Targeting macrophages with phosphatidylserine-rich liposomes as a potential antigen-specific immunotherapy for type 1 diabetes.

Type 1 diabetes (T1D) results from a breakdown in immunological tolerance, with pivotal involvement of antigen-presenting cells. In this context, antigen-specific immunotherapies have been developed to arrest autoimmunity, such as phosphatidylserine (PS)-liposomes. However, the role of certain antigen-presenting cells in immunotherapy, particularly human macrophages (Mφ) in T1D remains elusive. The aim of this study was to determine the role of Mφ in antigen-specific immune tolerance and T1D. To that end, we evaluated Mφ ability to capture apoptotic-body mimicking PS-liposomes in mice and conducted a phenotypic and functional characterisation of four human monocyte-derived Mφ (MoMφ) subpopulations (M0, M1, M2a and M2c) after PS-liposomes uptake. Our findings in mice identified Mφ as the most phagocytic cell subset in the spleen and liver. In humans, while phagocytosis rates were comparable between T1D and control individuals, PS-liposome capture dynamics differed among Mφ subtypes, favouring inflammatory (M1) and deactivated (M2c) Mφ. Notably, high nanoparticle concentrations did not affect macrophage viability. PS-liposome uptake by Mφ induced alterations in membrane molecule expression related to immunoregulation, reduced secretion of IL-6 and IL-12, and diminished autologous T-cell proliferation in the context of autoantigen stimulation. These results underscore the tolerogenic effects of PS-liposomes and emphasize their potential to target human Mφ, providing valuable insights into the mechanism of action of this preclinical immunotherapy.

Fast-screening flow cytometry method for detecting nanoplastics in human peripheral blood.

Plastic pollution is a global problem. Animals and humans can ingest and inhale plastic particles, with uncertain health consequences. Nanoplastics (NPs) are particles ranging from 1 nm to 1000 nm that result from the erosion or breakage of larger plastic debris, and can be highly polydisperse in physical properties and heterogeneous in composition. Potential effects of NPs exposure may be associated with alterations in the xenobiotic metabolism, nutrients absorption, energy metabolism, cytotoxicity, and behavior. In humans, no data on NPs absorptions has been reported previously. Given that their detection relies significantly on environmental exposure, we have prospectively studied the presence of NPs in human peripheral blood (PB). Specifically, we have used fluorescence techniques and nanocytometry, together with the staining of the lipophilic dye Nile Red (NR), to demonstrate that NPs can be accurately detected using flow cytometry.•Potential effects of nanoplastics exposure.•Fluorescence techniques and nanocytometry.•Accurate detection using flow cytometry.

Towards an automated approach for smart sterility test examination.

As new technologies emerge, deep learning applications are often integral parts of new products as features and often as differentiating benefits. This is especially notable in commercial consumer products in everyday applications, such as voice assistants or streaming content recommendation systems. Due to the power and applicability of these deep learning technologies significant efforts are being directed to the development and integration of appropriate models into science and engineering applications to supplant analogue systems that may be highly prone to human error. Here we present an innovative, low-cost approach to advance sterility assessment workflows that are required and regulated within drug release/manufacturing processes. The model system leverages off-the-shelf hardware as well as deep learning models to detect and classify different microbial contaminations in test containers. The paired hardware and software tools were evaluated in experiments using common model organisms (C. sporogenes, P. aeruginosa, S. aureus). With this approach we were able to detect all three test organisms across 40 experiments, furthermore we were capable of classifying the present organisms with an average classification accuracy of over 87%.

An Emerging Fluorescence-Based Technique for Quantification and Protein Profiling of Extracellular Vesicles.

Robust and well-established techniques for the quantification and characterization of extracellular vesicles (EVs) are a crucial need for the utilization of EVs as potential diagnostic and therapeutic tools. Current bulk analysis techniques such as proteomics and Western blot suffer from low resolution in the detection of small changes in target marker expression levels, exemplified by the heterogeneity of EVs. Microscopy-based techniques can provide valuable information from individual EVs; however, they are time-consuming and statistically less powerful than other techniques. Flow cytometry has been successfully employed for the quantification and characterization of individual EVs within larger populations. However, traditional flow cytometry is not highly suited for the examination of smaller, submicron particles. Here we demonstrate the accurate and precise quantification of nanoparticles such as EVs using the Virus Counter 3100 (VC3100) platform, a fluorescence-based technique that uses the principles of flow cytometry with critical enhancements to enable the effective detection of smaller particles. This approach can detect nanoparticles precisely with no evidence of inaccurate concentration measurement from masking effects associated with traditional nanoparticle tracking analysis (NTA). Fluorescently labeled EVs from different sources were successfully quantified using the VC3100 without a postlabeling washing step. Moreover, protein profiling and characterization of individual EVs were achieved and have been shown to determine the expression level of target protein markers.