. Laser microdissection (LM) has contributed immensely in this effort due to its high spatial specificity in the extraction of defined cell populations and ease of use. While LM has enhanced the precision of biochemical analysis, several drawbacks remain. The necessity of staining and human supervision limits throughput, molecular yield and purity of samples. There is little explicit control or confidence in the purity of extracted cell populations while it is difficult to extract multiple cells from the same sample. Combining the morphologic specificity of microscopy and molecular sensitivity of spectroscopy, infrared (IR) spectroscopic imaging been employed to automate histopathologic recognition in complex tissues using artificial intelligence algorithms applied of spectral data. This project will demonstrate a completely automated instrument by coupling LM with IR microscopy. Termed spectroscopy-assisted laser microdissection (SLaM), the developed prototype will be validated using state of the art IR imaging systems and commercial LCM in terms of accuracy, speed and type fidelity. Last, the approach will be applied to extract cells of different types from the same prostate sample to demonstrate the capability to multiplex LM (muxLM) from the same tissue. The project directly addresses the need to reduce the time- and labor-intensive nature of LM. SLaM can maximize the quality and utility of biological samples used for downstream analyses by automation, high throughput and precision while enabling a comprehensive acquisition of cells without user fatigue or error, thereby providing a sample of higher integrity and quality for cancer molecular analysis.