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Toward Scalable Detection of Microplastics and Nanoplastics in Human Biological Systems: Alignment with STOMP Objectives and Preliminary Feasibility Data
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This preliminary study described a non-destructive, image-based optical framework that detects microplastics and nanoplastics in intact biofluids such as urine and blood without sample destruction, showing reproducible spatial and temporal signal patterns in spiked matrices. The authors proposed this approach as a pathway toward scalable, high-throughput monitoring of human microplastic exposure aligned with the ARPA-H STOMP program objectives.
Microplastics and nanoplastics (MNPs) are increasingly detected in human blood, urine, and tissues, yet scalable, non-destructive methods for routine systemic exposure measurement remain unavailable. Current laboratory techniques are destructive, low-throughput, and unsuitable for longitudinal or population-scale monitoring. The ARPA-H STOMP program identifies scalable measurement, mechanistic insight, and eventual removal as priority needs. Here we describe a non-destructive, image-based optical framework that detects emergent interaction signatures in intact biofluids. Preliminary experiments in spiked urine and blood-relevant matrices demonstrate reproducible spatial and temporal patterns consistent with MNP presence, including earlier signal formation under nanoplastic conditions. An ongoing volunteer-based calibration study is evaluating biological variability and repeatability. This approach offers a pathway toward decentralized, high-throughput MNP monitoring that directly supports STOMP objectives.
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Toward Scalable Detection of Microplastics and Nanoplastics in Human Biological Systems: Alignment with STOMP Objectives and Preliminary Feasibility Data
AI summary Read the abstract
This preliminary study described a non-destructive, image-based optical framework that detects microplastics and nanoplastics in intact biofluids such as urine and blood without sample destruction, showing reproducible spatial and temporal signal patterns in spiked matrices. The authors proposed this approach as a pathway toward scalable, high-throughput monitoring of human microplastic exposure aligned with the ARPA-H STOMP program objectives.
Detection of Microplastics and Nanoplastics in Blood-Relevant Matrices for Scalable Routine Testing
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Using optical imaging and computational analysis of simulated blood matrices, researchers demonstrated a scalable, non-destructive method capable of distinguishing microplastic and nanoplastic contamination conditions, with nanoplastics showing earlier and more pronounced optical signatures. This framework could enable routine blood-based monitoring of systemic MNP exposure without the cost and complexity of current analytical methods.
Detection of Microplastics and Nanoplastics in Blood-Relevant Matrices for Scalable Routine Testing
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A scalable, non-destructive optical imaging workflow successfully detected microplastics and nanoplastics in simulated blood matrices, with nanoplastics showing distinct aggregation patterns even at low concentrations. This approach could enable routine clinical monitoring of systemic plastic burden, which is essential given that circulating particles may interact with immune, vascular, and neurological systems.
Preliminary Report: Scalable Detection of Microplastics and Nanoplastics in Human Urine
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A non-destructive optical imaging workflow successfully detected both micro- and nanoplastic signals in spiked human urine, showing reproducible differences in spatial heterogeneity and aggregation behavior at varying concentrations. This scalable, non-invasive detection method could enable routine biomonitoring of human plastic exposure through urine sampling.
Preliminary Report: Scalable Detection of Microplastics and Nanoplastics in Human Urine
AI summary Read the abstract
A preliminary study validated a non-destructive optical imaging workflow for detecting both micro- and nanoplastics in spiked human urine samples, observing reproducible differences in particle aggregation and spatial organization between microplastic and nanoplastic conditions at low concentrations. A scalable, non-invasive urine-based detection method would transform biomonitoring capacity, enabling population-level tracking of internal microplastic exposure without the invasive sampling currently required.
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