Chapter title |
Filtration and Analysis of Circulating Cancer Associated Cells from the Blood of Cancer Patients
|
---|---|
Chapter number | 32 |
Book title |
Biosensors and Biodetection
|
Published in |
Methods in molecular biology, March 2017
|
DOI | 10.1007/978-1-4939-6911-1_32 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6910-4, 978-1-4939-6911-1
|
Authors |
Cha-Mei Tang, Peixuan Zhu, Shuhong Li, Olga V. Makarova, Platte T. Amstutz, Daniel L. Adams |
Editors |
Ben Prickril, Avraham Rasooly |
Abstract |
Filtration is one of the most efficient methods to remove red and white blood cells from whole blood, while retaining larger cells on the surface of the filter. Precision pore microfilters, such as the CellSieve™ microfilters, are ideally suited for this purpose, as they are strong, with uniform pore size and distribution, and have low fluorescent background required for microscopic image analysis. We present a system to implement the filtration of whole blood in combination with CellSieve™ microfilters that is simple and straightforward to use. Being that the blood of cancer patients often contains both tumor cells and stromal cells associated with cancer that are larger than normal blood cells, microfiltration shows great promise in better understanding these cell types. Accurate identification and characterization of cancer associated cells has led to increased specificity as it relates to CTCs and epithelial-mesenchymal transition cells (EMTs) and enabled the identification of previously unknown cell types, such as cancer associated macrophage-like cells (CAMLs). Using a system that isolates both CTCs and circulating stromal cells, clinicians can better diagnose cancer patients to determine therapy, monitor treatment, and watch for recurrence. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 12 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 5 | 42% |
Researcher | 2 | 17% |
Student > Bachelor | 1 | 8% |
Unspecified | 1 | 8% |
Student > Doctoral Student | 1 | 8% |
Other | 1 | 8% |
Unknown | 1 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 5 | 42% |
Medicine and Dentistry | 2 | 17% |
Computer Science | 1 | 8% |
Unspecified | 1 | 8% |
Sports and Recreations | 1 | 8% |
Other | 1 | 8% |
Unknown | 1 | 8% |