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概要
The EasySep™ Direct Human CTC Enrichment Kit can be used to enrich Circulating Tumor Cells (CTCs) directly from human whole blood by immunomagnetic negative selection without the need for either density gradient centrifugation or RBC lysis. Isolated cells are immediately ready for downstream analysis or assays.
技术资料
| Document Type | 产品名称 | Catalog # | Lot # | 语言 |
|---|---|---|---|---|
| Product Information Sheet | EasySep™ Direct Human CTC Enrichment Kit | 19657 | All | English |
| Safety Data Sheet 1 | EasySep™ Direct Human CTC Enrichment Kit | 19657 | All | English |
| Safety Data Sheet 2 | EasySep™ Direct Human CTC Enrichment Kit | 19657 | All | English |
数据及文献
Data
Figure 1. Typical EasySep™ Direct Human CTC Enrichment Profile
Starting with human whole blood from healthy donors, spiked with approximately 1% of CAMA cells (epithelial tumor cell line), the typical CTC (epithelial cell+) content of non-lysed final enriched fraction is 79 ± 16 % (using the silver “Big Easy” EasySep™ Magnet; gated on DRAQ5™ for nucleated cells). Typically the log depletion of targeted CD45+ cells is 2.8 to 3.2. In the above example, CAMA cells were seeded into whole blood at a starting frequency of 0.98%. The CAMA cell (epithelial cell+) content of the enriched fraction is 92.02% with a 3.8 log depletion of CD45+ cells.
Publications (1)
Scientific reports 2020
Label-free detection of rare circulating tumor cells by image analysis and machine learning.
Abstract
Abstract
Detection and characterization of rare circulating tumor cells (CTCs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis.
