The in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus (MN) assay is a well-established assay for quantification of DNA damage, and is required by regulatory bodies worldwide to screen chemicals for genetic toxicity. The MN assay is performed in two variations: scoring MN in cytokinesis-blocked binucleated cells or directly in unblocked mononucleated cells. Several methods have been developed to score the MN assay, including manual and automated microscopy, and conventional flow cytometry, each with advantages and limitations. Previously, we applied imaging flow cytometry (IFC) using the ImageStream® to develop a rapid and automated MN assay based on high throughput image capture and feature-based image analysis in the IDEAS® software. However, the analysis strategy required rigorous optimization across chemicals and cell lines. To overcome the complexity and rigidity of feature-based image analysis, in this study we used the Amnis® AI software to develop a deep-learning method based on convolutional neural networks to score IFC data in both the cytokinesis-blocked and unblocked versions of the MN assay. We show that the use of the Amnis AI software to score imagery acquired using the ImageStream® compares well to manual microscopy and outperforms IDEAS® feature-based analysis, facilitating full automation of the MN assay.

Assessing Particle Formation of Biotherapeutics in Biological Fluids.

The stability of therapeutic proteins can be impacted in vivo after administration, which may affect patient safety or treatment efficacy, or both. Stability testing of therapeutic proteins using models representing physiologic conditions may guide preclinical development strategy; however, to date only a few studies assessing the physical stability are available in the public domain. In this manuscript, the stability of seven fluorescently labeled monoclonal antibodies (mAbs) was evaluated in human serum and phosphate-buffered saline, two models often discussed to be representative of the situation in humans after intravenous administration. Subvisible particles were analyzed using light obscuration, flow imaging, and imaging flow cytometry. All methods showed that serum itself formed particles under in vitro conditions. Imaging flow cytometry demonstrated that mean particle size and counts of mAbs increased substantially in serum over five days; however, particle formation in phosphate-buffered saline was comparably low. Stability differences were observed across the mAbs evaluated, and imaging flow cytometry data indicated that fluorescently labeled mAbs primarily interacted with serum components. The results indicate that serum may be more suitable as in vitro model to simulate physiologic intravenous conditions in patients closely and evaluate the in vivo stability of therapeutic proteins. Fluorescence labeling and detection methods may be applied to differentiate particles containing therapeutic protein from high amounts of serum particles that form over time.

Quantum dots as a platform for nanoparticle drug delivery vehicle design.

Nanoparticle-based drug delivery (NDD) has emerged as a promising approach to improving upon the efficacy of existing drugs and enabling the development of new therapies. Proof-of-concept studies have demonstrated the potential for NDD systems to simultaneously achieve reduced drug toxicity, improved bio-availability, increased circulation times, controlled drug release, and targeting. However, clinical translation of NDD vehicles with the goal of treating particularly challenging diseases, such as cancer, will require a thorough understanding of how nanoparticle properties influence their fate in biological systems, especially in vivo. Consequently, a model system for systematic evaluation of all stages of NDD with high sensitivity, high resolution, and low cost is highly desirable. In theory, this system should maintain the properties and behavior of the original NDD vehicle, while providing mechanisms for monitoring intracellular and systemic nanocarrier distribution, degradation, drug release, and clearance. For such a model system, quantum dots (QDots) offer great potential. QDots feature small size and versatile surface chemistry, allowing their incorporation within virtually any NDD vehicle with minimal effect on overall characteristics, and offer superb optical properties for real-time monitoring of NDD vehicle transport and drug release at both cellular and systemic levels. Though the direct use of QDots for drug delivery remains questionable due to their potential long-term toxicity, the QDot core can be easily replaced with other organic drug carriers or more biocompatible inorganic contrast agents (such as gold and magnetic nanoparticles) by their similar size and surface properties, facilitating translation of well characterized NDD vehicles to the clinic, maintaining NDD imaging capabilities, and potentially providing additional therapeutic functionalities such as photothermal therapy and magneto-transfection. In this review we outline unique features that make QDots an ideal platform for nanocarrier design and discuss how this model has been applied to study NDD vehicle behavior for diverse drug delivery applications.

Rapid multitarget immunomagnetic separation through programmable DNA linker displacement.

Immunomagnetic separation has become an essential tool for high-throughput and low-cost isolation of biomolecules and cells from heterogeneous samples. However, as magnetic selection is essentially a "black-and-white" assay, its application has been largely restricted to single-target and single-parameter studies. To address this issue, we have developed an immunomagnetic separation technology that can quickly sort multiple targets in high yield and purity using selectively displaceable DNA linkers. We envision that this technology will be readily adopted for experiments requiring high-throughput selection of multiple targets or further adapted for selection of a single target based on multiple surface epitopes.