The heterogeneity of exosomal populations has hindered our understanding of their biogenesis, molecular composition, biodistribution, and functions. By employing asymmetric-flow field-flow fractionation (AF4), we identified two exosome subpopulations (large exosome vesicles, Exo-L, 90-120 nm; small exosome vesicles, Exo-S, 60-80 nm) and discovered an abundant population of non-membranous nanoparticles termed “exomeres” (~35 nm). Exomere proteomic profiling revealed an enrichment in metabolic enzymes and hypoxia, microtubule and coagulation proteins and specific pathways, such as glycolysis and mTOR signaling. Exo-S and Exo-L contained proteins involved in endosomal function and secretion pathways, and mitotic spindle and IL-2/STAT5 signaling pathways, respectively. Exo-S, Exo-L, and exomeres each had unique N-glycosylation, protein, lipid, and DNA and RNA profiles and biophysical properties. These three nanoparticle subsets demonstrated diverse organ biodistribution patterns, suggesting distinct biological functions. This study demonstrates that AF4 can serve as an improved analytical tool for isolating and addressing the complexities of heterogeneous nanoparticle subpopulations.

R47H- TREM2 mutation enhances AKT signaling in human AD microglia and mediates proinflammatory and synaptic toxicity in a tauopathy mouse model.

No abstract

Increased deposition of extracellular matrix (ECM) is a known inhibitor of axonal regrowth and remyelination. Recent in vitro studies have demonstrated that oligodendrocyte differentiation is impacted by the physical properties of the ECM. However, characterization of the mechanical properties of the healthy and injured CNS myelin is challenging, and has largely relied on non-invasive, low-resolution methods. To address this, we have employed atomic force microscopy to perform micro-indentation measurements of demyelinated tissue at cellular scale. Analysis of mouse and human demyelinated brains indicate that acute demyelination results in decreased tissue stiffness that recovers with remyelination; while chronic demyelination is characterized by increased tissue stiffness, which correlates with augmented ECM deposition. Thus, changes in the mechanical properties of the acutely (softer) or chronically (stiffer) demyelinated brain might contribute to differences in their regenerative capacity. Our findings are relevant to the optimization of cell-based therapies aimed at promoting CNS regeneration and remyelination.

Background: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. Purpose: To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. Study Type: Retrospective. Population: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases). Field Strength/Sequence: 1.5 to 3.0 Tesla, T2-weighted image pulse sequences. Assessment: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor. Statistical Tests: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. Results: Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high-vs. low-risk of prostate disease, respectively. Data Conclusion: AI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time. Level of Evidence: 1 Technical Efficacy Stage: 2