Simple droplet microfluidics platform for drug screening on cancer spheroids

A Novel Microfluidic Platform for Personalized Anticancer Drug Screening Through Image Analysis

The advancement of personalized treatments in oncology has garnered increasing attention, particularly for rare and aggressive cancer with low survival rates like the bone tumors osteosarcoma and chondrosarcoma. This study introduces a novel PDMS-agarose microfluidic device tailored for generating patient-derived tumor spheroids and serving as a reliable tool for personalized drug screening. Using this platform in tandem with a custom imaging index, we evaluated the impact of the anticancer agent doxorubicin on spheroids from both tumor types. The device produces 20 spheroids, each around 300 µm in diameter, within a 24 h timeframe, facilitating assessments of characteristics and reproducibility. Following spheroid generation, we measured patient-derived spheroid diameters in bright-field images, calcein AM-positive areas/volume, and the binary fraction area, a metric analyzing fluorescence intensity. By employing a specially developed equation that combines viability signal extension and intensity, we observed a substantial decrease in spheroid viability of around 75% for both sarcomas at the highest dosage (10 µM). Osteosarcoma spheroids exhibited greater sensitivity to doxorubicin than chondrosarcoma spheroids within 48 h. This approach provides a reliable in vitro model for aggressive sarcomas, representing a personalized approach for drug screening that could lead to more effective cancer treatments tailored to individual patients, despite some implementation challenges.

Automated Dynamic Inlet Microfluidics (ADIM) system: cost-effective biaxial nanoliter droplet on demand generation platform and its application in agglutination assays

The paper demonstrates an adaptation of a Prusa Mini+ 3D printer through the integration of 3D printed modules, creating a system capable of producing varied droplets from multiple Eppendorf tubes. Building upon our previous model, this system enhances calibration methodology enabling any fused deposition modeling (FDM) printer to produce mono-disperse droplets (coefficient of variance (CV%) <2% for train of 100 droplets) with 6900 assays per hour rate. The cost of the developed system is 85% lower than that of existing droplet generation solutions on the market, and 30% more economical than the previous iteration of the system. Additionally, the system's utility in quantification of agglutination assays is highlighted using image analysis, capable of distinguishing between agglutinated and non-agglutinated samples. By offering significant savings and ease of use, this system aims to lower the barriers to entry for microfluidic research, potentially broadening the scope of scientific exploration and application in this field.

Size and shape control of microgel-encapsulating tumor spheroid via a user-friendly solenoid valve-based sorter and its application on precise drug testing

The precision of previous cancer research based on tumor spheroids, especially the microgel-encapsulating tumor spheroids, was limited by the high heterogeneity in the tumor spheroid size and shape. Here, we reported a user-friendly solenoid valve-based sorter to reduce this heterogeneity. The artificial intelligence algorithm was employed to detect and segmentate the tumor spheroids in real-time for the size and shape calculation. A simple off-chip solenoid valve-based sorting actuation module was proposed to sort out target tumor spheroids with the desired size and shape. Utilizing the developed sorter, we successfully uncovered the drug response variations on cisplatin of lung tumor spheroids in the same population but with different sizes and shapes. Moreover, with this sorter, the precision of drug testing on the spheroid population level was improved to a level comparable to the precise but complex single spheroid analysis. The developed sorter also exhibits significant potential for organoid morphology and sorting for precision medicine research.

Particle Detection in Free-Falling Nanoliter Droplets

Sorting and dispensing distinct numbers of cellular aggregates enables the creation of three-dimensional (3D) in vitro models that replicate in vivo tissues, such as tumor tissue, with realistic metabolic properties. One method for creating these models involves utilizing Drop-on-Demand (DoD) dispensing of individual Multicellular Spheroids (MCSs) according to material jetting processes. In the DoD approach, a droplet dispenser ejects droplets containing these MCSs. For the reliable printing of tissue models, the exact number of dispensed MCSs must be determined. Current systems are designed to detect MCSs in the nozzle region prior to the dispensing process. However, due to surface effects, in some cases the spheroids that are detected adhere to the nozzle and are not dispensed with the droplet as expected. In contrast, detection that is carried out only after the droplet has been ejected is not affected by this issue. This work presents a system that can detect micrometer-sized synthetic or biological particles within free-falling droplets with a volume of about 30 nanoliters. Different illumination modalities and detection algorithms were tested. For a glare point projection-based approach, detection accuracies of an average of 95% were achieved for polymer particles and MCF-7 spheroids with diameters above 75 μm. For smaller particles the detection accuracy was still in the range of 70%. An approach with diffuse white light illumination demonstrated an improvement for the detection of small opaque particles. Accuracies up to 96% were achieved using this concept. This makes the two demonstrated methods suitable for improving the accuracy and quality control of particle detection in droplets for Drop-on-Demand techniques and for bioprinting.

Automated dynamic inlet microfluidics system: 3D printer adaptation for cost-effective, low volume, on-demand multi-analyte droplet generator

The paper demonstrates an adaptation of a 3D printer (Prusa Mini+) with novel modules to develop a droplet generation system that generates combinatorial droplets from a standard 96 well plate. The calibration methodology developed would allow any fused deposition modeling (FDM) printer to generate monodisperse droplets (coefficient of variance (CV%) < 5%) from well plates or vials of any geometry. The system maintains precision across various volumes while maintaining a C.V. range of 0.81% to 3.61%, with an increased precision for larger volumes. The cost of the system developed is 70% less than commercially available droplet generation packages. Successful droplet library storage is accomplished via 3D printed cartridge connectors. The implemented system has been calibrated for Tygon® and PTFE at different velocities and volumetric configurations.

Adipose Tissue in Breast Cancer Microphysiological Models to Capture Human Diversity in Preclinical Models

Female breast cancer accounts for 15.2% of all new cancer cases in the United States, with a continuing increase in incidence despite efforts to discover new targeted therapies. With an approximate failure rate of 85% for therapies in the early phases of clinical trials, there is a need for more translatable, new preclinical in vitro models that include cellular heterogeneity, extracellular matrix, and human-derived biomaterials. Specifically, adipose tissue and its resident cell populations have been identified as necessary attributes for current preclinical models. Adipose-derived stromal/stem cells (ASCs) and mature adipocytes are a normal part of the breast tissue composition and not only contribute to normal breast physiology but also play a significant role in breast cancer pathophysiology. Given the recognized pro-tumorigenic role of adipocytes in tumor progression, there remains a need to enhance the complexity of current models and account for the contribution of the components that exist within the adipose stromal environment to breast tumorigenesis. This review article captures the current landscape of preclinical breast cancer models with a focus on breast cancer microphysiological system (MPS) models and their counterpart patient-derived xenograft (PDX) models to capture patient diversity as they relate to adipose tissue.