New methods for detection, analysis, and isolation of rare cell populations

James F. Leary; Scott R. McLaughlin; Kristina S. Kavanau

doi:10.1117/12.239512

10 May 1996 New methods for detection, analysis, and isolation of rare cell populations

James F. Leary, Scott R. McLaughlin, Kristina S. Kavanau

Proceedings Volume 2678, Optical Diagnostics of Living Cells and Biofluids; (1996) https://doi.org/10.1117/12.239512
Event: Photonics West '96, 1996, San Jose, CA, United States

Abstract

New methods for flow cytometric detection, analysis and cell sorting of rare (< 0.001 percent) cell subpopulations have been developed. A 2-stage system of high-speed cell (and data) classification (U.S. Patent 5,204,884) allows first-stage cell classifications and error-Checking at rates in excess of 100 000 cells/sec. Multi-queuing, secondary-stage electronic processing on the basis of up to 11 parameters, including mathematical functions such as principal components calculated in real-time using lookup tables, allows for reduction of false-positive cells multiply-labeled with positive- and negative-selection fluorescent markers. This has allowed detection of rare cell subpopulations with frequencies below 10^-6. Special "flexible sorting" hardware and software (U.S. Patent 5,199,576) permits sorting of cell subpopulations on the basis of mathematical algorithms which are not limited to conventional rectilinear or bitmap sort boundaries. Both high-speed enrichment sorting of live cells and high-resolution sorting for single-cell molecular characterizations are supported. New sampling statistics software allows for prediction of required sorting times for rare cell subpopulations. Home-built "bridging" software facilitates analysis of rare cells by a number of home-built (e.g. PC/Biplot) and commercial software packages (e.g. S-Plus for Windows). 3D stereo visualization and interactive software provide viewing of three raw and/or mathematically transformed or constructed data parameters, to aid in subsequent selection of optimal sort criteria. Special software has been developed for improved data analysis and selection of sort boundaries through the use of cell classification-tagged listmode data mixtures. This permits comparison of different classification algorithms (e.g. cluster analysis, neural networks, or recursive partitioning) for rare cells.

Citation Download Citation

James F. Leary, Scott R. McLaughlin, and Kristina S. Kavanau "New methods for detection, analysis, and isolation of rare cell populations", Proc. SPIE 2678, Optical Diagnostics of Living Cells and Biofluids, (10 May 1996); https://doi.org/10.1117/12.239512

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