Intracellular cytokine labeling combined with high-parametric flow cytometry offers substantial promise in elucidating the nuanced effector functions of cells. However, the establishment of complex multicolor panels is often laborious and the importance of validation processes may be underestimated in research practice. This raises the risk of prematurely translating multicolor panels into in vivo studies. Alternatively, researchers may resort to animal disease models to procure cytokine-producing cells. Both scenarios raise ethical concerns as they entail the potential for unnecessary animal suffering without yielding novel insights into immunobiology. Here, we perform multicolor panel optimization and validation without the need for stressful animal testing. We designed two spectral flow cytometry panels for cytokine expression analyses across mouse immune and joint cells. Animal testing was replaced by stimulated co-cultures of T cells, splenocytes, and fibroblast-like synoviocytes. These cultures were used for multicolor labeling experiments. Our method proved suitable for validating the two cytometry panels, as it provided a complex cellular environment in which a variety of cytokine-producing populations were identified. In summary, we here present a blueprint for the quality control of single-cell cytokine assays by cell culture and further introduce multicolor panels that can be employed for studies on inflammatory or infectious diseases.
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http://dx.doi.org/10.1002/eji.202451193 | DOI Listing |
Elife
March 2025
Machine Learning Core, National Institute of Mental Health, Bethesda, United States.
Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce the detection of effects because they condense signals into summary measures, and discard trial-level information by averaging . We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at , and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences.
View Article and Find Full Text PDFSci Adv
March 2025
Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, 17 Liverpool Street, Hobart, Tasmania 7000, Australia.
Understanding plastics' harmful impacts on wildlife would benefit from the application of hypothesis agnostic testing commonly used in medical research to detect declines in population health. Adopting a data-driven, proteomic approach, we assessed changes in 745 proteins in a free-living nonmodel organism with differing levels of plastic exposure. Seabird chicks heavily affected by plastic ingestion demonstrated a range of negative health consequences: Intracellular components that should not be found in the blood were frequently detected, indicative of cell lysis.
View Article and Find Full Text PDFPLoS One
March 2025
Department of Electrical Engineering, Faculty of Engineering, Suez Canal University, Ismailia, Egypt.
In distribution grids, excessive energy losses not only increase operational costs but also contribute to a larger environmental footprint due to inefficient resource utilization. Ensuring optimal placement of photovoltaic (PV) energy systems is crucial for achieving maximum efficiency and reliability in power distribution networks. This research introduces the Pelican Optimizer (PO) algorithm to optimally integrate solar PV systems to radial electrical distribution grids.
View Article and Find Full Text PDFPLoS One
March 2025
Instituto de Investigación en Zoonosis (CIZ), Universidad Central del Ecuador, Quito, Ecuador.
Anaplasmosis is a tick-borne disease (TBDs) caused by Anaplasma spp. In areas where TBDs are endemic, it is crucial to consider the animals' immunological status in relation to these diseases. The true prevalence of bovine anaplasmosis, the percentage of animals with protective antibodies against this TBD, and the diagnostic characteristics of three tests (multiplex polymerase chain reaction (mPCR), competitive-inhibition enzyme-linked immunosorbent assay (cELISA), and blood smear (BS)) were estimated using a Bayesian approach.
View Article and Find Full Text PDFJ Chem Ecol
March 2025
Department of Agricultural, Food and Forest Sciences, University of Palermo, Palermo, 90128, Italy.
Floral nectar is a sugar-rich resource which is ubiquitously inhabited by a wide array of microorganisms. Fermentation by nectar-inhabiting microbes can alter several nectar traits, including nectar scent, via changes in the blend of volatile organic compounds (VOCs). Although there is growing evidence on how yeasts and bacteria influence the foraging behavior of flower-visiting insects, the potential role of other microbial taxa that can colonize nectar has been largely neglected.
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