Atherosclerosis is a leading cause of death worldwide in industrialized countries. Disease progression and regression are associated with different activation states of macrophages derived from inflammatory monocytes entering the plaques. The features of monocyte-to-macrophage transition and the full spectrum of macrophage activation states during either plaque progression or regression, however, are incompletely established. Here, we use a combination of single-cell RNA sequencing and genetic fate mapping to profile, for the first time to our knowledge, plaque cells derived from CX3CR1 ⁺ precursors in mice during both progression and regression of atherosclerosis. The analyses revealed a spectrum of macrophage activation states with greater complexity than the traditional M1 and M2 polarization states, with progression associated with differentiation of CXC3R1 ⁺ monocytes into more distinct states than during regression. We also identified an unexpected cluster of proliferating monocytes with a stem cell–like signature, suggesting that monocytes may persist in a proliferating self-renewal state in inflamed tissue, rather than differentiating immediately into macrophages after entering the tissue.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Circulating cancer exosomes are microvesicles which originate from malignant cells and other organs influenced by the disease and can be found in blood. The exosomal proteomic cargo can often be traced to the cells from which they originated, reflecting the physiological status of these cells. The similarities between cancer exosomes and the tumor cells they originate from exhibit the potential of these vesicles as an invaluable target for liquid biopsies. Exosomes were isolated from the serum of eight osteosarcoma-bearing dogs, five healthy dogs, and five dogs with traumatic fractures. We also characterized exosomes which were collected longitudinally from patients with osteosarcoma prior and 2 weeks after amputation, and eventually upon detection of lung metastasis. Exosomal proteins fraction were analyzed by label-free mass spectrometry proteomics and were validated with immunoblots of selected proteins. Ten exosomal proteins were found that collectively discriminate serum of osteosarcoma patients from serum healthy or fractured dogs with an accuracy of 85%. Additionally, serum from different disease stages could be distinguished with an accuracy of 77% based on exosomal proteomic composition. The most discriminating protein changes for both sample group comparisons were related to complement regulation, suggesting an immune evasion mechanism in early stages of osteosarcoma as well as in advanced disease.

Electrochemical detection, with its advantages of being rapid, multi-time point, compatible with cost-effective fabrication methods, and having the potential for miniaturization and portability, has great promise for point-of-care drug monitoring. However, a continuing challenge concerns the robust and sensitive electrochemical detection of pharmaceutical analytes from biological fluids. These complex matrices, such as saliva, sweat, interstitial fluid, urine, and blood/serum, contain multiple components that can contribute to an increased background or reduced analyte signal. In this mini-review, we discuss progress on electrochemical sensing in complex biofluids. We first introduce the challenge of drug titration in the management of various health conditions and provide an overview of the motivation for improved therapeutic drug monitoring, including current limitations. We then review progress on pharmaceutical drug detection from these biofluids with a focus on sample preprocessing, electrode modification for signal amplification, and/or electrode passivation to minimize fouling. Finally, we highlight promising strategies that have enabled robust drug quantification for clinical relevance and that may be useful for field-use systems.

Background: Long noncoding RNAs (lncRNAs) have roles in gene regulation, epigenetics, and molecular scaffolding and it is hypothesized that they underlie some mammalian evolutionary adaptations. However, for many mammalian species, the absence of a genome assembly precludes the comprehensive identification of lncRNAs. The genome of the American beaver (Castor canadensis) has recently been sequenced, setting the stage for the systematic identification of beaver lncRNAs and the characterization of their expression in various tissues. The objective of this study was to discover and profile polyadenylated lncRNAs in the beaver using high-throughput short-read sequencing of RNA from sixteen beaver tissues and to annotate the resulting lncRNAs based on their potential for orthology with known lncRNAs in other species.Results: Using de novo transcriptome assembly, we found 9528 potential lncRNA contigs and 187 high-confidence lncRNA contigs. Of the high-confidence lncRNA contigs, 147 have no known orthologs (and thus are putative novel lncRNAs) and 40 have mammalian orthologs. The novel lncRNAs mapped to the Oregon State University (OSU) reference beaver genome with greater than 90% sequence identity. While the novel lncRNAs were on average shorter than their annotated counterparts, they were similar to the annotated lncRNAs in terms of the relationships between contig length and minimum free energy (MFE) and between coverage and contig length. We identified beaver orthologs of known lncRNAs such asXIST,MEG3,TINCR, andNIPBL-DT.We profiled the expression of the 187 high-confidence lncRNAs across 16 beaver tissues (whole blood, brain, lung, liver, heart, stomach, intestine, skeletal muscle, kidney, spleen, ovary, placenta, castor gland, tail, toe-webbing, and tongue) and identified both tissue-specific and ubiquitous lncRNAs.Conclusions: To our knowledge this is the first report of systematic identification of lncRNAs and their expression atlas in beaver. LncRNAs—both novel and those with known orthologs—are expressed in each of the beaver tissues that we analyzed. For some beaver lncRNAs with known orthologs, the tissue-specific expression patterns were phylogenetically conserved. The lncRNA sequence data files and raw sequence files are available via the web supplement and the NCBI Sequence Read Archive, respectively.