Publications by authors named "P D Rybakowska"

Article Synopsis
  • - Clinical studies aim to understand disease mechanisms and identify biomarkers for disease activity, treatment responses, and outcome predictions.
  • - Mass cytometry (MC) is an advanced technology that analyzes hundreds of cells quickly, allowing for detailed immune monitoring and biomarker discovery.
  • - Proper experimental design is crucial in clinical research to address variations that can occur during sample processing and analysis, which this review will discuss in relation to MC studies.
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Article Synopsis
  • Researchers are using human induced pluripotent stem cell (hiPSC)-derived neural models to study the interactions between the Varicella-Zoster Virus (VZV) and the immune system in neurons.
  • A new study explored whether macrophages could help activate an antiviral response in VZV-infected hiPSC-neurons, but found the macrophages were ineffective in suppressing the infection.
  • RNA sequencing results showed a weak immune response in both infected neurons and co-cultured macrophages, indicating that other immune cells, like T-cells, may be necessary for a strong antiviral response against VZV.
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Background: Meniere Disease (MD) is an inner ear syndrome, characterized by episodes of vertigo, tinnitus and fluctuating sensorineural hearing loss. The pathological mechanism leading to sporadic MD is still poorly understood, however an allergic inflammatory response seems to be involved in some patients with MD.

Objective: Decipher an immune signature associated with the syndrome.

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Mass cytometry (MC) is a powerful large-scale immune monitoring technology. To maximize MC data quality, we present a protocol for whole blood analysis together with an R package, Cyto Quality Pipeline (CytoQP), which minimizes the experimental artifacts and batch effects to ensure data reproducibility. We describe the steps to stimulate, fix, and freeze blood samples before acquisition to make them suitable for retrospective studies.

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In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high-dimensional datasets. During the measurement of these samples, erroneous events can occur such as clogs, speed changes, slow uptake of the sample etc., which can influence the downstream analysis and can even lead to false discoveries.

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