Flow cytometry

From LipidomicsWiki

Flow Cytometry and Cellular Imaging
Implementation of cellular assays for the analysis of new biomarkers in the clinical
laboratory
In the upcoming era of clinical Cellomics biomarker analysis for diagnosis, patient stratification and therapy monitoring will combine well-established analysis tolls with
techniques that transfer research methods into the clinical laboratory. In this regard flow cytometry is already established as a valuable method to investigate cell surface as well as intracellular expression, activation and colocalisation of proteins, also in regard of signaling events (phosphorylation, calcium homeostasis). Thus, it is likely to play a major role in the future as a complementary analysis tool to well-established analysis techniques (Fig. 101).

Unfortunately, flow cytometric analysis still requires expensive instrumentation and skilled and experienced operators to obtain optimal results and implement complex assays and methodologies. Therefore, integration of basic flow cytometric techniques into todays hematology analyzers would provide the opportunity to introduce new diagnostic parameters into clinical routine. Moreover, data analysis needs academic expertise and is time consuming. Thus, there is an urgent need for improved, IT-based data analysis to extract actionable health information. Platform integration and improved IT-solutions would not only complement current diagnostic procedures but would also offer the opportunity for the rapidexecution of additional, more complex and expensive tests depending on the results obtained by routine screening tests, such as red and white blood cell differentials.
Currently, several manufacturers of hematology counters develop more complex analyzers integrating additional modules, e. g. for fluorescence analysis (Fig. 102). Identification of abnormal samples (“flagging”) is available on all standard hematology systems such as shape changes in red blood cells or platelets with enlarged cellular volume. This offers the possibility to subsequent analysis of flagged samples in a flow cytometric approach to quantify and further delineate the cellular abnormalities (red blood cell dysmorphology could relate e.g. to membrane skeleton defects, large platelet volume to young or old platelets). Finally, future diagnostic strategies based on this approach have to be evaluated in large patient groups and clinical studies.

These diagnostic strategies can be illustrated by the following examples.

Phagocyte analysis (monocytes, neutrophils)
There is a clear need for improved indicators of infection or sytemic inflammation to increase the sensitivity and specificity of both diagnosis and therapeutic monitoring. Furthermore, cell subset analysis for estimation of a dynamic flux through different cell compartments (e.g. monocytes, granulocytes) during cell activation is necessary. Flow cytometric parameters of neutrophil activity such as intracellular pH, phagocytosis, oxidative burst and protease activity are well known to differ between samples from sepsis and non-sepsis patients. The multifunctional analysis of neutrophils by flow cytometry seems of interest for early clinical intervention in pre-septic and pre-shock patients. One effect of inflammatory cytokines on the innate immune response also is the rapid up-regulation of CD64 expression on the neutrophil membrane. Current data indicate that neutrophil CD64 expression as determined by quantitative flow cytometry is an improved diagnostic indicator of infection/sepsis relative to current laboratory such as neutrophil counts, band counts, myeloid immaturity fraction, or flagging on automated hematology analyzers. Other trials have also shown that monocytic
HLA-DR is a good marker for monitoring the severity of temporary immunodepression after trauma, major surgery, or sepsis. Furthermore, flow cytometry based detection of fluorescence energy transfer (FRET) would allow for the identification of activation clusters like the CD14-cluster that builds upon monocyte activation, its composition depending on the stimulating ligand. The recruited proteins cluster in raft microdomains and initiate src kinase signaling.

Red blood cell analysis
Red blood cell morphology is known to be crucial for its function as oxygen donator. Thus, erythrocyte dysfunction plays an important role in several diseases with vascular
background, like hereditary spherocytosis, sepsis and atherosclerosis. Specific changes in erythrocyte morphology like anisocytosis and echinocytosis accompany altered cell function. These shape changes can readily be measured on routine haematology analysers. In the cellomics approach the subsequent analysis of more specific parameters on a second platform or on the same analyser would render additional information for disease identification. For example, flow cytometric analysis of eosin-5-maleimide that was shown to bind to band 3 and Rh-related proteins can serve as a test to detect hereditary spherocytosis. Additionally, the measurement of red blood cell-derived microparticles can provide valuable information for thalassemia diagnostics.

Platelet analysis
Modern haematology analysers are able to detect parameters that are related to platelet morphology like the mean platelet volume (MPV). The secondary analysis could for example consist of residual RNA measurement in flow cytometry to classify young (high RNA content) and old platelets (low RNA content). Additionally, the “ageing” of platelets could also be assessed by measuring the loss of sialo-modifications of given proteins for which specific antibodies are available or by surface binding of Annexin V. These second line analysis tools could help in identifying “old”, reactive platelets. The importance of this platelet fraction in vascular diseases like atherosclerosis and the development of acute cardial or cerebral events has been elucidated during the last years. Red blood cell and platelet analysis are examples that the integration of cell-based, innovative techniques in well-established analysis set-ups are able bring the power of laboratory testing to a new level in early disease
identification.

Microparticle analysis as an alternative to cellular analysis
Cell-derived microparticles have been shown to be relevant in a number of diseases like thrombosis, cardiovascular disease, anti-phospholipidsyndrome, and systemic inflammation (SIRS, sepsis). Microparticles have been shown to be a valuable tool in the field of red blood cell dysmorphology and dysfunction. Furthermore, it is likely that microparticle release is part of the ageing process of blood cells like erythrocytes or platelets. It may coincide with other cell alterations like shape change or loss of surface protein modifications. In this sense the microparticle release is thought to be an innovative marker for chronic vascular diseases like
atherosclerosis or other ageing disorders. Microparticles are defined as shedded membranous fragments or vesicles with a diameter of less than 1 μm bearing on their surface markers of the parent cell. They are surrounded by a phospholipid bilayer that is mainly composed of phosphatidylcholine, sphingomyelin and phosphatidylethanolamin. Microparticles differ from their parental cells in regard of the lipid composition and distribution between the two membrane leaflets. The asymmetrical phospholipid distribution with anionic phospholipids being confined to the inner leaflet usually changes during microparticle formation. Activation as well as apoptosis lead to calciumdependant swelling, budding and microparticle release. Finally, both cell activation and apoptosis result in disruption of the membrane skeleton structure that is necessary for particle release. The principal sites of microparticle release seem to be cell protrusions that
in regard to the cell type resemble microvilli, pseudopodia, filopodia or proteopodia.Microparticles are derived from a number of different cell types. In addition to its wellestablished role of platelet-derived microparticles in hemostasis an important function in a
variety of additional blood cells (e.g. red blood cells, monocytes, and lymphocytes) and inother cell types like endothelial and stromal progenitor cells has been proposed. Red blood cells have been known for a long time to shed membrane microparticles under stimulation. It could be shown that as for other microparticles this process depends on a rise in intracellular calcium. Additionally, they found that the microparticle release coincides with the formation of echinocytes which is characterised by loss of the natural discoid shape to gain a more spherical form with regularly distributed cell membrane protrusions. The shape change as well as the microparticle release may resemble the in vivo events during cell ageing. Circulating microparticles of leukocyte origin modulate cellular interactions through the upregulation of cytokines and cytoadhesins in endothelial cells and monocytes. Monocytes have been shown to transfer tissue factor to platelets via the microparticle pathway. Tissue factor seems to be released from pseudopodia. Membrane microparticles from antigenpresenting cells (APCs) were shown to activate T-cells as an example for their role in the immune system. In addition to its role in stem cell engraftment mediated by platelet-derived microparticles, those originating from bone marrow stromal cells or stem cells could serve as biologic vectors providing neighbouring cells with lipids and other essential cell components. Antigenic markers measured by flow cytometry are mainly used to classify and further subdivide membrane microparticles. Alternative approaches include ELISA and solid phase capture assays. The advantage of flow cytometry lies in its capacity to distinguish different microparticle populations and subpopulations according to the expression of surface and intracellular antigens in a multi-parameter approach. The sensitivity and specificity of flow cytometry could therefore to be used to improve the analysis of blood and other cell types. In this sense it should be combined with the well-established technique of conventional analysers as outlined above.

Clinical systems biology of blood cells: Analysis of the monocyte compartment
Macrophages play a central role in the initiation and progression of atherosclerotic lesions. Peripheral blood monocytes extravasate into different tissues including the vessel wall triggered by several activation mechanisms involving innate, humoral, classical and alternative pathways. Thus, multiple mediators affect the monocyte during its residence time in the blood circulation and prime its differentiation into macrophages with pro- or antiinflammatory properties. As depicted in figure 103, in the Metabolic Syndrome Complex including atherosclerosis, several factors originating from different tissues affect monocytes and drive their transformation towards tissue foam cell macrophages or osteoclasts in calcifying lesions. Atherosclerosis is clearly a multiorgan disorder and peripheral blood monocytes can be regarded as sentinels and cellular integrators/reporters of pathophysiological changes. The EU Specific Targeted Research Project (STREP) “Genes and environment in atherosclerosis: Designing the E-monocyte” (ATHEROSYSTEMS) focuses on the central role of monocytes and macrophages in the development of atherosclerotic lesions.
Peripheral blood monocytes extravasate into the vessel wall in response to chemotactic factors, which finally results in subendothelial foam cell formation or lesion calcification. Understanding monocyte priming and its transcriptional network response in relation to gene variation and environmental influence will allow identification of novel biomarkers. Along with new technological capabilities, finding and validation of specific therapeutic targets will enable large clinical trials on new medications as a pre-requisite for the development of the European health care system towards individualized medicine. The consortium aims not only to gain biologic and physiologic information but also to integrate these data into a holistic concept of the monocyte. Interacting and vulnerable pathways will be integrated into a
network we term the E-Monocyte, which represents the transition towards the potentials that systems biology will offer in the future. The functional genomics approach allows comparison of the patient’s monocyte response with that of the virtual control monocyte network to rapidly identify disease-relevant aberrations. The E-monocyte concept combines knowledgebased and experimental data-driven analysis rather than simply a collection of individual molecular results (see figure 104). The E-monocyte is a step towards systems biology that requires simultaneous investigation of multiple interacting components and use of quantitative high-throughput technologies. Computational biology and bioinformatics approaches are also required to handle and interprete the volumes of data necessary to understand complex biological systems. These prospects will help in successfully identifying biomarkers and provide actionable health information. The E-Monocyte represents an example and in the future will be extended to other cells of blood and tissue origin. Cellomics are a a prerequisite in this strategy as demonstrated in figure 105.

Cellular Imaging and Fluorescence Microscopy
The physiological status of a cell is influenced by and reflected in the set of proteins that is expressed at a given time point. However, simply knowing how much of a protein is expressed is not sufficient to understand its contribution to cell function. It is particularly important to know about its subcellular location because changes in protein subcellular distribution can have strong effects on the cell behaviour. Perhaps the most thoroughly studied example of this phenomenon is the changes in protein location associated with apoptosis. Cellular redistribution of proteins may also cause or result from a disease state, as illustrated by the suspected involvement of the Wnt- and β-catenin pathway in number of diseases. Accordingly, there is a need for the high-resolution, comprehensive analysis of the subcellular location of proteins. In this regard high-throughput methods for protein imaging and automated methods for image analysis are mandatory. The feasibility of an automated classification system for subcellular distribution patterns has been demonstrated during the last years. The pattern analysis has been refined to the point that all major subcellular patterns can be recognized in two- and three-dimensional images of single cultured cells with high accuracy. An important conclusion form this work is that automated classification
systems may perform better than visual examination in regard of standardised criteria. Although automated, these classification approaches still have the same limitation as the visual approach since: they can mainly recognize the major patterns that they have been trained for. An important alternative therefore is to use unsupervised machine learning approaches (cluster analysis) to group proteins by their high-resolution patterns. Taken together, the combination of large-scale protein tagging, high-resolution imaging and clustering by subcellular pattern is one way to enhance the capabilities of Cellomics. Four major imaging techniques with application examples are presented in figure 10. In addition to standardised, automated cellular imaging analysis it will be necessary in the future to screen several different tissue specimens from healthy controls and patients for abnormalities in protein expression or distribution. In this regard tissue microarrays have recently been proven to be a valuable tool. Tissue microarrays are composed of hundreds of sections to allow for the parallel investigation of different histological entities or subtypes. Each section is linked to a well-characterised patient chart that enables the direct correlation of distribution patterns or expression signals to e.g. disease state or tumor type. This approach has been found to be very useful in the analysis of protein expression patterns for example in breast carcinoma (see figure 106). Cellular imaging in addition to flow cytometry will also help in to identify and visualise distinct plasma membrane compartments, so called raft microdomains.