Label-free, real-time monitoring of cytochrome C drug responses in microdissected tumor biopsies with a multi-well aptasensor platform.

Functional assays on intact tumor biopsies can complement genomics-based approaches for precision oncology, drug testing, and organs-on-chips cancer disease models by capturing key therapeutic response determinants, such as tissue architecture, tumor heterogeneity, and the tumor microenvironment. Most of these assays rely on fluorescent labeling, a semiquantitative method best suited for single-time-point assays or labor-intensive immunostaining analysis. Here, we report integrated aptamer electrochemical sensors for on-chip, real-time monitoring of cytochrome C, a cell death indicator, from intact microdissected tissues with high affinity and specificity. The platform features a multi-well sensor layout and a multiplexed electronic setup. The aptasensors measure increases in cytochrome C in the supernatant of mouse or human microdissected tumors after exposure to various drug treatments. Because of the sensor's high affinity, it primarily tracks rising concentrations of cytochrome C, capturing dynamic changes during apoptosis. This approach could help develop more advanced cancer disease models and apply to other complex in vitro disease models, such as organs-on-chips and organoids.

Microdissected tumor cuboids: a microscale cancer model for large-scale testing that retains a complex tumor microenvironment.

Current cancer disease models fail to faithfully recapitulate key features of the human tumor microenvironment (TME), such as immune and vascular cells, while simultaneously enabling high-throughput drug tests. We have recently developed a precision slicing method that optimizes the yield of large numbers of cuboidal microtissues ("cuboids", ∼(400 µm) 3 ) from a single tumor biopsy. Here we demonstrate that cuboids from syngeneic mouse tumor models and human tumors retain a complex TME, making them amenable for drug and immunotherapy evaluation. We characterize relevant TME parameters, such as cellular architecture, cytokine secretion, proteomics profiles, and response to drug panels in multi-well arrays. Despite the cutting procedure and the time spent in culture (up to 7 days), the cuboids display strong cytokine and drug responses, including to immunotherapy. Overall, our results suggest that cuboids could provide invaluable therapeutic information for personalized oncology applications, and could help the development of TME-dependent therapeutics and cancer disease models, including for clinical trials.

Low-Cost Robotic Manipulation of Live Microtissues for Cancer Drug Testing.

The scarcity of human biopsies available for drug testing is a paramount challenge for developing new therapeutics, disease models, and personalized treatments. Microtechnologies that combine the microscale manipulation of tissues and fluids offer the exciting possibility of miniaturizing both disease models and drug testing workflows on scarce human biopsies. Unfortunately, these technologies presently require microfluidic devices or robotic dispensers that are not widely accessible. We have rapidly-prototyped an inexpensive platform based on an off-the-shelf robot that can microfluidically manipulate live microtissues into/out of culture plates without using complicated accessories such as microscopes or pneumatic controllers. The robot integrates complex functions with a simple, cost-effective and compact construction, allowing placement inside a tissue culture hood for sterile workflows. We demonstrated a proof-of-concept cancer drug evaluation workflow of potential clinical utility using patient tumor biopsies with multiple drugs on 384-well plates. Our user-friendly, low-cost platform promises to make drug testing of microtissues broadly accessible to pharmaceutical, clinical, and biological laboratories. Teaser: A low-cost robot for handling microtissues and catalyzing their use in cancer drug evaluation and personalized oncology.

Drug testing of monodisperse arrays of live microdissected tumors using a valved multiwell microfluidic platform.

Cancer drug testing in animals is an extremely poor predictor of the drug's safety and efficacy observed in humans. Hence there is a pressing need for functional testing platforms that better predict traditional and immunotherapy responses in human, live tumor tissue or tissue constructs, and at the same time are compatible with the use of mouse tumor tissue to facilitate building more accurate disease models. Since many cancer drug actions rely on mechanisms that depend on the tumor microenvironment (TME), such platforms should also retain as much of the native TME as possible. Additionally, platforms based on miniaturization technologies are desirable to reduce animal use and sensitivity to human tissue scarcity. Present high-throughput testing platforms that have some of these features, e.g. based on patient-derived tumor organoids, require a growth step that alters the TME. On the other hand, microdissected tumors (µDTs) or "spheroids" that retain an intact TME have shown promising responses to immunomodulators acting on native immune cells. However, difficult tissue handling after microdissection has reduced the throughput of drug testing on µDTs, thereby constraining the inherent advantages of producing numerous TME-preserving units of tissue for drug testing. Here we demonstrate a microfluidic 96-well platform designed for drug treatment of hundreds of similarly-sized, cuboidal µDTs ("cuboids") produced from a single tumor sample. The platform organizes a monodisperse array of four cuboids per well in 384 hydrodynamic traps. The microfluidic device, entirely fabricated in thermoplastics, features 96 microvalves that fluidically isolate each well after the cuboid loading step for straightforward multi-drug testing. Since our platform makes the most of scarce tumor tissue, it can potentially be applied to human biopsies that preserve the human TME while minimizing animal testing.

Label-Free, Real-Time Monitoring of Cytochrome C Responses to Drugs in Microdissected Tumor Biopsies with a Multi-Well Aptasensor Platform.

Functional assays on intact tumor biopsies can potentially complement and extend genomics-based approaches for precision oncology, drug testing, and organs-on-chips cancer disease models by capturing key determinants of therapeutic response, such as tissue architecture, tumor heterogeneity, and the tumor microenvironment. Currently, most of these assays rely on fluorescent labeling, a semi-quantitative method best suited to be a single-time-point terminal assay or labor-intensive terminal immunostaining analysis. Here, we report integrated aptamer electrochemical sensors for on-chip, real-time monitoring of increases of cytochrome C, a cell death indicator, from intact microdissected tissues with high affinity and specificity. The platform features a multi-well sensor layout and a multiplexed electronic setup. The aptasensors measure increases in cytochrome C in the supernatant of mouse or human microdissected tumors after exposure to various drug treatments. Since the aptamer probe can be easily exchanged to recognize different targets, the platform could be adapted for multiplexed monitoring of various biomarkers, providing critical information on the tumor and its microenvironment. This approach could not only help develop more advanced cancer disease models but also apply to other complex in vitro disease models, such as organs-on-chips and organoids.

Environmentally adaptive automated recognition of underwater mines with synthetic aperture sonar imagery.

This work demonstrates that automated mine countermeasure (MCM) tasks are greatly facilitated by characterizing the seafloor environment in which the sensors operate as a first step within a comprehensive strategy for how to exploit information from available sensors, multiple detector types, measured features, and target classifiers, depending on the specific seabed characteristics present within the high-frequency synthetic aperture sonar (SAS) imagery used to perform MCM tasks. This approach is able to adapt as environmental characteristics change and includes the ability to recognize novel seabed types. Classifiers are then adaptively retrained through active learning in these unfamiliar seabed types, resulting in improved mitigation of challenging environmental clutter as it is encountered. Further, a segmentation constrained network algorithm is introduced to enable enhanced generalization abilities for recognizing mine-like objects from underrepresented environments within the training data. Additionally, a fusion approach is presented that allows the combination of multiple detectors, feature types spanning both measured expert features and deep learning, and an ensemble of classifiers for the particular seabed mixture proportions measured around each detected target. The environmentally adaptive approach is demonstrated to provide the best overall performance for automated mine-like object recognition.