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Exosomes, nanosized membrane-bound vesicles released by cells, play roles in cell signaling, immunology, virology, and oncology. Their study, however, has been hampered by difficulty in isolation and quantification due to their size and the complexity of biological samples. Conventional approaches to improved isolation require specialized equipment and extensive sample preparation time. Therefore, isolation and detection methods of exosomes will benefit biological and clinical studies. Here, we report a microfluidic platform for inline exosome isolation and fluorescent detection using inertial manipulation of antibody-coated exosome capture beads from biological fluids.

Micro-scale biological tools that have allowed probing of individual cells--from the genetic, to proteomic, to phenotypic level--have revealed important contributions of single cells to direct normal and diseased body processes. In analyzing single cells, sample heterogeneity between and within specific cell types drives the need for high-throughput and quantitative measurement of cellular parameters. In recent years, high-throughput single-cell analysis platforms have revealed rare genetic subpopulations in growing tumors, begun to uncover the mechanisms of antibiotic resistance in bacteria, and described the cell-to-cell variations in stem cell differentiation and immune cell response to activation by pathogens. This review surveys these recent technologies, presenting their strengths and contributions to the field, and identifies needs still unmet toward the development of high-throughput single-cell analysis tools to benefit life science research and clinical diagnostics.

Background: Biomarkers of disease progression and treatment response are urgently needed for patients with lymphangioleiomyomatosis (LAM). Activity-based nanosensors, an emerging biosensor class, detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease. Because proteases are dysregulated in LAM and may directly contribute to lung function decline, activity-based nanosensors may enable quantitative, real-time monitoring of LAM progression and treatment response. We aimed to assess the diagnostic utility of activity-based nanosensors in a pre-clinical model of pulmonary LAM., Methods: Tsc2 -null cells were injected intravenously into female nude mice to establish a mouse model of pulmonary LAM. A library of 14 activity-based nanosensors, designed to detect proteases across multiple catalytic classes, was administered into the lungs of LAM mice and healthy controls, urine was collected, and mass spectrometry was performed to measure nanosensor cleavage products. Mice were then treated with rapamycin and monitored with activity-based nanosensors. Machine learning was performed to distinguish diseased from healthy and treated from untreated mice., Results: Multiple activity-based nanosensors (PP03 (cleaved by metallo, aspartic and cysteine proteases), p adjusted <0.0001; PP10 (cleaved by serine, aspartic and cysteine proteases), p adjusted =0.017)) were differentially cleaved in diseased and healthy lungs, enabling strong classification with a machine learning model (area under the curve (AUC) 0.95 from healthy). Within 2 days after rapamycin initiation, we observed normalisation of PP03 and PP10 cleavage, and machine learning enabled accurate classification of treatment response (AUC 0.94 from untreated)., Conclusions: Activity-based nanosensors enable noninvasive, real-time monitoring of disease burden and treatment response in a pre-clinical model of LAM., Competing Interests: Conflict of interest: J.D. Kirkpatrick reports grants from the Ludwig Fund for Cancer Research, during the conduct of the study. In addition, J.D. Kirkpatrick has a patent pending (Lung protease nanosensors and uses thereof; PCT/US2019/052868, filed 25 September 2019). Conflict of interest: A.P. Soleimany has nothing to disclose. Conflict of interest: J.S. Dudani has nothing to disclose. Conflict of interest: H-J. Liu has nothing to disclose. Conflict of interest: H.C. Lam has nothing to disclose. Conflict of interest: C. Priolo has nothing to disclose. Conflict of interest: E.P. Henske has nothing to disclose. Conflict of interest: S.N. Bhatia reports other support from the Howard Hughes Medical Institute and National Institute of Environmental Health Sciences, grants from the Ludwig Fund for Cancer Research and grants from the Koch Institute Marble Center for Cancer Nanomedicine, during the conduct of the study; other support from Vertex Pharmaceuticals, Glympse Bio, Maverick Therapeutics, Satellite Bio, CEND Rx and Moderna Therapeutics, outside the submitted work. In addition, S.N. Bhatia has a patent pending (Lung protease nanosensors and uses thereof; PCT/US2019/052868, filed 25 September 2019)., (Copyright ©The authors 2022.)