&lt;title&gt;Real-time multivariate statistical classification of cells for flow cytometry and cell sorting: a data mining application for stem cell isolation and tumor purging&lt;/title&gt;

Leary, James F.; McLaughlin, Scott R.; Reece, Lisa M.; Rosenblatt, Judah; Hokanson, Jim

doi:10.1117/12.349196

Cited by 12 publications

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“…Over the past 20 years our laboratory has developed a large number of instrument hardware and software technologies and methodologies for dealing with the highthroughput processing of cells and other objects (9,(19)(20)(21) and a wide variety of cell labeling and manipulation techniques for rare-event analysis, namely the analysis and sorting of cell subpopulations with frequencies less than 0.1% (12,13). More recently, we have extended these procedures to the realm of ''ultra-rare'' cells, namely those cell subpopulations of frequencies less than 0.001% (12,16,22).…”

Section: Methods For High-throughput Flow Cytometry/cell Sortingmentioning

confidence: 99%

Ultra high‐speed sorting

Leary

2005

Cytometry Pt A

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BackgroundCell sorting has a history dating back approximately 40 years. The main limitation has been that, although flow cytometry is a science, cell sorting has been an art during most of this time. Recent advances in assisting technologies have helped to decrease the amount of expertise necessary to perform sorting.MethodsDroplet‐based sorting is based on a controlled disturbance of a jet stream dependent on surface tension. Sorting yield and purity are highly dependent on stable jet break‐off position. System pressures and orifice diameters dictate the number of droplets per second, which is the sort rate limiting step because modern electronics can more than handle the higher cell signal processing rates.ResultsCell sorting still requires considerable expertise. Complex multicolor sorting also requires new and more sophisticated sort decisions, especially when cell subpopulations are rare and need to be extracted from background. High‐speed sorting continues to pose major problems in terms of biosafety due to the aerosols generated.ConclusionsCell sorting has become more stable and predictable and requires less expertise to operate. However, the problems of aerosol containment continue to make droplet‐based cell sorting problematical. Fluid physics and cell viability restraints pose practical limits for high‐speed sorting that have almost been reached. Over the next 5 years there may be advances in fluidic switching sorting in lab‐on‐a‐chip microfluidic systems that could not only solve the aerosol and viability problems but also make ultra high‐speed sorting possible and practical through massively parallel and exponential staging microfluidic architectures. © 2005 International Society for Analytical Cytology

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Section: Methods For High-throughput Flow Cytometry/cell Sortingmentioning

confidence: 99%

Ultra high‐speed sorting

Leary

2005

Cytometry Pt A

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“…We are interested in a DNA, lipids, and CD45 panel because it doesn't use epithelial biomarkers and because the lipid label (Bodipy) is compatible with live cell imaging. The WBC‐MCF7 model system has been previously used by other investigators in studying CTCs . Fatty acid synthase has been shown to be over expressed in many cancers .…”

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Statistical performance of image cytometry for DNA, lipids, cytokeratin, & CD45 in a model system for circulation tumor cell detection

et al. 2017

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Detection of circulating tumor cells (CTCs) in a blood sample is limited by the sensitivity and specificity of the biomarker panel used to identify CTCs over other blood cells. In this work, we present Bayesian theory that shows how test sensitivity and specificity set the rarity of cell that a test can detect. We perform our calculation of sensitivity and specificity on our image cytometry biomarker panel by testing on pure disease positive (D+) populations (MCF7 cells) and pure disease negative populations (D−) (leukocytes). In this system, we performed multi-channel confocal fluorescence microscopy to image biomarkers of DNA, lipids, CD45, and Cytokeratin. Using custom software, we segmented our confocal images into regions of interest consisting of individual cells and computed the image metrics of total signal, second spatial moment, spatial frequency second moment, and the product of the spatial-spatial frequency moments. We present our analysis of these 16 features. The best performing of the 16 features produced an average separation of 3 standard deviations between D+ and D− and an average detectable rarity of ~1 in 200. We performed multivariable regression and feature selection to combine multiple features for increased performance and showed an average separation of 7 standard deviations between the D+ and D− populations making our average detectable rarity of ~1 in 480. Histograms and receiver operating characteristics (ROC) curves for these features and regressions are presented. We conclude that simple regression analysis holds promise to further improve the separation of rare cells in cytometry applications.

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Molecular Characterization of Rare Single Tumor Cells

Leary

2009

Single Cell Analysis

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<title>Real-time multivariate statistical classification of cells for flow cytometry and cell sorting: a data mining application for stem cell isolation and tumor purging</title>

Cited by 12 publications

References 14 publications

Ultra high‐speed sorting

Ultra high‐speed sorting

Statistical performance of image cytometry for DNA, lipids, cytokeratin, & CD45 in a model system for circulation tumor cell detection

Molecular Characterization of Rare Single Tumor Cells

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