Enumeration of Functional T-Cell Subsets by Fluorescence-Immunospot Defines Signatures of Pathogen Burden in Tuberculosis

IFN-γ and IL-2 cytokine-profiles define three functional T-cell subsets which may correlate with pathogen load in chronic intracellular infections. We therefore investigated the feasibility of the immunospot platform to rapidly enumerate T-cell subsets by single-cell IFN-γ/IL-2 cytokine-profiling and establish whether immunospot-based T-cell signatures distinguish different clinical stages of human tuberculosis infection.We used fluorophore-labelled anti-IFN-γ and anti-IL-2 antibodies with digital overlay of spatially-mapped colour-filtered images to enumerate dual and single cytokine-secreting M. tuberculosis antigen-specific T-cells in tuberculosis patients and in latent tuberculosis infection (LTBI). We validated results against established measures of cytokine-secreting T-cells.Fluorescence-immunospot correlated closely with single-cytokine enzyme-linked-immunospot for IFN-γ-secreting T-cells and IL-2-secreting T-cells and flow-cytometry-based detection of dual IFN-γ/IL-2-secreting T-cells. The untreated tuberculosis signature was dominated by IFN-γ-only-secreting T-cells which shifted consistently in longitudinally-followed patients during treatment to a signature dominated by dual IFN-γ/IL-2-secreting T-cells in treated patients. The LTBI signature differed from active tuberculosis, with higher proportions of IL-2-only and IFN-γ/IL-2-secreting T-cells and lower proportions of IFN-γ-only-secreting T-cells.Fluorescence-immunospot is a quantitative, accurate measure of functional T-cell subsets; identification of cytokine-signatures of pathogen burden, distinct clinical stages of M. tuberculosis infection and long-term immune containment suggests application for treatment monitoring and vaccine evaluation

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry

Guidelines for the use of flow cytometry and cell sorting in immunological studies.

The marriage between immunology and cytometry is one of the most stable and productive in the recent history of science. A rapid search in PubMed shows that, as of July 2017, using “flow cytometry immunology” as a search term yields more than 68 000 articles, the first of which, interestingly, is not about lymphocytes. It might be stated that, after a short engagement, the exchange of the wedding rings between immunology and cytometry officially occurred when the idea to link fluorochromes to monoclonal antibodies came about. After this, recognizing different types of cells became relatively easy and feasible not only by using a simple fluorescence microscope, but also by a complex and sometimes esoteric instrument, the flow cytometer that is able to count hundreds of cells in a single second, and can provide repetitive results in a tireless manner. Given this, the possibility to analyse immune phenotypes in a variety of clinical conditions has changed the use of the flow cytometer, which was incidentally invented in the late 1960s to measure cellular DNA by using intercalating dyes, such as ethidium bromide. The epidemics of HIV/AIDS in the 1980s then gave a dramatic impulse to the technology of counting specific cells, since it became clear that the quantification of the number of peripheral blood CD4+ T cells was crucial to follow the course of the infection, and eventually for monitoring the therapy. As a consequence, the development of flow cytometers that had to be easy-to-use in all clinical laboratories helped to widely disseminate this technology. Nowadays, it is rare to find an immunological paper or read a conference abstract in which the authors did not use flow cytometry as the main tool to dissect the immune system and identify its fine and complex functions. Of note, recent developments have created the sophisticated technology of mass cytometry, which is able to simultaneously identify dozens of molecules at the single cell level and allows us to better understand the complexity and beauty of the immune system.</p

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion