All-in-One Fusogenic Nanoreactor for the Rapid Detection of Exosomal MicroRNAs for Breast Cancer Diagnosis.

Molecular-profiling-based cancer diagnosis has significant implications for predicting disease prognosis and selecting targeted therapeutic interventions. The analysis of cancer-derived extracellular vesicles (EVs) provides a noninvasive and sequential method to assess the molecular landscape of cancer. Here, we developed an all-in-one fusogenic nanoreactor (FNR) encapsulating DNA-fueled molecular machines (DMMs) for the rapid and direct detection of EV-associated microRNAs (EV miRNAs) in a single step. This platform was strategically designed to interact selectively with EVs and induce membrane fusion under a specific trigger. After fusion, the DMMs recognized the target miRNA and initiated nonenzymatic signal amplification within a well-defined reaction volume, thus producing an amplified fluorescent signal within 30 min. We used the FNRs to analyze the unique expression levels of three EV miRNAs in various biofluids, including cell culture, urine, and plasma, and obtained an accuracy of 86.7% in the classification of three major breast cancer (BC) cell lines and a diagnostic accuracy of 86.4% in the distinction between patients with cancer and healthy donors. Notably, a linear discriminant analysis revealed that increasing the number of miRNAs from one to three improved the accuracy of BC patient discrimination from 78.8 to 95.4%. Therefore, this all-in-one diagnostic platform performs nondestructive EV processing and signal amplification in one step, providing a straightforward, accurate, and effective individual EV miRNA analysis strategy for personalized BC treatment.

Deep learning-assisted monitoring of trastuzumab efficacy in HER2-Overexpressing breast cancer via SERS immunoassays of tumor-derived urinary exosomal biomarkers.

Monitoring drug efficacy is significant in the current concept of companion diagnostics in metastatic breast cancer. Trastuzumab, a drug targeting human epidermal growth factor receptor 2 (HER2), is an effective treatment for metastatic breast cancer. However, some patients develop resistance to this therapy; therefore, monitoring its efficacy is essential. Here, we describe a deep learning-assisted monitoring of trastuzumab efficacy based on a surface-enhanced Raman spectroscopy (SERS) immunoassay against HER2-overexpressing mouse urinary exosomes. Individual Raman reporters bearing the desired SERS tag and exosome capture substrate were prepared for the SERS immunoassay; SERS tag signals were collected to prepare deep learning training data. Using this deep learning algorithm, various complicated mixtures of SERS tags were successfully quantified and classified. Exosomal antigen levels of five types of cell-derived exosomes were determined using SERS-deep learning analysis and compared with those obtained via quantitative reverse transcription polymerase chain reaction and western blot analysis. Finally, drug efficacy was monitored via SERS-deep learning analysis using urinary exosomes from trastuzumab-treated mice. Use of this monitoring system should allow proactive responses to any treatment-resistant issues.

Colorimetric Detection of HER2-Overexpressing-Cancer-Derived Exosomes in Mouse Urine Using Magnetic-Polydiacetylene Nanoparticles.

Breast cancer (BC) is a major global health problem, with ≈20-25% of patients overexpressing human epidermal growth factor receptor 2 (HER2), an aggressive marker, yet access to early detection and treatment varies across countries. A low-cost, equipment-free, and easy-to-use polydiacetylene (PDA)-based colorimetric sensor is developed for HER2-overexpressing cancer detection, designed for use in low- and middle-income countries (LMICs). PDA nanoparticles are first prepared through thin-film hydration. Subsequently, hydrophilic magnetic nanoparticles and HER2 antibodies are sequentially conjugated to them. The synthesized HER2-MPDA can be concentrated and separated by a magnetic field while inheriting the optical characteristics of PDA. The specific binding of HER2 antibody in HER2-MPDA to HER2 receptor in HER2-overexpressing exosomes causes a blue-to-red color change by altering the molecular structure of the PDA backbone. This colorimetric sensor can simultaneously separate and detect HER2-overexpressing exosomes. HER2-MPDA can detect HER2-overexpressing exosomes in the culture medium of HER2-overexpressing BC cells and in mouse urine samples from a HER2-overexpressing BC mouse model. It can selectively isolate and detect only HER2-overexpressing exosomes through magnetic separation, and its detection limit is found to be 8.5 × 108 particles mL-1. This colorimetric sensor can be used for point-of-care diagnosis of HER2-overexpressing BC in LMICs.

An immuno-magnetophoresis-based microfluidic chip to isolate and detect HER2-Positive cancer-derived exosomes via multiple separation.

Exosomes are useful for cancer diagnosis and monitoring. However, clinical samples contain impurities that complicate direct analyses of cancer-derived exosomes. Therefore, a microfluidic chip-based magnetically labeled exosome isolation system (MEIS-chip) was developed as a lab-on-a-chip platform for human epidermal growth factor receptor 2 (HER2)-positive cancer diagnosis and monitoring. Various magnetic nanoclusters (MNCs) were synthesized with different degrees of magnetization, and antibodies were introduced to capture HER2-overexpressing and common exosomes using immunoaffinity. MNC-bonded exosomes were separated into different exits according to their magnetization degrees. The MEIS-chip efficiently separated HER2-overexpressing exosomes from common exosomes that did not contain disease-related information. The simultaneous separation of HER2-and non-HER2-overexpressing exosomes provided a means of analyzing high-purity HER2-overexpressing exosomes while minimizing the contribution of non-target exosomes, reducing misdiagnosis risk. Notably, common exosomes served as a negative control for monitoring real-time changes in HER2 expression. These findings support the application of MEIS-chip for cancer diagnosis and treatment monitoring via effective exosome isolation.

Advanced bioactive glue tethering Lubricin/PRG4 to promote integrated healing of avascular meniscus tears.

Meniscus injuries are extremely common with approximately one million patients undergoing surgical treatment annually in the U.S. alone, but no regenerative therapy exist. Previously, we showed that controlled applications of connective tissue growth factor (CTGF) and transforming growth factor beta 3 (TGFß3) via fibrin-based bio-glue facilitate meniscus healing by inducing recruitment and stepwise differentiation of synovial mesenchymal stem/progenitor cells. Here, we first explored the potential of genipin, a natural crosslinker, to enhance fibrin-based glue's mechanical and degradation properties. In parallel, we identified the harmful effects of lubricin on meniscus healing and investigated the mechanism of lubricin deposition on the injured meniscus surface. We found that the pre-deposition of hyaluronic acid (HA) on the torn meniscus surface mediates lubricin deposition. Then we implemented chemical modifications with heparin conjugation and CD44 on our bioactive glue to achieve strong initial bonding and integration of lubricin pre-coated meniscal tissues. Our data suggested that heparin conjugation significantly enhances lubricin-coated meniscal tissues. Similarly, CD44, exhibiting a strong binding affinity to lubricin and hyaluronic acid (HA), further improved the integrated healing of HA/lubricin pre-coated meniscus injuries. These findings may represent an important foundation for developing a translational bio-active glue guiding the regenerative healing of meniscus injuries.

Overexpression of transcription factor FoxA2 in the developing skeleton causes an enlargement of the cartilage hypertrophic zone, but it does not trigger ectopic differentiation in immature chondrocytes.

We previously found that FoxA factors are necessary for chondrocyte differentiation. To investigate whether FoxA factors alone are sufficient to drive chondrocyte hypertrophy, we build a FoxA2 transgenic mouse in which FoxA2 cDNA is driven by a reiterated Tetracycline Response Element (TRE) and a minimal CMV promoter. This transgenic line was crossed with a col2CRE;Rosa26rtTA/+ mouse line to generate col2CRE;Rosa26rtTA/+;TgFoxA2+/- mice for inducible expression of FoxA2 in cartilage using doxycycline treatment. Ectopic expression of FoxA2 in the developing skeleton reveals skeletal defects and shorter skeletal elements in E17.5 mice. The chondro-osseous border was frequently mis-shaped in mutant mice, with small islands of col.10+ hypertrophic cells extending in the metaphyseal bone. Even though overexpression of FoxA2 causes an accumulation of hypertrophic chondrocytes, it did not trigger ectopic hypertrophy in the immature chondrocytes. This suggests that FoxA2 may need transcriptional co-factors (such as Runx2), whose expression is restricted to the hypertrophic zone, and absent in the immature chondrocytes. To investigate a potential FoxA2/Runx2 interaction in immature chondrocytes versus hypertrophic cells, we separated these two subpopulations by FACS to obtain CD24+CD200+ hypertrophic chondrocytes and CD24+CD200- immature chondrocytes and we ectopically expressed FoxA2 alone or in combination with Runx2 via lentiviral gene delivery. In CD24+CD200+ hypertrophic chondrocytes, FoxA2 enhanced the expression of chondrocyte hypertrophic markers collagen 10, MMP13, and alkaline phosphatase. In contrast, in the CD24+CD200- immature chondrocytes, neither FoxA2 nor Runx2 overexpression could induce ectopic expression of hypertrophic markers MMP13, alkaline phosphatase, or PTH/PTHrP receptor. Overall these findings mirror our in vivo data, and suggest that induction of chondrocyte hypertrophy by FoxA2 may require other factors in addition to Runx2 (i.e., Hif2α, MEF2C, or perhaps unknown factors), whose expression/activity is rate-limiting in immature chondrocytes.

Oxo-M and 4-PPBP Delivery via Multi-Domain Peptide Hydrogel Toward Tendon Regeneration.

We have recently identified novel small molecules, Oxo-M and 4-PPBP, which specifically stimulate endogenous tendon stem/progenitor cells (TSCs), leading to potential regenerative healing of fully transected tendons. Here, we investigated an injectable, multidomain peptide (MDP) hydrogel providing controlled delivery of the small molecules for regenerative tendon healing. We investigated the release kinetics of Oxo-M and 4-PPBP from MDP hydrogels and the effect of MDP-released small molecules on tenogenic differentiation of TSCs and in vivo tendon healing. In vitro, MDP showed a sustained release of Oxo-M and 4-PPBP and a slower degradation than fibrin. In addition, tenogenic gene expression was significantly increased in TSC with MDP-released Oxo-M and 4-PPBP as compared to the fibrin-released. In vivo, MDP releasing Oxo-M and 4-PPBP significantly improved tendon healing, likely associated with prolonged effects of Oxo-M and 4-PPBP on suppression of M1 macrophages and promotion of M2 macrophages. Comprehensive analyses including histomorphology, digital image processing, and modulus mapping with nanoindentation consistently suggested that Oxo-M and 4-PPBP delivered via MDP further improved tendon healing as compared to fibrin-based delivery. In conclusion, MDP delivered with Oxo-M and 4-PPBP may serve as an efficient regenerative therapeutic for in situ tendon regeneration and healing.

Automated Tracking and Quantification of Autistic Behavioral Symptoms Using Microsoft Kinect.

The prevalence of autism spectrum disorder (ASD) has risen significantly in the last ten years, and today, roughly 1 in 68 children has been diagnosed. One hallmark set of symptoms in this disorder are stereotypical motor movements. These repetitive movements may include spinning, body-rocking, or hand-flapping, amongst others. Despite the growing number of individuals affected by autism, an effective, accurate method of automatically quantifying such movements remains unavailable. This has negative implications for assessing the outcome of ASD intervention and drug studies. Here we present a novel approach to detecting autistic symptoms using the Microsoft Kinect v.2 to objectively and automatically quantify autistic body movements. The Kinect camera was used to film 12 actors performing three separate stereotypical motor movements each. Visual Gesture Builder (VGB) was implemented to analyze the skeletal structures in these recordings using a machine learning approach. In addition, movement detection was hard-coded in Matlab. Manual grading was used to confirm the validity and reliability of VGB and Matlab analysis. We found that both methods were able to detect autistic body movements with high probability. The machine learning approach yielded highest detection rates, supporting its use in automatically quantifying complex autistic behaviors with multi-dimensional input.