Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Interfacial Adsorption Controls Particle Formation in Antibody Formulations Subjected to Extensional Flows and Hydrodynamic Shear.

During their manufacturing and delivery to patients, therapeutic proteins are commonly exposed to various interfaces and to hydrodynamic shear forces. Although adsorption of proteins to solid-liquid interfaces is known to foster formation of protein aggregates and particles, the impact of shear remains controversial, in part because of experimental challenges in separating the effects of shear from those caused by simultaneous exposure to interfaces. Extensional flows (occurring when solutions flow through sudden contractions) exert localized elongational forces that have been suspected to be damaging to proteins. In this work, we measured aggregation and particle formation in formulations of polyclonal and monoclonal antibodies subjected to extensional flow, high shear (105 s-1) and exposure to stainless-steel/water interfaces. Modification of the surface charge at the stainless steel/water interface changed protein adsorption characteristics without altering shear profiles, enabling shear and interfacial interactions to be separated. Even under conditions where antibodies were subjected to high hydrodynamic shear and extensional flow, production of subvisible particles could be inhibited by modifying the stainless-steel surface charge to minimize antibody adsorption. Digital images of particles recorded by flow imaging microscopy (FIM) and analyzed with machine learning algorithms were consistent with a particle formation mechanism by which antibodies adsorb and aggregate at the stainless-steel/water interface and subsequently form particles when shear displaces the interfacial aggregates, transporting them into the bulk solution. Topographical differences measured using atomic force microscopy (AFM) supported the proposed mechanism by showing reduced levels of protein adsorption on surface-charge-modified stainless-steel.

Epidermal Loss of RORα Enhances Skin Inflammation in a MC903-Induced Mouse Model of Atopic Dermatitis.

Atopic dermatitis (AD) is a chronic inflammatory skin disease featuring skin barrier dysfunction and immune dysregulation. Previously, we reported that the retinoid-related orphan nuclear receptor RORα was highly expressed in the epidermis of normal skin. We also found that it positively regulated the expression of differentiation markers and skin barrier-related genes in human keratinocytes. In contrast, epidermal RORα expression was downregulated in the skin lesions of several inflammatory skin diseases, including AD. In this study, we generated mouse strains with epidermis-specific Rora ablation to understand the roles of epidermal RORα in regulating AD pathogenesis. Although Rora deficiency did not cause overt macroscopic skin abnormalities at the steady state, it greatly amplified MC903-elicited AD-like symptoms by intensifying skin scaliness, increasing epidermal hyperproliferation and barrier impairment, and elevating dermal immune infiltrates, proinflammatory cytokines, and chemokines. Despite the normal appearance at the steady state, Rora-deficient skin showed microscopic abnormalities, including mild epidermal hyperplasia, increased TEWL, and elevated mRNA expression of Krt16, Sprr2a, and Tslp genes, indicating subclinical impairment of epidermal barrier functions. Our results substantiate the importance of epidermal RORα in partially suppressing AD development by maintaining normal keratinocyte differentiation and skin barrier function.

Machine Learning Analysis Provides Insight into Mechanisms of Protein Particle Formation Inside Containers During Mechanical Agitation.

Container choice can influence particle generation within protein formulations. Incompatibility between proteins and containers can manifest as increased particle concentrations, shifts in particle size distributions and changes in particle morphology distributions. In this study, flow imaging microscopy (FIM) combined with machine learning-based goodness-of-fit hypothesis testing algorithms were used in accelerated stability studies to investigate the impact of containers on particle formation. Containers in four major container categories subdivided into eleven container types were filled with monoclonal antibody formulations and agitated with and without headspace, producing subvisible particles. Digital images of the particles were recorded using flow imaging microscopy and analyzed with machine learning algorithms. Particle morphology distributions depended on container category and type, revealing differences that would not have been obvious by analysis of particle concentrations or container surface characteristics alone. Additionally, the algorithm was used to compare morphologies of particles generated in containers against those generated using isolated stresses at air-liquid and container-air-liquid interfaces. These comparisons showed that the morphology distributions of particles formed during agitation most closely resemble distributions that result from exposure of proteins to moving triple interface lines at points where container-air-liquid interfaces intersect. The approach described here can be used to identify dominant causes of particle generation due to protein-container interactions.

Usefulness of a KT Event to Address Practice and Policy Gaps Related to Integrated Care.

There are limited evaluations of the impact of knowledge translation (KT) activities aimed at addressing practice and policy gaps. We report on the impact of an interactive, end-of-grant KT event. Although action items were developed and key stakeholder support attained, minimal follow-through had occurred three months after the KT event. Several organizational obstacles to transitioning knowledge into action were identified: leadership, program policies, infrastructure, changing priorities, workload and physician engagement. Key messages include: (1) ensure ongoing and facilitated networking opportunities, (2) invest in building implementation capacity, (3) target multi-level implementation activities and (4) focus further research on KT evaluation.

Influence of shell composition on the resonance frequency of microbubble contrast agents.

The effect of variations in microbubble shell composition on microbubble resonance frequency is revealed through experiment. These variations are achieved by altering the mole fraction and molecular weight of functionalized polyethylene glycol (PEG) in the microbubble phospholipid monolayer shell and measuring the microbubble resonance frequency. The resonance frequency is measured via a chirp pulse and identified as the frequency at which the pressure amplitude loss of the ultrasound wave is the greatest as a result of passing through a population of microbubbles. For the shell compositions used herein, we find that PEG molecular weight has little to no influence on resonance frequency at an overall PEG mole fraction (0.01) corresponding to a mushroom regime and influences the resonance frequency markedly at overall PEG mole fractions (0.050-0.100) corresponding to a brush regime. Specifically, the measured resonance frequency was found to be 8.4, 4.9, 3.3 and 1.4 MHz at PEG molecular weights of 1000, 2000, 3000 and 5000 g/mol, respectively, at an overall PEG mole fraction of 0.075. At an overall PEG mole fraction of just 0.01, on the other hand, resonance frequency exhibited no systematic variation, with values ranging from 5.7 to 4.9 MHz. Experimental results were analyzed using the Sarkar bubble dynamics model. With the dilatational viscosity held constant (10(-8) N·s/m) and the elastic modulus used as a fitting parameter, model fits to the pressure amplitude loss data resulted in elastic modulus values of 2.2, 2.4, 1.6 and 1.8 N/m for PEG molecular weights of 1000, 2000, 3000 and 5000 g/mol, respectively, at an overall PEG mole fraction of 0.010 and 4.2, 1.4, 0.5 and 0.0 N/m, respectively, at an overall PEG mole fraction of 0.075. These results are consistent with theory, which predicts that the elastic modulus is constant in the mushroom regime and decreases with PEG molecular weight to the inverse 3/5 power in the brush regime. Additionally, these results are consistent with inertial cavitation studies, which revealed that increasing PEG molecular weight has little to no effect on inethe rtial cavitation threshold in the mushroom regime, but that increasing PEG molecular weight decreases inertial cavitation markedly in the brush regime. We conclude that the design and synthesis of microbubbles with a prescribed resonance frequency is attainable by tuning PEG composition and molecular weight.

Ultra-trace analysis of illicit drugs from transfer of an electrostatic lift.

This article introduces a method of collecting and analysing drug residues that integrates both electrostatic lifting and nanomanipulation-coupled to nanospray ionization mass spectrometry. The application of this hyphenated technique exhibits a useful means of collection and extraction of drug residues with ease and efficiency along with decreased limits of detection. From this method, it will be shown how increased sensitivity of analysis and lower limits of detection for drug analysis can be achieved. The same principles that allow lifting of dust prints by electrostatic lifting can be applied to lifting drug residues. Probing of the drug residues by nanomanipulation occurs directly from the lift, which provides a great platform for extraction. Nanomanipulation-coupled to nanospray ionization-mass spectrometry has been used for the extraction of trace analytes in previous experiments and is known as a very sensitive technique for the detection of ultra-trace residue. This method will demonstrate the electrostatic lifting of drug residue particles from a surface followed by extraction and ionization with nanomanipulation-nanospray ionization. The utility of this novel methodology allows for a more productive analysis when presented with ultra-trace amounts of sample.