Overview of clinical flow cytometry data analysis: recent advances and future challenges

Highlights

• Polychromatic flow cytometry generates increasingly complex n-dimensional data sets.

• New tools are being created for objective flow cytometry data analysis/interpretation.

• Full automation of data analysis remains a challenge.

Major technological advances in flow cytometry (FC), both for instrumentation and reagents, have emerged over the past few decades. These advances facilitate simultaneous evaluation of more parameters in single cells analyzed at higher speed. Consequently, larger and more complex data files that contain information about tens of parameters for millions of cells are generated. This increasing complexity has challenged pre-existing data analysis tools and promoted the development of new algorithms and tools for data analysis and visualization. Here, we review the currently available (conventional and newly developed) data analysis and visualization strategies that aim for easier, more objective, and robust interpretation of FC data both in biomedical research and clinical diagnostic laboratories.

Introduction

Together with the microscope, flow cytometers have become invaluable instruments for cell analysis both in research and clinical laboratories, particularly for the evaluation of normal and malignant hematopoietic and lymphoid cells [1]. Since the 1980s, FC has progressively extended from basic research to clinical diagnostic laboratories. Development of the hybridoma technology and massive production of monoclonal antibodies, together with many well-characterized, high-quality reagents and a progressively broad variety of compatible fluorochromes [1], have been crucial in the translation of FC to clinical diagnostic laboratories. In the past two decades, multiparameter FC immunophenotyping of normal, reactive, and malignant cells has been augmented by: (i) a larger number of parameters that can be simultaneously assessed in individual cells [2], and (ii) greater analysis speed of digital cytometers [3] that interrogate tens of thousands of cells per second. However, the exponentially growing, complex information about individual cells (and their products) in a sample, has required changes and developments in both data analysis, representation and visualization tools [4].

For decades, conventional FC data analysis has identified unique cell populations within a sample and characterized them by defining a number of regions (e.g., ‘gates’) in single and bidimensional plots. Such gating strategies are typically set for the selection of the subpopulations of interest by an experienced operator [5]. However, due to the greater multiparameter capabilities of FCs, strategies to extract maximum information from an n-dimensional space and to provide clear, simple and intuitive graphical representation of data, have become a major challenge [4]. In fact, the real challenge is to build FC data mining tools that extract relevant information in an objective, precise, reproducible, and comprehensive way. Moreover, it should be available to users through intuitive graphical representations and user-friendly interpretation-guided tools.

FC data analysis can be roughly divided into three steps: (i) generating FC data (Box 1); (ii) data storage; and (iii) mining stored information. In this paper we review the most relevant developments achieved in FC data analysis, and provide a perspective on current needs and potential future developments in the field.