A novel data fusion method for the effective analysis of multiple panels of flow cytometry data

Natural killer cell profiles in recurrent pregnancy loss: Increased expression and positive associations with TACTILE and LILRB1

PROBLEM

NK cells are important for healthy pregnancy and aberrant phenotypes or effector functions have been associated with RPL. We compared expression of a broad panel of NK cell receptors, including immune checkpoint receptors, and investigated their clinical association with RPL as this might improve patient stratification and prediction of RPL.

METHOD OF STUDY

Peripheral blood mononuclear cells were isolated from 52 women with RPL and from 2 women with an uncomplicated pregnancy for flowcytometric analysis and plasma was used to determine anti-CMV IgG antibodies.

RESULTS

Between RPL and controls, we observed no difference in frequencies of T-, NKT or NK cells, in CD56dimCD16+ or CD56brightCD16- NK cell subsets or in the expression of KIRs, NKG2A, NKG2C, NKG2D, NKp30, NKp44, NKp46 or DNAM1. NK cells from women with RPL had a higher expression of LILRB1 and TACTILE and this was associated with the number of losses. The immune checkpoint receptors PD1, TIM3 and LAG3 were not expressed on peripheral blood NK cells. In RPL patients, there was a large variation in NKG2C expression and higher levels could be explained by CMV seropositivity.

CONCLUSION

Our study identified LILRB1 and TACTILE as NK cell receptors associated with RPL. Moreover, we provide first support for the potential role of CMV in RPL via its impact on the NK cell compartment. Thereby our study could guide future studies to confirm the clinical association of LILRB1, TACTILE and NKG2C with RPL in a larger cohort and to explore their functional relevance in reproductive success.

Impacts of microplastics on immunity

Most disposable plastic products are degraded slowly in the natural environment and continually turned to microplastics (MPs) and nanoplastics (NPs), posing additional environmental hazards. The toxicological assessment of MPs for marine organisms and mammals has been reported. Thus, there is an urgent need to be aware of the harm of MPs to the human immune system and more studies about immunological assessments. This review focuses on how MPs are produced and how they may interact with the environment and our body, particularly their immune responses and immunotoxicity. MPs can be taken up by cells, thus disrupting the intracellular signaling pathways, altering the immune homeostasis and finally causing damage to tissues and organs. The generation of reactive oxygen species is the mainly toxicological mechanisms after MP exposure, which may further induce the production of danger-associated molecular patterns (DAMPs) and associate with the processes of toll-like receptors (TLRs) disruption, cytokine production, and inflammatory responses in immune cells. MPs effectively interact with cell membranes or intracellular proteins to form a protein-corona, and combine with external pollutants, chemicals, and pathogens to induce greater toxicity and strong adverse effects. A comprehensive research on the immunotoxicity effects and mechanisms of MPs, including various chemical compositions, shapes, sizes, combined exposure and concentrations, is worth to be studied. Therefore, it is urgently needed to further elucidate the immunological hazards and risks of humans that exposed to MPs.

A novel approach for discovering correlations between elemental and molecular composition using laser-based spectroscopic techniques

LIBS and Raman spectra of marine zooplankton processed together to study trends in anomalous lithium enrichment.

High-throughput single cell data analysis - A tutorial

White blood cells protect the body against disease but may also cause chronic inflammation, auto-immune diseases or leukemia. There are many different white blood cell types whose identity and function can be studied by measuring their protein expression. Therefore, high-throughput analytical instruments were developed to measure multiple proteins on millions of single cells. The information-rich biochemistry information may only be fully extracted using multivariate statistics. Here we show an overview of the most essential steps for multivariate data analysis of single cell data. We used white blood cells (immunology) as a case study, but a similar approach may be used in environment or biotech research. The first step is analyzing the study design and subsequently formulating a research question. The three main designs are immunophenotyping (finding different cell types), cell activation and rare cell discovery. When preparing the data it is essential to consider the design and focus on the cell type of interest by removing all unwanted events. After pre-processing, the ten-thousands to millions of single cells per sample need to be converted into a cellular distribution. For immunophenotyping a clustering method such as Self-Organizing Maps is useful and for cell activation a model that describes the covariance such as Principal Component Analysis is useful. In rare cell discovery it is useful to first model all common cells and remove them to find the rare cells. Finally discriminant analysis based on the cellular distribution may highlight which cell (sub)types are different between groups.

High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning

Modern immunologic research increasingly requires high-dimensional analyses to understand the complex milieu of cell types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the coexpression patterns of hundreds of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and the identification of previously unknown cellular heterogeneity in the lungs of melanoma metastasis–bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost, and accessible solution to single-cell proteomics in complex tissues.