Inkjet-printed micro-calibration standards for ultraquantitative Raman spectral cytometry.

Herein we report the development of a cytometric analysis platform for measuring the contents of individual cells in absolute (picogram) scales; this study represents the first report of Raman-based quantitation of the absolute mass - or the total amount - of multiple endogenous biomolecules within single-cells. To enable ultraquantitative calibration, we engineered single-cell-sized micro-calibration standards of known composition by inkjet-printer deposition of biomolecular components in microarrays across the surface of silicon chips. We demonstrate clinical feasibility by characterizing the compositional phenotype of human skin fibroblast and porcine alveolar macrophage cell populations in the respective contexts of Niemann-Pick disease and drug-induced phospholipidosis: two types of lipid storage disorders. We envision this microanalytical platform as the foundation for many future biomedical applications, ranging from diagnostic assays to pathological analysis to advanced pharmaco/toxicokinetic research studies.

Processing-Size Correlations in the Preparation of Magnetic Alginate Microspheres Through Emulsification and Ionic Crosslinking.

Magnetic alginate microspheres are biocompatible due to their alginate matrix, and motion-controllable by applied magnetic fields due to their magnetic character. Therefore, they have the potential of being used as vessels to a broad variety of materials, including drugs and therapeutic agents, facilitating entry to biological systems in a relatively non-invasive manner. Here, magnetic alginate microspheres were prepared through an emulsification and ionic cross-linking process, where a mixture of alginate and iron oxide magnetic nanoparticles was initially dispersed in a continuous phase, followed by gelation of this dispersed phase into microspheres by cross-linking the dispersion with calcium ions. The resulting magnetic alginate microspheres were found to be superparamagnetic and to respond to applied magnetic fields by chain formation. The effect of shear rate, alginate concentration, and magnetic nanoparticle concentration on microsphere size was investigated with the aim to control the size of microspheres with respect to process and formulation parameters. Two of these parameters, shear rate and alginate concentration, were used to correlate experimental results with a theoretical model for the case where the dispersed phase is more viscous than the continuous phase.

Co-Delivery Polymeric Poly(Lactic-Co-Glycolic Acid) (PLGA) Nanoparticles to Target Cancer Stem-Like Cells.

Nanoparticle drug delivery has been promoted as an effective mode of delivering antineoplastic therapeutics. However, most nanoparticle designs fail to consider the multifaceted tumor microenvironment (TME) that produce pro-tumoral niches, which are often resistant to chemo- and targeted therapies. In order to target the chemoresistant cancer stem-like cells (CSCs) and their supportive TME, in this chapter we describe a nanoparticle-based targeted co-delivery that addresses the paracrine interactions between CSC and non-cancerous mesenchymal stem cells (MSCs) in the TME. Carcinoma-activated MSCs have been shown to increase the chemoresistance and metastasis of CSC. Yet their contributions to protect the CSC TME have not yet been systematically investigated in the design of nanoparticles for drug delivery. Therefore, we describe the fabrication of degradable poly(lactic-co-glycolic acid) (PLGA) nanoparticles (120-200 nm), generated with an electrospraying process that encapsulates both a conventional chemotherapeutic, paclitaxel, and a targeted tyrosine kinase inhibitor, sunitinib, to limit MSC interactions with CSC. In the 3D hetero-spheroid model that comprises both CSCs and MSCs, the delivery of sunitinib as a free drug disrupted the MSC-protected CSC stemness and migration. Therefore, this chapter describes the co-delivery of paclitaxel and sunitinib via PLGA nanoparticles as a potential targeted therapy strategy for targeting CSCs. Overall, nanoparticles can provide an effective delivery platform for targeting CSCs and their TME together. Forthcoming studies can corroborate similar combined therapies with nanoparticles to improve the killing of CSC and chemoresistant cancer cells, thereby improving treatment efficiency.

The Role of the Tumor Microenvironment in CSC Enrichment and Chemoresistance: 3D Co-culture Methods.

Cancer stem-like cells (CSC) are responsible for tumor progression, chemoresistance, recurrence, and poor outcomes in many cancers, making them critical research and therapeutic targets. One of the critical components potentiating CSC chemoresistance is the interactions between CSC and the surrounding cells in the tumor microenvironment. Our lab has developed several 3D co-culture models to study ovarian CSC interactions with stromal or immune cells found in ovarian tumor microenvironments. In this chapter, we use ovarian cancer as a model to describe the methodologies developed in our lab; however, these techniques are applicable to a wide range of cancers. First, we discuss our method for isolating CSC from heterogeneous tumors and for creating 3D self-assembled tumoroids in hanging drop plates, in either monoculture or co-culture with mesenchymal stem cells or monocytes/macrophages. We then discuss methods for analyzing these models with a focus on isolating cell-type-specific changes and mechanism investigation. Specifically, we describe lentiviral transduction and flow cytometry as established and robust methods to identify and separate each cell type for downstream analysis. We then describe methods to examine CSC functionality with transwell migration assays and colorimetric MTS-based proliferation assays. Finally, we demonstrate enzyme-linked immunosorbent assays (ELISA ) and quantitative polymerase chain reaction (qPCR) methods as mechanistic investigation tools to decouple paracrine and juxtacrine interactions. These methods have wide-reaching applications in cancer research from basic biological investigations, to drug discovery, and personalized drug screening for precision medicine.

Injectable three-dimensional tumor microenvironments to study mechanobiology in ovarian cancer.

Epithelial ovarian cancers are among the most aggressive forms of gynecological malignancies. Despite the advent of poly adenosine diphosphate-ribose polymerase (PARP) and checkpoint inhibitors, improvement to patient survival has been modest. Limited in part by clinical translation, beneficial therapeutic strategies remain elusive in ovarian cancers. Although elevated levels of extracellular proteins, including collagens, proteoglycans, and glycoproteins, have been linked to chemoresistance, they are often missing from the processes of drug- development and screening. Biophysical and biochemical signaling from the extracellular matrix (ECM) determine cellular phenotype and affect both tumor progression and therapeutic response. However, many state-of-the-art tumor models fail to mimic the complexities of the tumor microenvironment (TME) and omit key signaling components. In this article, two interpenetrating network (IPN) hydrogel scaffold platforms, comprising of alginate-collagen or agarose-collagen, have been characterized for use as 3D in vitro models of epithelial ovarian cancer ECM. These highly tunable, injection mold compatible, and inexpensive IPNs replicate the critical governing physical and chemical signaling present within the ovarian TME. Additionally, an effective and cell-friendly live-cell retrieval method has been established to recover cells post-encapsulation. Lastly, functional mechanotransduction in ovarian cancers was demonstrated by increasing scaffold stiffness within the 3D in vitro ECM models. With these features, the agarose-collagen and alginate-collagen hydrogels provide a robust TME for the study of mechanobiology in epithelial cancers. STATEMENT OF SIGNIFICANCE: Ovarian cancer is the most lethal gynecologic cancer afflicting women today. Here we present the development, characterization, and validation of 3D interpenetrating platforms to shift the paradigm in standard in vitro modeling. These models help elucidate the roles of biophysical and biochemical cues in ovarian cancer progression. The agarose-collagen and alginate-collagen interpenetrating network (IPN) hydrogels are simple to fabricate, inexpensive, and can be modified to create custom mechanical stiffnesses and concentrations of bio-adhesive motifs. Given that investigations into the roles of biophysical characteristics in ovarian cancers have provided incongruent results, we believe that the IPN platforms will be critically important to uncovering molecular drivers. We also expect these platforms to be broadly applicable to studies involving mechanobiology in solid tumors.

Surfaces with instant and persistent antimicrobial efficacy against bacteria and SARS-CoV-2.

Surfaces contaminated with bacteria and viruses contribute to the transmission of infectious diseases and pose a significant threat to global public health. Modern day disinfection either relies on fast-acting (>3-log reduction within a few minutes), yet impermanent, liquid-, vapor-, or radiation-based disinfection techniques, or long-lasting, but slower-acting, passive antimicrobial surfaces based on heavy metal surfaces, or metallic nanoparticles. There is currently no surface that provides instant and persistent antimicrobial efficacy against a broad spectrum of bacteria and viruses. In this work, we describe a class of extremely durable antimicrobial surfaces incorporating different plant secondary metabolites that are capable of rapid disinfection (>4-log reduction) of current and emerging pathogens within minutes, while maintaining persistent efficacy over several months and under significant environmental duress. We also show that these surfaces can be readily applied onto a variety of desired substrates or devices via simple application techniques such as spray, flow, or brush coating.

Personalized models of heterogeneous 3D epithelial tumor microenvironments: Ovarian cancer as a model.

Intractable human diseases such as cancers, are context dependent, unique to both the individual patient and to the specific tumor microenvironment. However, conventional cancer treatments are often nonspecific, targeting global similarities rather than unique drivers. This limits treatment efficacy across heterogeneous patient populations and even at different tumor locations within the same patient. Ultimately, this poor efficacy can lead to adverse clinical outcomes and the development of treatment-resistant relapse. To prevent this and improve outcomes, it is necessary to be selective when choosing a patient's optimal adjuvant treatment. In this review, we posit the use of personalized, tumor-specific models (TSM) as tools to achieve this remarkable feat. First, using ovarian cancer as a model disease, we outline the heterogeneity and complexity of both the cellular and extracellular components in the tumor microenvironment. Then we examine the advantages and disadvantages of contemporary cancer models and the rationale for personalized TSM. We discuss how to generate precision 3D models through careful and detailed analysis of patient biopsies. Finally, we provide clinically relevant applications of these versatile personalized cancer models to highlight their potential impact. These models are ideal for a myriad of fundamental cancer biology and translational studies. Importantly, these approaches can be extended to other carcinomas, facilitating the discovery of new therapeutics that more effectively target the unique aspects of each individual patient's TME. STATEMENT OF SIGNIFICANCE: In this article, we have presented the case for the application of biomaterials in developing personalized models of complex diseases such as cancers. TSM could bring about breakthroughs in the promise of precision medicine. The critical components of the diverse tumor microenvironments, that lead to treatment failures, include cellular- and extracellular matrix- heterogeneity, and biophysical signals to the cells. Therefore, we have described these dynamic components of the tumor microenvironments, and have highlighted how contemporary biomaterials can be utilized to create personalized in vitro models of cancers. We have also described the application of the TSM to predict the dynamic patterns of disease progression, and predict effective therapies that can produce durable responses, limit relapses, and treat any minimal residual disease.

Carcinoma-Associated Mesenchymal Stem Cells Promote Chemoresistance in Ovarian Cancer Stem Cells via PDGF Signaling.

Within the ovarian cancer tumor microenvironment, cancer stem-like cells (CSC) interact with carcinoma associated mesenchymal stem/stromal cells (CA-MSC) through multiple secreted cytokines and growth factors. These paracrine interactions have been revealed to cause enrichment of CSC and their chemoprotection; however, it is still not known if platelet-derived growth factor (PDGF) signaling is involved in facilitating these responses. In order to probe this undiscovered bidirectional communication, we created a model of ovarian malignant ascites in the three-dimensional (3D) hanging drop heterospheroid array, with CSC and CA-MSC. We hypothesized that PDGF secretion by CA-MSC increases self-renewal, migration, epithelial to mesenchymal transition (EMT) and chemoresistance in ovarian CSC. Our results indicate that PDGF signaling in the CSC-MSC heterospheroids significantly increased stemness, metastatic potential and chemoresistance of CSC. Knockdown of PDGFB in MSC resulted in abrogation of these phenotypes in the heterospheroids. Our studies also reveal a cross-talk between PDGF and Hedgehog signaling in ovarian cancer. Overall, our data suggest that when the stromal signaling via PDGF to ovarian CSC is blocked in addition to chemotherapy pressure, the tumor cells are significantly more sensitive to chemotherapy. Our results emphasize the importance of disrupting the signals from the microenvironment to the tumor cells, in order to improve response rates. These findings may lead to the development of combination therapies targeting stromal signaling (such as PDGF and Hedgehog) that can abrogate the tumorigenic, metastatic and platinum resistant phenotypes of ovarian CSC through additional investigations.

Wettability Engendered Templated Self-Assembly (WETS) for the Fabrication of Biocompatible, Polymer-Polyelectrolyte Janus Particles.

Fabrication of charged, multiphasic, polymeric micro- and nanoparticles with precise control over their composition, size, and shape is critical for developing the next generation of drug carriers for combinatorial therapies and theranostics. The addition of charged polyelectrolyte multilayers on the surface of polymeric particles can significantly improve their stability, targeting efficacy, drug-release kinetics, and their ability to encapsulate different drugs within a single particle. Many of the traditional methods for multilayer functionalization of multiphasic polymeric particles are time and energy intensive which significantly limits their scalability, and therefore therapeutic potential. In this work, we combine the bulk layer-by-layer polyelectrolyte application methodology with our previously developed technique of fabricating multiphasic polymeric particles on substrates with patterned wettability to synthesize biocompatible, monodisperse, Janus polymer-polyelectrolyte particles.

Integrated cancer tissue engineering models for precision medicine.

Tumors are not merely cancerous cells that undergo mindless proliferation. Rather, they are highly organized and interconnected organ systems. Tumor cells reside in complex microenvironments in which they are subjected to a variety of physical and chemical stimuli that influence cell behavior and ultimately the progression and maintenance of the tumor. As cancer bioengineers, it is our responsibility to create physiologic models that enable accurate understanding of the multi-dimensional structure, organization, and complex relationships in diverse tumor microenvironments. Such models can greatly expedite clinical discovery and translation by closely replicating the physiological conditions while maintaining high tunability and control of extrinsic factors. In this review, we discuss the current models that target key aspects of the tumor microenvironment and their role in cancer progression. In order to address sources of experimental variation and model limitations, we also make recommendations for methods to improve overall physiologic reproducibility, experimental repeatability, and rigor within the field. Improvements can be made through an enhanced emphasis on mathematical modeling, standardized in vitro model characterization, transparent reporting of methodologies, and designing experiments with physiological metrics. Taken together these considerations will enhance the relevance of in vitro tumor models, biological understanding, and accelerate treatment exploration ultimately leading to improved clinical outcomes. Moreover, the development of robust, user-friendly models that integrate important stimuli will allow for the in-depth study of tumors as they undergo progression from non-transformed primary cells to metastatic disease and facilitate translation to a wide variety of biological and clinical studies.

Physiologic Patient Derived 3D Spheroids for Anti-neoplastic Drug Screening to Target Cancer Stem Cells.

In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of anti-cancer therapeutics in a physiologically representative microenvironment. We outline the formation of patient derived cancer stem cell spheroids, as well as, the manipulation of these spheroids for thorough analysis following drug treatment. Specifically, we describe collection of spheroid morphology, proliferation, viability, drug toxicity, cell phenotype and cell localization data. This protocol focuses heavily on analysis techniques that are easily implemented using the 384-well hanging drop platform, making it ideal for high throughput drug screening. While we emphasize the importance of this model in ovarian cancer studies and cancer stem cell research, the 384-well platform is amenable to research of other cancer types and disease models, extending the utility of the platform to many fields. By improving the speed of personalized drug screening and the quality of screening results through easily implemented physiologically representative 3D cultures, this platform is predicted to aid in the development of new therapeutics and patient-specific treatment strategies, and thus have wide-reaching clinical impact.