Computational analysis of high-throughput flow cytometry data

Abstract

Introduction: Flow cytometry has been around for over 40 years, but only recently has the opportunity arisen to move into the high-throughput domain. The technology is now available and is highly competitive with imaging tools under the right conditions. Flow cytometry has, however, been a technology that has focused on its unique ability to study single cells and appropriate analytical tools are readily available to handle this traditional role of the technology.

Areas covered: Expansion of flow cytometry to a high-throughput (HT) and high-content technology requires both advances in hardware and analytical tools. The historical perspective of flow cytometry operation as well as how the field has changed and what the key changes have been discussed. The authors provide a background and compelling arguments for moving toward HT flow, where there are many innovative opportunities. With alternative approaches now available for flow cytometry, there will be a considerable number of new applications. These opportunities show strong capability for drug screening and functional studies with cells in suspension.

Expert opinion: There is no doubt that HT flow is a rich technology awaiting acceptance by the pharmaceutical community. It can provide a powerful phenotypic analytical toolset that has the capacity to change many current approaches to HT screening. The previous restrictions on the technology, based on its reduced capacity for sample throughput, are no longer a major issue. Overcoming this barrier has transformed a mature technology into one that can focus on systems biology questions not previously considered possible.

Figure 1

The major advantage of the cytomics (i.e., single-cell analysis) approach is the ability to interrogate single cells and to separate functionally and phenotypically distinctive populations of cells during the process of statistical analysis of the entire biological system. Every defined population can be studied separately or in the context of functional relations to other populations. Reproduced from a poster presented at the NIH Common Fund Single Cell Analysis Workshop, April 17 – 18, 2012 [115] by the University Cytometry Laboratories with their permission.

Figure 2

Traditional flow cytometry analyzes one sample at a time. Due to the interactivity required for visualization and gating, this approach is relatively slow and cumbersome. In the provided example four gating steps were defined. The first and the third gates are polygonal and are defined in two-dimensional spaces formed by FC-measured variables. The steps numbered 2 and 4 use only one variable and simple thresholding to separate cells demonstrating high intensity of fluorescenceinspecificbands.

Figure 3

The multifactorial experimental design demonstrated in this figure, based on a study from Bodenmiller (personal communication), allows simultaneous study of responses demonstrated by nine populations of cells to a number of drugs in the presence of 14 activators. Over 30 functional parameters based on molecular markers of phosphorylation were measured for every one of the 14 cell types in the system.

Figure 4

This figure demonstrates the SPADE algorithm as applied to hematopoietic cells. This technology packages data into regions of similarity so that it is possible to see the strength and range of interactions within a complex system. SPADE uses a color- and shape-coded process to allow the investigator to get an overview of a very complex system. Reproduced from [95] with permission of the American Association for the Advancement of Science.

Figure 5

Screenshot of PlateAnalyzer, an interactive analysis system for multifactorial FC data. The GUI uses a pipeline metaphor to enable even inexperienced users access to sophisticated embedded visual-programming tools. Reproduced from [12] with permission of International Drug Discovery and Russell Publishing.