Dataset Information

Non-linear multidimensional flow cytometry analyses delineate NK cell phenotypes in normal and HIV-infected chimpanzees.

ABSTRACT: Natural killer (NK) cells are primary immune effector cells with both innate and potentially adaptive functions against viral infections, but commonly become exhausted or dysfunctional during chronic diseases such as human immunodeficiency virus (HIV). Chimpanzees are the closest genetic relatives of humans and have been previously used in immunology, behavior and disease models. Due to their similarities to humans, a better understanding of chimpanzee immunology, particularly innate immune cells, can lend insight into the evolution of human immunology, as well as response to disease. However, the phenotype of NK cells has been poorly defined. In order to define NK cell phenotypes, we unbiasedly quantified NK cell markers among mononuclear cells in both naive and HIV-infected chimpanzees by flow cytometry. We identified NKG2D and NKp46 as the most dominant stable NK cells markers using multidimensional data reduction analyses. Other traditional NK cell markers such as CD8α, CD16 and perforin fluctuated during infection, while some such as CD56, NKG2A and NKp30 were generally unaltered by HIV infection, but did not delineate the full NK cell repertoire. Taken together, these data indicate that phenotypic dysregulation may not be pronounced during HIV infection of chimpanzees, but traditional NK cell phenotyping used for both humans and other non-human primate species may need to be revised to accurately identify chimpanzee NK cells.

SUBMITTER: Manickam C

PROVIDER: S-EPMC6400041 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Advancement in flow cytometry reagents and instrumentation has allowed for simultaneous analysis of large numbers of lineage/functional immune cell markers. Highly complex datasets generated by polychromatic flow cytometry require proper analytical software to answer investigators' questions. A problem among many investigators and flow cytometry Shared Resource Laboratories (SRLs), including our own, is a lack of access to a flow cytometry-knowledgeable bioinformatics team, making it difficult to learn and choose appropriate analysis tool(s). Here, we comparatively assess various multidimensional flow cytometry software packages for their ability to answer a specific biologic question and provide graphical representation output suitable for publication, as well as their ease of use and cost. We assessed polyfunctional potential of TCR-transduced T cells, serving as a model evaluation, using multidimensional flow cytometry to analyze 6 intracellular cytokines and degranulation on a per-cell basis. Analysis of 7 parameters resulted in 128 possible combinations of positivity/negativity, far too complex for basic flow cytometry software to analyze fully. Various software packages were used, analysis methods used in each described, and representative output displayed. Of the tools investigated, automated classification of cellular expression by nonlinear stochastic embedding (ACCENSE) and coupled analysis in Pestle/simplified presentation of incredibly complex evaluations (SPICE) provided the most user-friendly manipulations and readable output, evaluating effects of altered antigen-specific stimulation on T cell polyfunctionality. This detailed approach may serve as a model for other investigators/SRLs in selecting the most appropriate software to analyze complex flow cytometry datasets. Further development and awareness of available tools will help guide proper data analysis to answer difficult biologic questions arising from incredibly complex datasets.

Project description:Bacteria modify their morphology in response to various factors including growth stage, nutrient availability, predation, motility and long-term survival strategies. Morphological changes may also be associated with specific physiological phenotypes such as the formation of dormant or persister cells in a "viable but non-culturable" (VBNC) state which frequently display different shapes and size compared to their active counterparts. Such dormancy phenotypes can display various degrees of tolerance to antibiotics and therefore a detailed understanding of these phenotypes is crucial for combatting chronic infections and associated diseases. Cell shape and size are therefore more than simple phenotypic characteristics; they are important physiological properties for understanding bacterial life-strategies and pathologies. However, quantitative studies on the changes to cell morphologies during bacterial growth, persister cell formation and the VBNC state are few and severely constrained by current limitations in the most used investigative techniques of flow cytometry (FC) and light or electron microscopy. In this study, we applied high-throughput Imaging Flow Cytometry (IFC) to characterise and quantify, at single-cell level and over time, the phenotypic heterogeneity and morphological changes in cultured populations of four bacterial species, Bacillus subtilis, Lactiplantibacillus plantarum, Pediococcus acidilactici and Escherichia coli. Morphologies in relation to growth stage and stress responses, cell integrity and metabolic activity were analysed. Additionally, we were able to identify and morphologically classify dormant cell phenotypes such as VBNC cells and monitor the resuscitation of persister cells in Escherichia coli following antibiotic treatment. We therefore demonstrate that IFC, with its high-throughput data collection and image capture capabilities, provides a platform by which a detailed understanding of changes in bacterial phenotypes and their physiological implications may be accurately monitored and quantified, leading to a better understanding of the role of phenotypic heterogeneity in the dynamic microbiome.

Dataset Information

Non-linear multidimensional flow cytometry analyses delineate NK cell phenotypes in normal and HIV-infected chimpanzees.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets