Image-based cell sorting using a high throughput fluorescence imaging flow cytometer

 描述（由申请人提供）：从异质群体中分离亚群甚至单细胞的能力是现代生物学和生物医学的基本要求。用于分离亚群的常规技术包括荧光激活细胞分选（FACS）、磁激活细胞分选（MACS）、激光捕获显微切割和DEP阵列分选。虽然所有这些技术都用于单独的应用程序中，但每种技术都有自己的优点和缺点。最广泛用于从具有高纯度的群体中分离单细胞的技术是FACS，这归因于其相对的 温和的分选机制、高灵敏度、多参数测量能力以及分析细胞内和表面标记物的能力。虽然FACS广泛用于细胞生物学的所有领域，但由于其缺乏亚细胞分辨率，其仅基于细胞参数的平均值进行分选决策具有显著的局限性。例如，FACS仅测量来自细胞的总荧光，并且不能区分荧光探针位于细胞内的何处（例如在细胞膜上、在细胞质中或在细胞核中）如果希望分离可能已将蛋白质从细胞质转移到细胞核的细胞群体，这是一个显着的限制，或者如果希望例如基于核与胞质面积比的测量来分离细胞。 在这里，我们建议开发一种基于图像的细胞分选仪，该细胞分选仪基于新型信号处理算法和高通量成像流式细胞仪，该流式细胞仪已由Omega Biosystems开发。该工具将在该计划的第二阶段结束时完成并准备商业化，将在流式分选仪的灵敏度，特异性和空间分辨率方面提供变革性的进步，用于从异质细胞群体中分离单细胞和亚群。这种高分辨率细胞分选工具的出现将对生物医学和细胞生物学产生广泛的影响，并使药物发现和稀有细胞检测领域取得直接进展。

 DESCRIPTION (provided by applicant): The ability to isolate subpopulations or even single cells from heterogeneous populations is a fundamental necessity in modern biology and biomedicine. Conventional techniques for separating subpopulations include fluorescence activated cell sorting (FACS), magnetic activated cell sorting (MACS), laser capture microdissection, and DEP array sorting. While all of these techniques are utilized in individual applications, each has its own set of advantages and drawbacks. The technique most widely used for isolating single cells from populations with high purity is FACS, thanks to its relatively gentle sorting mechanism, high sensitivity, multiparameter measurement capability, and ability to analyze both intracellular and surface markers. While FACS is widely used across all areas of cell biology, it has a significant limitation of making sort decisions based only on the average value of a cell's parameters, due to its lack of sub-cellular resolution. For example, FACS only measures the total fluorescence from a cell, and is unable to distinguish where the fluorescent probe is localized within the cell (e.g. on the cell membrane, in the cytoplasm, or in the nucleus) This is a significant limitation if one desires to separate populations of cells that may have translocated a protein from the cytoplasm to the nucleus, or if one wishes to separate cells based on a measurement of the nuclear to cytoplasmic area ratio, for example. Here, we propose to develop an image-based cell sorter based on novel signal processing algorithms and a high throughput imaging flow cytometer, already developed by Omega Biosystems. This tool, to be completed and ready for commercialization by the end of Phase II of this program, will provide a transformative advance in the sensitivity, specificity and spatial resolution of flow sorters for separating single cells and subpopulations from a heterogeneous cellular population. The advent of such a high-resolution cell sorting tool will have a broad impact across biomedicine and cell biology, as well as enable immediate advances in the fields of drug discovery and rare cell detection.