Floptics: A Novel Automated Gating Technique for Flow Cytometry Data

Floptics: A Novel Automated Gating Technique for Flow Cytometry Data

Wiwat Sriphum, Gary B. Wills, Nicolas G. Green
Copyright: © 2022 |Pages: 21
DOI: 10.4018/IJOCI.301561
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Abstract

Flow cytometry (FCM) determines the characteristics of individual biological cells using optical and fluorescence measurements. It is a widely used standard method for analysing blood samples in medical diagnostics, through identifying and quantifying the different types of cells in the samples. The multidimensional dataset obtained from FCM is large and complex, so it is difficult and time-consuming to analyse manually. The main process of differentiation and therefore labelling of the different cell populations in the data is referred to as Gating. This is the first step of FCM and is highly subjective, an issue that significant research has focussed on reducing. Existing automated gating techniques are time-consuming or retain subjectivity by requiring many user-defined parameters. This paper presents FLOPTICS: a novel automated gating technique that is a combination of density-based and grid-based clustering algorithms. FLOPTICS has an ability to classify and label cell populations in FCM data faster and with fewer user-defined parameters than many state-of-the-art techniques.
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Introduction

Early detection of most categories of disease increases the likelihood of successful treatment. In 2015, more than 360,000 cancer cases were diagnosed in the UK, representing some 183,000 males and 177,000 females (Cancer Research UK, 2015). In addition to cancers such as leukaemia, serious infections, such as sepsis, HIV and other viruses, require significant investment in both medical instruments and expertise, in order to achieve successful detection and then treatment.

One industry standard diagnostic process for both serious conditions and day-to-day general practise uses Flow Cytometry (FCM) to classify and count blood cells in medical patient samples. FCM is a powerful technology used for measuring the characteristics of different cells in blood samples such as size, complexity, and granularity. The measurements are then used to classify and label the different populations of cells, to allow a clinician to analyse and quantify the distributions of the components of the blood sample. Although this technology provides high quality results, the specialist staff working with this technology and performing analysis must have advanced training and develop considerable experience. In addition, the standard technique produces significantly more data than is or can be readily used by the standard diagnostic procedure in the time available. The development of technology which can assist in data recognition, analysis, and retention in support of the expert operator would have a significant impact in many areas of healthcare. Faster real-time assistive analysis has an industry changing potential in early detection of many conditions and successful treatment (Mclean, Bhonda, & Lewis, 2012; Migdady et al., 2020). In addition, more complete data analysis of the full data set may provide additional insight.

Counting Blood Cells

Population analysis of blood is a standard diagnostic process in general healthcare, referred to as the Full Blood Count and extended to further differential analysis such as the White Blood Cell Differential Count. This is the most common general practise diagnostic test on blood and is performed using FCM. It has been widely applied in medical settings, especially in haematology and immunology, and in clinical environments to diagnose and monitor treatments, such as leukaemia and stem cell transplantation monitoring (Jahan-Tigh, Ryan, Obermoser, & Schwarzenberger, 2012). This technique was developed over 40 years ago but was limited initially as the cytometer was large, difficult to maintain and expensive. As with many modern pieces of equipment (Robinson, Rajwa, Patsekin, & Davisson, 2012), FCM is now more accurate, cheaper, and easier to use, hence its wide application in clinical research, particularly haematology and immunology.

The primary use of flow cytometry in this process is to determine cell type and count the population of each type: the proportional count of different blood cells provides key information on the current health of a patient. This has become a standard blood analysis as a provision of data to doctors in general practise and hospitals.

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