Flow Cytometric Identification and Enumeration of Monocyte Subsets in Bovine and Water Buffalo Peripheral Blood

Monocytes are innate immune system key players with pivotal roles during infection and inflammation. They migrate into tissues and differentiate into myeloid effect cells (macrophages, dendritic cells) which orchestrate inflammatory processes and are interfaces between the innate and adaptive immune responses. Their clinical relevance to health and disease of cattle (Bos taurus) and water buffalo (Bubalus bubalis), two of the most important livestock species, has been highlighted in physiologic (pregnancy) and pathologic (mastitis, metritis, and viral infections) conditions. The existence of three different monocyte subsets in cattle was established by flow cytometry (FC), as follows: classical (cM; CD14⁺⁺ CD16^-/low ), intermediate (intM; CD14^++/+ CD16⁺ ), and non-classical (ncM; CD14^-/low CD16⁺⁺ ) monocytes. FC applications for studying the immune system of cattle and water buffalo still have significant limitations. In this article, we describe some practical approaches to overcome these limitations and, in particular, allow the identification and enumeration of cM, intM, and ncM subpopulations in cattle and buffalo peripheral blood. Indeed, we propose the new procedure lyse/wash/no-centrifugation (L/W/NC) that can be combined with the FC absolute counting procedures and can overcome specific issues of the lyse/no-wash protocols (L/NW). Finally, for the first time, we demonstrated the existence of cM, intM, and ncM monocyte subsets also in the water buffalo, showing some interesting differences with cattle, such as the bubaline cM are mainly CD14^+/++ /CD16⁺ . These subtle differences may influence inflammatory disease regulation in, for example, mastitis and metritis. The upregulation of CD16 expression on cM may reveal different monocyte priming, leading to different functional features of macrophages/dendritic cells in tissues after infection. © 2023 Wiley Periodicals LLC. Basic Protocol: Absolute count of cM, intM, and ncM without compensation Alternate Protocol: Absolute count of cM, intM, and ncM for single laser platform Support Protocol 1: In-house monoclonal antibody labeling using a Pacific Blue^™ kit Support Protocol 2: In-house monoclonal antibody labeling using an Alexa Fluor^® 647 kit Support Protocol 3: Titration of fluorochrome-conjugated antibodies.

Reference values for T and B lymphocyte subpopulations in Turkish children and adults

Background/aim

Established reference values are critical for the interpretation of immunologic assessments. In particular, the proportion and absolute counts of T- and B- cell subpopulations are subject to change with age and ethnicity. We aimed to establish age-specific reference values for lymphocyte subsets using updated immunophenotyping panels.

Materials and methods

We studied a total of 297 healthy Turkish subjects aged 0 to 50 years, stratified into major age brackets in a cluster factor of 10 per age-group. The predetermined age intervals contained randomly allocated participants enrolled over a period of 6 months, who were homogenously distributed by sex. We analyzed a complete blood count test and simultaneously with detailed immunophenotyping enumerated the percent and absolute cell counts of lymphocyte subsets.

Results

The percentage and absolute counts of lymphocyte subsets show a marked surge across the age-span. T helper, T cytotoxic, and the natural killer cell numbers were increasing from birth until 6 months, followed by a gradual decrease thereafter. B cell numbers were rising until 2 years, followed by a gradual decrease for the upcoming years, accompanied by a steady expansion of unclass-switched- and class-switched- B cells.

Conclusion

We provide updated extensive reference intervals for lymphocyte subpopulations in Turkish people.

Current Applications of Absolute Bacterial Quantification in Microbiome Studies and Decision-Making Regarding Different Biological Questions

High throughput sequencing has emerged as one of the most important techniques for characterizing microbial dynamics and revealing bacteria and host interactions. However, data interpretation using this technique is mainly based on relative abundance and ignores total bacteria load. In certain cases, absolute abundance is more important than compositional relative data, and interpretation of microbiota data based solely on relative abundance can be misleading. The available approaches for absolute quantification are highly diverse and challenging, especially for quantification in differing biological situations, such as distinguishing between live and dead cells, quantification of specific taxa, enumeration of low biomass samples, large sample size feasibility, and the detection of various other cellular features. In this review, we first illustrate the importance of integrating absolute abundance into microbiome data interpretation. Second, we briefly discuss the most widely used cell-based and molecular-based bacterial load quantification methods, including fluorescence spectroscopy, flow cytometry, 16S qPCR, 16S qRT-PCR, ddPCR, and reference spike-in. Last, we present a specific decision-making scheme for absolute quantification methods based on different biological questions and some of the latest quantitative methods and procedure modifications.