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A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms.

ABSTRACT: Background:Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described. Subjects and Methods:Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system. Results:Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC) curve of 0.912. Conclusions:The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.

SUBMITTER: Angeletti C

PROVIDER: S-EPMC5937296 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms.

Angeletti Cesar C

Journal of pathology informatics 20180420

<h4>Background</h4>Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described.<h4>Subjects and methods</h4>Initially, Flow Cytometry Standard files f ...[more]

PMID: 29770255

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Project description:BackgroundPresently available flow cytometric methods of bromodeoxyuridine (BrdUrd) labelling do not provide information on the cell cycle time (TC) and the growth fraction (GF). In this paper, we describe a novel and simple method to estimate TC and GF from flow cytometric analysis of a single tumour sample after BrdUrd labelling.MethodsThe proposed method is based on two assumptions: (1) the number of labelled cells traversing the cell cycle per unit time is constant and (2) the total number of labelled cells is constant throughout the cycle, provided that cells produced after division are excluded. The total numbers of labelled divided G1 cells, labelled divided S cells, labelled undivided S cells, and labelled undivided G2 cells were obtained for DNA histograms of BrdUrd-positive cells in a collected sample. These cell numbers were used to write equations to determine the durations of cell cycle phases, TC and GF. To illustrate the application of the proposed formulae, cell cycle kinetic parameters were analysed in solid SL2 tumours growing in DBA/2 mice and in human T-leukaemia Jurkat cells in culture.ResultsThe suitability of the proposed method for estimating durations of the cell cycle phases, TC and GF was demonstrated. TC in SL2 tumours was found to be relatively constant at 4 and 10 days after tumour implantation (20.3 +/- 1.1 h and 21.6 +/- 0.9 h, respectively). GF in tumours at day 10 was lower than GF at day 4 (54.2 +/- 7.7% vs. 79.2 +/- 5.9%, p = 0.0003). Approximate values of TC and GF of cultured Jurkat cells were 23.9 h and 79.3%, respectively.ConclusionThe proposed method is relatively simple and permits estimation of the cell cycle parameters, including TC and GF, from a single tumour sample after labelling with BrdUrd. We have shown that this method may be useful in preclinical studies, allowing estimation of changes in GF during growth of murine tumours. Experiments with human Jurkat cells suggest that the proposed method might also prove suitable for measurement of cell kinetics in human tumours. Development of suitable software enabling more objective interpretation of the DNA profile in this method would be desirable.

Dataset Information

A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms.

Publications

A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms.

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