Molecular Characterization of Rare Single Tumor Cells

Rare cells can be defined as those of less than 0.1% frequency.Ultra-rare cells can be defined as those of less than 0.001% frequency. These are terms for ease of discussion and are not universally agreed upon. In terms of flow cytometry and cell sorting, rare cell applications not only push the limits of the technology but also require levels of staining specificity beyond those assumed by most biologists. It is important for engineers to understand that good technology can bemade irrelevant by bad cell staining and preparation. The greatest difficulty with rare cell applications is not the immediate technological constraints but the requirement that there be no weak links in the experimental methodology anywhere in the process from cell preparation, flow cytometry/cell sorting and data analysis or subsequent analysis of isolated cells. Each and every one of these steps in the methodology must be excellent and the entire process must be thought through to eliminate or deal with the weaker links of a given rare cell application. For reviews on “rare event” analysis techniques, see Refs. [1–3]. There are many rare cell applications of importance to basic or clinical research. The ones discussed in this paper help show the range not only of the applications but also of the technological challenges to engineers working in this field. Some basic research examples that are briefly discussed include: (1) isolation of rare cell clones with specific mutations or transfected genes, (2) isolation of clones with combinatorial libraries of inserted genes and (3) studies of environmentally inducedmutations in human cells.

[1]  James F. Leary,et al.  High-speed cell classification systems for real-time data classification and cell sorting , 1997, Photonics West - Biomedical Optics.

[2]  James F. Leary,et al.  Comparison of multidimensional flow cytometric data by a novel data mining technique , 2007, SPIE BiOS.

[3]  R T Stovel,et al.  Individual cell sorting. , 1979, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[4]  T. Lindmo,et al.  Measurements of the distribution of time intervals between cell passages in flow cytometry as a method for the evaluation of sample preparation procedures. , 1981, Cytometry.

[5]  R. Hubert,et al.  Whole genome amplification from a single cell: implications for genetic analysis. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[6]  M. Wigler,et al.  PTEN, a Putative Protein Tyrosine Phosphatase Gene Mutated in Human Brain, Breast, and Prostate Cancer , 1997, Science.

[7]  James F. Leary,et al.  Real-time decision making for high-throughput screening applications , 2001 .

[8]  James F. Leary,et al.  High-speed real-time data classification and cell sorting using discriminant functions and probabilities of misclassification for stem cell enrichment and tumor purging , 1998, Photonics West - Biomedical Optics.

[9]  Feng He,et al.  Detection and isolation of single tumor cells containing mutated DNA sequences , 1999, Photonics West - Biomedical Optics.

[10]  J R Beck,et al.  The use of relative operating characteristic (ROC) curves in test performance evaluation. , 1986, Archives of pathology & laboratory medicine.

[11]  James F. Leary,et al.  Importance of high-throughput cell separation technologies for genomics/proteomics-based clinical diagnostics , 2002, SPIE BiOS.

[12]  Nan Wang,et al.  Getting the right cells to the array: Gene expression microarray analysis of cell mixtures and sorted cells , 2004, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[13]  W. Anderson,et al.  Gene-marking to trace origin of relapse after autologous bone-marrow transplantation , 1993, The Lancet.

[14]  James F. Leary,et al.  New high-speed cell sorting methods for stem cell sorting and breast cancer cell purging , 1998, Photonics West - Biomedical Optics.

[15]  James F. Leary,et al.  Real-time multivariate statistical classification of cells for flow cytometry and cell sorting: a data mining application for stem cell isolation and tumor purging , 1999, Photonics West - Biomedical Optics.

[16]  S. Shuman,et al.  Novel approach to molecular cloning and polynucleotide synthesis using vaccinia DNA topoisomerase. , 1994, The Journal of biological chemistry.

[17]  James F. Leary,et al.  High‐Speed Cell Sorting , 1997, Current protocols in cytometry.

[18]  A van Rotterdam,et al.  Models for the electronic processing of flow cytometric data at high particle rates. , 1992, Cytometry.

[19]  J A Hokanson,et al.  Theoretical basis for sampling statistics useful for detecting and isolating rare cells using flow cytometry and cell sorting. , 1997, Cytometry.

[20]  J. Hanley Receiver operating characteristic (ROC) methodology: the state of the art. , 1989, Critical reviews in diagnostic imaging.

[21]  Bernard C. K. Choi Slopes of a Receiver Operating Characteristic Curve and Likelihood Ratios for a Diagnostic Test , 1998 .

[22]  J A Hokanson,et al.  Some theoretical and practical considerations for multivariate statistical cell classification useful in autologous stem cell transplantation and tumor cell purging. , 1999, Cytometry.

[23]  John A. Swets,et al.  Evaluation of diagnostic systems : methods from signal detection theory , 1982 .

[24]  James F. Leary,et al.  Rare‐Event Detection and Sorting of Rare Cells , 2002 .

[25]  James F. Leary,et al.  Application of a new novel data-mining technique to cytometry data , 2000, Photonics West - Biomedical Optics.

[26]  K. Bauer,et al.  Improved detection of rare CALLA-positive cells in peripheral blood using multiparameter flow cytometry. , 1984, Journal of immunological methods.