图像激活细胞分选

IF 37.6
Tianben Ding, Kelvin C. M. Lee, Kevin K. Tsia, T. Nicolai Siegel, Dino Di Carlo, Keisuke Goda
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引用次数: 0

摘要

随着高质量基因组数据库的迅速扩展、表观遗传因素的发现以及CRISPR-Cas技术的出现,基因组研究领域发生了显著的转变。然而,一个主要的挑战仍然是将这些遗传和表观遗传扰动与尺度上的空间分辨细胞表型联系起来。图像激活细胞分选(IACS)提供了一种解决这一挑战的方法,它能够以每秒1000次以上的高速率对悬浮物体(例如单个活细胞、细胞簇或粘附在载体上的细胞)进行实时图像分选。与依赖于细胞的一维荧光强度谱的荧光激活细胞分选(FACS)不同,IACS利用多维光学成像来捕捉细胞特征的全部复杂性,实现基于视觉和功能属性的高含量分选。IACS的一个显著特点是它能够集成人工智能进行实时图像分析,从而在分类过程中实现复杂和精确的决策。本文对IACS的原理、组成和关键绩效指标进行了探讨。我们还强调其在微生物学,免疫学,癌症生物学,食品科学和可持续性科学等领域的独特应用,同时解决IACS面临的挑战和未来的机遇。我们的目标是使本综述成为IACS初学者和经验丰富的用户的全面指南,促进其采用并推动生物学和医学领域的发现。基因组研究已经被基因组数据库、表观遗传学发现和精确编辑所改变,但将遗传和表观遗传学变化与细胞表型联系起来仍然具有挑战性。在这篇综述中,我们探讨了图像激活细胞分选(IACS)的原理、组成和应用,并讨论了它如何通过实现高速率、实时的基于图像的分选来解决这些限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Image-activated cell sorting

Image-activated cell sorting
The field of genomic research has undergone a remarkable transformation with the rapid expansion of high-quality genome databases, the discovery of epigenetic factors and the advent of CRISPR–Cas technology. However, a major challenge remains in linking these genetic and epigenetic perturbations to spatially resolved cellular phenotypes at scale. Image-activated cell sorting (IACS) offers a way to address this challenge by enabling real-time image-based sorting of suspended objects (for example, single live cells, cell clusters or cells adhered to carriers) at high rates of over 1,000 events per second. Unlike fluorescence-activated cell sorting (FACS), which relies on one-dimensional fluorescence intensity profiles of cells, IACS leverages multi-dimensional optical imaging to capture the full complexity of cellular characteristics, enabling high-content sorting based on both visual and functional attributes. A distinctive feature of IACS is its ability to integrate artificial intelligence for real-time image analysis, enabling sophisticated and precision decision-making in the sorting process. In this Review, we explore the principles, components and key performance indicators of IACS. We also highlight its unique applications across fields such as microbiology, immunology, cancer biology, food science and sustainability science, while addressing the challenges and future opportunities that lie ahead for IACS. Our goal is for this Review to serve as a comprehensive guide to IACS for beginners and experienced users alike, fostering its adoption and driving discoveries across biology and medicine. Genomic research has been transformed by genome databases, epigenetic discoveries and precise editing, yet linking genetic and epigenetic changes to cellular phenotypes remains challenging. In this Review, we explore the principles, components and applications of image-activated cell sorting (IACS) and discuss how it can address these limitations by enabling high-rate, real-time image-based sorting.
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