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A suitable method for identifying cell aggregates in laser scanning cytometry listmode data for analyzing
Stanley E Shackney1, Charles A Smith, Agnese A Pollice
1Department of Human Oncology and Allegheny Singer Research Institute, Allegheny General Hospital, Pittsburgh, PA 15212, USA. shackney@wpahs.org
Background:
The presence of cell aggregates in cell suspensions obtained from human solid tumors can interfere with the measurement of cell DNA content of cell singlets, and can confound multiparameter analysis of other measurements on the same cells. Flow cytometric corrections for cell aggregates based on signal pulse shape have not proven to be reliable. Mathematical models have been developed to correct for cell aggregates in binned DNA histogram data, but they are not suitable for the correction of correlated non-DNA measurements obtained on the same cells.
Methods:
A total of 21 samples representing a variety of normal and malignant human cell types, including normal lymphocytes, normal sputum, human breast cancer cell lines, and mechanically disaggregated cell suspensions from primary breast cancers and nonsmall cell lung cancers, were studied by laser scanning cytometry (LSC) using the CompuCyte laser scanning cytometer (Cambridge, MA). Nuclear area, nuclear perimeter, and an LSC-based cell texture parameter were measured on approximately 400 cells in each sample, using an air-cooled, violet laser emitting at a wavelength of 405 nm for DAPI excitation, and each cell was classified as a singlet or aggregate by its appearance under direct observation. A "saddle function" provided by CompuCyte was used, together with an algorithm based on the measurements of nuclear area, perimeter, and cell texture (the APT algorithm), to identify cell aggregates and exclude them from the listmode data file.
Results:
Proportions of cell aggregates in the uncorrected samples ranging from 6 to 56% (mean, 20%) were reduced to proportions ranging from 0 to 7% (mean, 2.4%) after correction. The discriminant function was "tuned" to maintain both average cell singlet purity and average cell singlet yield at >70% over a broad range of cell DNA contents.
Conclusions:
A combined approach to cell aggregate detection, which utilizes both the saddle function and the APT algorithm, produces list mode data files that exclude >80% of cell aggregates from samples of disaggregated cell suspensions of human tumors and other sources of clinical material. Such data files are suitable for multiparameter analysis.