Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

Organoid intelligence: Integration of organoid technology and artificial intelligence in the new era of in vitro models.

Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence. Organoids, three-dimensional miniature organ-like structures cultivated from stem cells, offer an unparalleled opportunity to simulate complex human organ systems in vitro. Through the convergence of organoid technology and AI, researchers gain the means to accelerate discoveries and insights across various disciplines. Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors, intricate cellular interactions, and dynamic responses to stimuli. This synergy empowers the development of predictive models, precise disease simulations, and personalized medicine approaches, revolutionizing our understanding of human development, disease mechanisms, and therapeutic interventions. Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling, propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.

AI-organoid integrated systems for biomedical studies and applications.

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.

Utilizing recent advances in human induced pluripotent stem cell (hiPSC) technology, nonlinear analysis and machine learning we can create novel tools to evaluate drug-induced cardiotoxicity on human cardiomyocytes. With cardiovascular disease remaining the leading cause of death globally it has become imperative to create effective and modern tools to test the efficacy and toxicity of drugs to combat heart disease. The calcium transient signals recorded from hiPSC-derived cardiomyocytes (hiPSC-CMs) are highly complex and dynamic with great degrees of response characteristics to various drug treatments. However, traditional linear methods often fail to capture the subtle variation in these signals generated by hiPSC-CMs. In this work, we integrated nonlinear analysis, dimensionality reduction techniques and machine learning algorithms for better classifying the contractile signals from hiPSC-CMs in response to different drug exposure. By utilizing extracted parameters from a commercially available high-throughput testing platform, we were able to distinguish the groups with drug treatment from baseline controls, determine the drug exposure relative to IC50 values, and classify the drugs by its unique cardiac responses. By incorporating nonlinear parameters computed by phase space reconstruction, we were able to improve our machine learning algorithm's ability to predict cardiotoxic levels and drug classifications. We also visualized the effects of drug treatment and dosages with dimensionality reduction techniques, t-distributed stochastic neighbor embedding (t-SNE). We have shown that integration of nonlinear analysis and artificial intelligence has proven to be a powerful tool for analyzing cardiotoxicity and classifying toxic compounds through their mechanistic action.

Assessing multimodal optical imaging of perfusion in burn wounds.

A critical need exists for early, accurate diagnosis of burn wound severity to help identify the course of treatment and outcome of the wound. Laser speckle imaging (LSI) is a promising blood perfusion imaging approach, but it does not account for changes in tissue optical properties that can occur with burn wounds, which are highly dynamic environments. Here, we studied optical property dynamics following burn injury and debridement and the associated impact on interpretation of LSI measurements of skin perfusion. We used spatial frequency domain imaging (SFDI) measurements of tissue optical properties to study the impact of burn-induced changes in these properties on LSI measurements. An established preclinical porcine model of burn injury was used (n = 8). SFDI and LSI data were collected from burn wounds of varying severity. SFDI measurements demonstrate that optical properties change in response to burn injury in a porcine model. We then apply theoretical modeling to demonstrate that the measured range of optical property changes can affect the interpretation of LSI measurements of blood flow, but this effect is minimal for most of the measured data. Collectively, our results indicate that, even with a dynamic burn wound environment, blood-flow measurements with LSI can serve as an appropriate strategy for accurate assessment of burn severity.

Engineering spatial-organized cardiac organoids for developmental toxicity testing.

Emerging technologies in stem cell engineering have produced sophisticated organoid platforms by controlling stem cell fate via biomaterial instructive cues. By micropatterning and differentiating human induced pluripotent stem cells (hiPSCs), we have engineered spatially organized cardiac organoids with contracting cardiomyocytes in the center surrounded by stromal cells distributed along the pattern perimeter. We investigated how geometric confinement directed the structural morphology and contractile functions of the cardiac organoids and tailored the pattern geometry to optimize organoid production. Using modern data-mining techniques, we found that pattern sizes significantly affected contraction functions, particularly in the parameters related to contraction duration and diastolic functions. We applied cardiac organoids generated from 600 µm diameter circles as a developmental toxicity screening assay and quantified the embryotoxic potential of nine pharmaceutical compounds. These cardiac organoids have potential use as an in vitro platform for studying organoid structure-function relationships, developmental processes, and drug-induced cardiac developmental toxicity.

Platelet rich plasma hydrogels promote in vitro and in vivo angiogenic potential of adipose-derived stem cells.

Despite great advances in skin wound care utilizing grafting techniques, the resulting severe scarring, deformity and ineffective vascularization remains a challenge. Alternatively, tissue engineering of new skin using patient-derived stem cells and scaffolding materials promises to greatly increase the functional and aesthetic outcome of skin wound healing. This work focused on the optimization of a polyethylene glycol modified (PEGylated) platelet-rich plasma (PRP) hydrogel for the protracted release of cytokines, growth factors, and signaling molecules and also the delivery of a provisional physical framework for stem cell angiogenesis. Freshly collected whole blood was utilized to synthesize PEGylated PRP hydrogels containing platelet concentrations ranging from 0 to 200,000 platelets/µl. Hydrogels were characterized using thromboelastography and impedance aggregometry for platelet function and were visualized using scanning electron microscopy. To assess the effects of PEGylated PRP hydrogels on cells, PRP solutions were seeded with human adipose-derived stem cells (ASCs) prior to gelation. Following 14 days of incubation in vitro, increased platelet concentrations resulted in higher ASC proliferation and vascular gene and protein expression (assessed via RT-PCR, ELISA, and immunochemistry). Using a rat skin excision model, wounds treated with PRP + ASC hydrogels increased the number of vessels in the wound by day 8 (80.2 vs. 62.6 vessels/mm2) compared to controls. In conclusion, the proposed PEGylated PRP hydrogel promoted both in vitro and transient in vivo angiogenesis of ASCs for improved wound healing. STATEMENT OF SIGNIFICANCE: Our findings support an innovative means of cellular therapy intervention to improve surgical wound healing in a normal wound model. ASCs seeded within PEGylated PRP could be an efficacious and completely autologous therapy for treating patients who have poorly healing wounds caused by vascular insufficiency, previous irradiation, or full-thickness burns. Because wound healing is a dynamic and complex process, the application of more than one growth factor with ASCs demonstrates an advantageous way of improving healing.

Plasma-Alginate Composite Material Provides Improved Mechanical Support for Stem Cell Growth and Delivery.

Blood plasma-based products have been recently utilized in different tissue engineering applications, ranging from soft tissue repair to bone regeneration. Plasma contains fibrinogen which can be converted to an insoluble fibrin-laden gel in the presence of activated thrombin. In tissue engineering, these plasma-based materials can serve either as a three-dimensional scaffold to deliver therapeutic cells in vivo or as a growth factor-rich supply for tissue regeneration. Unfortunately, plasma-based materials are often mechanically weak and easily deformed, thus limiting their usability in harsh clinical settings. Simpler methods to create sturdier plasma-based materials are therefore needed. To this end, we hypothesized that combining alginate with plasma can create a composite plasma material with improved mechanical properties. Incorporating alginate into plasma produced composite gels with increasing bulk stiffness, as measured by rheology. Specifically, the plasma-alginate composite (PAC) gels with an alginate concentration of 2.86 mg/mL were 10-fold stiffer than pure plasma gels (11 vs 112 Pa). Interestingly, gel lysis rates were unchanged despite increasing alginate concentration (lysis time approximately 50 min). Adipose-derived stem cells cultured in the stiffer PAC gels expressed stemness markers (THY1, ENG, NT5E) at levels comparable to those in the pure plasma gels. Similarly, proangiogenic factor secretion was also constant across all gel conditions. In sum, we envision this PAC gel system will extend the use of plasma gel-based therapies into more rigorous clinical applications.

PEGylated Platelet-Free Blood Plasma-Based Hydrogels for Full-Thickness Wound Regeneration.

Objective: To develop a cost-effective and clinically usable therapy to treat full-thickness skin injuries. We accomplished this by preparing a viscoelastic hydrogel using polyethylene glycol (PEG)-modified platelet-free plasma (PEGylated PFP) combined with human adipose-derived stem cells (ASCs). Approach: PEGylated PFP hydrogels were prepared by polymerizing the liquid mixture of PEG and PFP±ASCs and gelled either by adding calcium chloride (CaCl2) or thrombin. Rheological and in vitro studies were performed to assess viscoelasticity and the ability of hydrogels to direct ASCs toward a vasculogenic phenotype, respectively. Finally, a pilot study evaluated the efficacy of hydrogels±ASCs using an athymic rat full-thickness skin wound model. Results: Hydrogels prepared within the range of 11 to 27 mM for CaCl2 or 5 to 12.5 U/mL for thrombin exhibited a storage modulus of ∼62 to 87 Pa and ∼47 to 92 Pa, respectively. The PEGylated PFP hydrogels directed ASCs to form network-like structures resembling vasculature, with a fourfold increase in perivascular specific genes that were confirmed by immunofluorescent staining. Hydrogels combined with ASCs exhibited an increase in blood vessel density when applied to excisional rat wounds compared with those treated with hydrogels (110.3 vs. 95.6 BV/mm2; p < 0.05). Furthermore, ASCs were identified in the perivascular region associated with newly forming blood vessels. Innovation: This study demonstrates that PFP modified with PEG along with ASCs can be used to prepare cost-effective stable hydrogels, at the bed-side, to treat extensive skin wounds. Conclusion: These results indicate that PEGylated plasma-based hydrogels combined with ASCs may be a potential regenerative therapy for full-thickness skin wounds.

Spatial frequency domain imaging: a quantitative, noninvasive tool for in vivo monitoring of burn wound and skin graft healing.

There is a need for noninvasive, quantitative methods to characterize wound healing in the context of longitudinal investigations related to regenerative medicine. Such tools have the potential to inform the assessment of wound status and healing progression and aid the development of new treatments. We employed spatial frequency domain imaging (SFDI) to characterize the changes in optical properties of tissue during wound healing progression in a porcine model of split-thickness skin grafts and also in a model of burn wound healing with no graft intervention. Changes in the reduced scattering coefficient measured using SFDI correlated with structural changes reported by histology of biopsies taken concurrently. SFDI was able to measure spatial inhomogeneity in the wounds and predicted heterogeneous healing. In addition, we were able to visualize differences in healing rate, depending on whether a wound was debrided and grafted, versus not debrided and left to heal without intervention apart from topical burn wound care. Changes in the concentration of oxy- and deoxyhemoglobin were also quantified, giving insight into hemodynamic changes during healing.

Interlesion differences in the local photodynamic therapy response of oral cavity lesions assessed by diffuse optical spectroscopies.

Photodynamic therapy (PDT) efficacy depends on the local dose deposited in the lesion as well as oxygen availability in the lesion. We report significant interlesion differences between two patients with oral lesions treated with the same drug dose and similar light dose of 2-1[hexyloxyethyl]-2-devinylpyropheophorbide-a (HPPH)-mediated photodynamic therapy (PDT). Pre-PDT and PDT-induced changes in hemodynamic parameters and HPPH photosensitizer content, quantified by diffuse optical methods, demonstrated substantial differences between the two lesions. The differences in PDT action determined by the oxidative cross-linking of signal transducer and activator of transcription 3 (STAT3), a molecular measure of accumulated local PDT photoreaction, also showed >100-fold difference between the lesions, greatly exceeding what would be expected from the slight difference in light dose. Our results suggest diffuse optical spectroscopies can provide in vivo metrics that are indicative of local PDT dose in oral lesions.