The global burden of infectious disease, even before Covid-19 was staggering, with HIV/AIDS accounting for more than 4.5 million deaths per year. As we are all now fully aware, new microbial pathogens are emerging at an alarming rate. From 1940 to 2004, more than 335 new infectious diseases were described. Their emergence is believed to be driven by a combination of socio-economic, ecological, and environmental factors, fueled by poorly allocated global resources. As areas of high population densities, the adoption of poorly planned agricultural practices and antibiotic drug resistance continue to be established, the problem is only going to worsen. We, therefore, need access to robust and sensitive tools for infectious disease diagnosis, and more comprehensive solutions to characterize the process of infection and disease epidemiology.
An Introduction to Flow Cytometry Gating Strategy
Flow Cytometry data analysis is built upon the principle of gating. Hierarchical gating involves drawing a series of outlines or ‘gates’ onto a flow cytometry plot, in order to select a population of cells with common phenotypic characteristics. This typically uses forward scatter versus side scatter plots and the expression of one or more biomarkers to identify and select specific cell populations, which can be further investigated and quantified. Although it can be a complicated process and involve multiple gates or regions of interest, the process of flow cytometry gating itself is a simple selection mechanism for identifying cells of interest.
Computational Cytometry: Data Analysis in the Era of Quantitative Data Science
Recent technical advances in flow cytometry instrumentation and associated fluorophore development has dramatically increased the total number of parameters that can be measured at the single-cell level, with over 35 for traditional fluorescent-based flow cytometry, to well over 50 for mass cytometry. These recent advances allow scientists to routinely measure an increasing number of parameters per cell, generating massive high-dimensional data sets that require sophisticated data analysis methods . The complexity of these data sets poses significant challenges for analysis, which historically has relied on manual techniques. These manual analysis methods have numerous shortcomings, the biggest being amount of time and skill that is needed along with bias introduction by the researcher. In addition, manual gating is not easily scalable to meet the demand of both larger sample numbers with the increased number of measured parameters. We have now entered into the realm of “Big Data” in the field of immunology and thus a transformation in how flow cytometry data is being analyzed. Computational flow cytometry is allowing for new biological knowledge to be revealed from high-dimensional single-cell data sets. These new approaches in visualization and understanding of these larger data sets in a more automated and unbiased way have revolutionized personalized immune-therapy and helped to improve diagnostic capability.
There is no question that the discovery of vaccines spearheaded the path of modern medicine and in so doing, eradicated at least two diseases, smallpox, and rinderpest from the global population. Today’s modern vaccines are being developed not only to tackle infectious diseases but also for the treatment and prevention of autoimmune diseases and cancers. Whereas vaccines for infectious diseases and cancer are designed to provoke a specific Th 1-driven immune response to target and reject the tumor or pathogen, vaccines driving Th 2 responses appear to be the best at targeting autoimmune diseases. Understanding the driving factors behind these underlying responses is central to the development of safe and effective vaccines, and flow cytometry provides unprecedented clarity on how the immune system responds to different vaccine strategies.
What are cytokines?
Cytokines are low molecular weight proteins produced by various types of immune cells in response to different forms of stimuli. These chemical signals serve as messengers for both adaptive and innate immunity, enabling immune cells to communicate with each other over short distances (Figure 1). Essentially, they act as intracellular messengers with complex regulatory influences on the behavior of various target cells regulating key functions in the body including inflammation and immunity.