Predicting gene expression using morphological cell responses to nanotopography

Histone demethylase KDM3B mediates matrix stiffness-induced osteogenic differentiation of adipose-derived stem cells

Biomechanical signals in the extracellular niche are considered promising for programming the lineage specification of stem cells. Recent studies have reported that biomechanics, such as the microstructure of nanomaterials, can induce adipose-derived stem cells (ASCs) to differentiate into osteoblasts, mediating gene regulation at the epigenetic level. Therefore, in this study, transcriptome expression levels of histone demethylases in ASCs were screened after treatment with different matrix stiffnesses, and histone lysine demethylase 3B (KDM3B) was found to promote osteogenic differentiation of ASCs in response to matrix stiffness, indicating a positive modulatory effect on this biological process. ASCs exhibited widespread and polygonal shapes with a distinct bundle-like expression of vinculin parallel to the axial cytoskeleton along the cell margins on the stiff matrix rather than round shapes with a smeared and shorter expression on the soft matrix. Comparatively rigid polydimethylsiloxane material directed ASCs into an osteogenic phenotype in inductive culture media via the upregulation of osteocalcin, alkaline phosphatase, and runt-related transcription factor 2. Treatment with KDM3B-siRNA decreased the expression of osteogenic differentiation markers and impaired mitochondrial dynamics and mitochondrial membrane potential. These results illustrate the critical role of KDM3B in the biomechanics-induced osteogenic commitment of ASCs and provide new avenues for the further application of stem cells as potential therapeutics for bone regeneration.

Dynamic regulation of stem cell adhesion and differentiation on degradable piezoelectric poly (L-lactic acid) (PLLA) nanofibers

Degradable piezoelectric materials possess significant potential for application in the realm of bone tissue regeneration. However, the correlation between cell regulation mechanisms and the dynamic variation caused by material degradation has not been explained, hindering the optimization of material design and its in vivo application. Herein, piezoelectric poly (L-lactic acid) (PLLA) nanofibers with different molecular weights (MW) were fabricated, and the effects of their piezoelectric properties, structural morphology, and material products during degradation on the adhesion and osteogenic differentiation of mesenchymal stem cells (MSCs) were investigated. Our results demonstrated that cell adhesion-mediated piezoelectric stimulation could significantly enhance cell spreading, cell orientation, and upregulate the expression of calmodulin, which further triggers downstream signaling cascade to regulate osteogenic differentiation markers of type I collagen and runt-related transcription factor 2. Additionally, during the degradation of the nanofibers, the piezoelectric properties of PLLA weakened, the fibrous structure gradually diminished, and pH levels in the vicinity decreased, which resulting in reduced osteogenic differentiation capability of MSCs. However, nanofibers with higher MW (280 kDa) have the ability to maintain the fibrous morphology and piezoelectricity for a longer time, which can regulate the osteogenic differentiation of stem cells for more than 4 weeks. These findings have provide a new insight to correlate cell behavior with MW and the biodegradability of piezopolymers, which revealed an active method for cell regulation through material optimization for bone tissue engineering in near future.

Deep Learning-Based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology

Purpose

Genome-wide association studies have recently uncovered many loci associated with variation in intraocular pressure (IOP). Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs) and thus, IOP regulation.

Design

Experimental study.

Subjects

Primary TMCs collected from human donors.

Methods

Sixty-two genes at 55 loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a 5-channel fluorescent cell staining protocol. A convolutional neural network was trained to distinguish between gene knockout and normal control cell images. The area under the receiver operator curve (AUC) metric was used to quantify morphological variation in gene knockouts to identify potential pathological perturbations.

Main Outcome Measures

Degree of morphological variation as measured by deep learning algorithm accuracy of differentiation from normal controls.

Results

Cells where LTBP2 or BCAS3 had been perturbed demonstrated the greatest morphological variation from normal TMCs (AUC 0.851, standard deviation [SD] 0.030; and AUC 0.845, SD 0.020, respectively). Of 7 multigene loci, 5 had statistically significant differences in AUC (P < 0.05) between genes, allowing for pathological gene prioritization. The mitochondrial channel most frequently showed the greatest degree of morphological variation (33.9% of cell lines).

Conclusions

We demonstrate a robust method for functionally interrogating genome-wide association signals using high-throughput microscopy and AI. Genetic variations inducing marked morphological variation can be readily identified, allowing for the gene-based dissection of loci associated with complex traits.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Hierarchically Assembled Nanofiber Scaffold Guides Long Bone Regeneration by Promoting Osteogenic/Chondrogenic Differentiation of Endogenous Mesenchymal Stem Cells

Critical-sized segmental long bone defects represent a challenging clinical dilemma in the management of battlefield and trauma-related injuries. The residual bone marrow cavity of damaged long bones contains many bone marrow mesenchymal stem cells (BMSCs), which provide a substantial source of cells for bone repair. Thus, a three-dimensional (3D) vertically aligned nanofiber scaffold (VAS) is developed with long channels and large pore size. The pore of VAS toward the bone marrow cavity after transplantation, enables the scaffolds to recruit BMSCs from the bone marrow cavity to the defect area. In vivo, it is found that VAS can significantly shorten gap distance and promote new bone formation compared to the control and collagen groups after 4 and 8 weeks of implantation. The single-cell sequencing results discovered that the 3D nanotopography of VAS can promote BMSCs differentiation to chondrocytes and osteoblasts, and up-regulate related gene expression, resulting in enhancing the activities of bone regeneration, endochondral ossification, bone trabecula formation, bone mineralization, maturation, and remodeling. The Alcian blue and bone morphogenetic protein 2 (BMP-2) immunohistochemical staining verified significant cartilage formation and bone formation in the VAS group, corresponding to the single-cell sequencing results. The study can inspire the design of next-generation scaffolds for effective long-bone regeneration is expected by the authors.

Bioprinting Soft 3D Models of Hematopoiesis using Natural Silk Fibroin-Based Bioink Efficiently Supports Platelet Differentiation

Hematopoietic stem and progenitor cells (HSPCs) continuously generate platelets throughout one's life. Inherited Platelet Disorders affect ≈ 3 million individuals worldwide and are characterized by defects in platelet formation or function. A critical challenge in the identification of these diseases lies in the absence of models that facilitate the study of hematopoiesis ex vivo. Here, a silk fibroin-based bioink is developed and designed for 3D bioprinting. This bioink replicates a soft and biomimetic environment, enabling the controlled differentiation of HSPCs into platelets. The formulation consisting of silk fibroin, gelatin, and alginate is fine-tuned to obtain a viscoelastic, shear-thinning, thixotropic bioink with the remarkable ability to rapidly recover after bioprinting and provide structural integrity and mechanical stability over long-term culture. Optical transparency allowed for high-resolution imaging of platelet generation, while the incorporation of enzymatic sensors allowed quantitative analysis of glycolytic metabolism during differentiation that is represented through measurable color changes. Bioprinting patient samples revealed a decrease in metabolic activity and platelet production in Inherited Platelet Disorders. These discoveries are instrumental in establishing reference ranges for classification and automating the assessment of treatment responses. This model has far-reaching implications for application in the research of blood-related diseases, prioritizing drug development strategies, and tailoring personalized therapies.

xSiGra: Explainable model for single-cell spatial data elucidation

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multi-channel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multi-modal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of Grad-CAM component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within the complex cellular communications.

Evaluating batch correction methods for image-based cell profiling

High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmarked seven high-performing scRNA-seq batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different scenarios with varying complexity, and we found that Harmony, a mixture-model based method, consistently outperformed the other tested methods. Our proposed framework, benchmark, and metrics can additionally be used to assess new batch correction methods in the future. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.

A Perforated Plate-Based Cell Showering Device for Uniform Cell Distribution over Various Culture Substrates

Cell distribution is one of the primary factors that can affect cell morphology and behaviors, as it determines cell-cell interactions. Despite the importance of cell distribution, the seeding process of in vitro cell culture still highly relies on the traditional method using manual pipetting. Because manual pipetting cannot ensure a uniform cell distribution and has the possibility of compromising experimental reproducibility, an accurate and systemic seeding method that enables uniform cell seeding over versatile culture substrates is required. Here, we developed a perforated plate-based cell seeding device called the CellShower, which enabled uniform cell seeding over a large area of cell culture substrates. The working principles of the CellShower are based on the laminar filling flow and capillary force in microfluidics, and the design of the CellShower was optimized with numerical simulations. The versatility of the CellShower in view of uniform cell seeding was demonstrated by applying it to various types of culture substrates from a conventional culture dish to culture substrates having nanotopography, porous structures, and 3D concave structures. The CellShower and its operating principles are expected to contribute to enhancing the accuracy and reproducibility of biological experiments.

NanoIEA: A Nanopatterned Interdigitated Electrode Array-Based Impedance Assay for Real-Time Measurement of Aligned Endothelial Cell Barrier Functions

A nanopatterned interdigitated electrode array (nanoIEA)-based impedance assay is developed for quantitative real-time measurement of aligned endothelial cell (EC) barrier functions in vitro. A bioinspired poly(3,4-dihydroxy-L-phenylalanine) (poly (l-DOPA)) coating is applied to improve the human brain EC adhesion onto the Nafion nanopatterned surfaces. It is found that a poly (l-DOPA)-coated Nafion grooved nanopattern makes the human brain ECs orient along the nanopattern direction. Aligned human brain ECs on Nafion nanopatterns exhibit increased expression of genes encoding tight and adherens junction proteins. Aligned human brain ECs also have enhanced impedance and resistance versus unaligned ones. Treatment with a glycogen synthase kinase-3 inhibitor (GSK3i) further increases impedance and resistance, suggesting synergistic effects occur on the cell-cell tightness of in vitro human brain ECs via a combination of anisotropic matrix nanotopography and GSK3i treatment. It is found that this enhanced cell-cell tightness of the combined approach is accompanied by increased expression of claudin protein. These data demonstrate that the proposed nanoIEA assay integrated with poly (l-DOPA)-coated Nafion nanopatterns and interdigitated electrode arrays can make not only biomimetic aligned ECs, but also enable real-time measurement of the enhanced barrier functions of aligned ECs via tighter cell-cell junctions.

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image

Inferring gene expressions from histopathological images has long been a fascinating yet challenging task, primarily due to the substantial disparities between the two modality. Existing strategies using local or global features of histological images are suffering model complexity, GPU consumption, low interpretability, insufficient encoding of local features, and over-smooth prediction of gene expressions among neighboring sites. In this paper, we develop TCGN (Transformer with Convolution and Graph-Node co-embedding method) for gene expression estimation from H&E-stained pathological slide images. TCGN comprises a combination of convolutional layers, transformer encoders, and graph neural networks, and is the first to integrate these blocks in a general and interpretable computer vision backbone. Notably, TCGN uniquely operates with just a single spot image as input for histopathological image analysis, simplifying the process while maintaining interpretability. We validate TCGN on three publicly available spatial transcriptomic datasets. TCGN consistently exhibited the best performance (with median PCC 0.232). TCGN offers superior accuracy while keeping parameters to a minimum (just 86.241 million), and it consumes minimal memory, allowing it to run smoothly even on personal computers. Moreover, TCGN can be extended to handle bulk RNA-seq data while providing the interpretability. Enhancing the accuracy of omics information prediction from pathological images not only establishes a connection between genotype and phenotype, enabling the prediction of costly-to-measure biomarkers from affordable histopathological images, but also lays the groundwork for future multi-modal data modeling. Our results confirm that TCGN is a powerful tool for inferring gene expressions from histopathological images in precision health applications.

ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning

Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies.

Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.

Reversing pathological cell states: the road less travelled can extend the therapeutic horizon

Acquisition of omics data advances at a formidable pace. Yet, our ability to utilize these data to control cell phenotypes and design interventions that reverse pathological states lags behind. Here, we posit that cell states are determined by core networks that control cell-wide networks. To steer cell fate decisions, core networks connecting genotype to phenotype must be reconstructed and understood. A recent method, cell state transition assessment and regulation (cSTAR), applies perturbation biology to quantify causal connections and mechanistically models how core networks influence cell phenotypes. cSTAR models are akin to digital cell twins enabling us to purposefully convert pathological states back to physiologically normal states. While this capability has a range of applications, here we discuss reverting oncogenic transformation.

Cyclic stretch modulates the cell morphology transition under geometrical confinement by covalently immobilized gelatin

Fibroblasts geometrically confined by photo-immobilized gelatin micropatterns were subjected to cyclic stretch on the silicone elastomer. By using covalently micropatterned surfaces, the cell morphologies such as cell area and length were quantitatively investigated under a cyclic stretch for 20 hours. The mechanical forces did not affect the cell growth but significantly altered the cellular morphology on both non-patterned and micropatterned surfaces. It was found that cells on non-patterns showed increasing cell length and decreasing cell area under the stretch. The width of the strip micropatterns provided a different extent of contact guidance for fibroblasts. The highly extended cells on the 10 μm pattern under static conditions would perform a contraction behavior once treated by cyclic stretch. In contrast, cells with a low extension on the 2 μm pattern kept elongating according to the micropattern under the cyclic stretch. The vertical stretch induced an increase in cell area and length more than the parallel stretch in both the 10 μm and 2 μm patterns. These results provided new insights into cell behaviors under geometrical confinement in a dynamic biomechanical environment and may guide biomaterial design for tissue engineering in the future.

Micropipette-assisted atomic force microscopy for single-cell 3D manipulations and nanomechanical measurements

Mechanical cues play a crucial role in regulating physiological and pathological processes, and atomic force microscopy (AFM) has become an important and standard tool for measuring the mechanical properties of single cells. In particular, providing a capability to manipulate cells in a three-dimensional (3D) space benefits enhancing the applications of AFM measurements in cell biology. Here, we present the complementary integration of AFM and micropipette micromanipulation, which allows precise 3D manipulations and nanomechanical measurements of single living cells. A micropipette micromanipulation system under the guidance of optical microscopy was established to isolate single living cells, and polydimethylsiloxane (PDMS) micropillar substrates were used to physically immobilize the isolated living cells for downstream AFM detection. The viscoelastic properties (Young's modulus, relaxation time, viscosity) of cells were quantitatively measured by AFM-based indentation assay. The effectiveness of micropipette-assisted AFM in single-cell analysis was confirmed on both living animal suspended cells and living animal adherent cells, showing dramatic changes in cell mechanics in different states and revealing the dynamics of single cells grown on micropillar arrays. The study demonstrates the great potential of a micropipette to aid AFM in single-cell manipulations for better accessing the mechanical cues involved in cellular processes, which will allow additional studies of single-cell mechanics and will benefit the field of mechanobiology.

Development of an integrin αv-based universal marker, capable of both prediction and direction of stem cell fate

Integrin-mediated focal adhesion (FA) and subsequent cytoskeletal reorganization influence cell morphology, migration, and ultimately cell fate. Previous studies have used various patterned surfaces with defined macroscopic cell shapes or nanoscopic FA distributions to explore how different substrates affect the fate of human bone marrow mesenchymal stem cells (BMSCs). However, there is currently no straightforward relationship between BMSC cell fates induced by patterned surfaces and FA distribution substrates. In this study, we conducted single-cell image analysis of integrin αv-mediated FA and cell morphological features of BMSCs during biochemically induced differentiation. This enabled the identification of distinct FA features that can discriminate between osteogenic and adipogenic differentiation, demonstrating that integrin αv-mediated focal adhesion (FA) can be used as a non-invasive biomarker for real time observation. Based on these results, we developed an organized microscale fibronectin (FN) patterned surface where the fate of BMSC could be precisely manipulated by these FA features. Notably, even in the absence of any biochemical inducers, such as those contained in the differentiation medium, BMSCs cultured on these FN patterned surfaces exhibited upregulation of differentiation markers comparable to BMSCs cultured using conventional differentiation methods. Therefore, the present study reveals the application of these FA features as universal markers not only for predicting differentiation status, but also for regulating cell fate by precisely controlling the FA features with a new cell culture platform. STATEMENT OF SIGNIFICANCE: Although the effects of material physiochemical properties on cell morphology and subsequent cell fate decisions have been extensively studied, a simple yet intuitive correlation between cellular features and differentiation remains unavailable. We present a single cell image-based strategy for predicting and directing stem cell fate. By using a specific integrin isoform, integrin αv, we identified distinct geometric features that can be used as a marker for discriminating between osteogenic and adipogenic differentiation in real-time. From these data, new cell culture platforms capable of regulating cell fate by precisely controlling FA features and cell area can be developed.

A review on deep learning applications in highly multiplexed tissue imaging data analysis

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Reconfigurable scaffolds for adaptive tissue regeneration

Tissue engineering and regenerative medicine have offered promising alternatives for clinical treatment of body tissue traumas, losses, dysfunctions, or diseases, where scaffold-based strategies are particularly popular and effective. Over the decades, scaffolds for tissue regeneration have been remarkably evolving. Nevertheless, conventional scaffolds still confront grand challenges in bio-adaptions in terms of both tissue-scaffold and cell-scaffold interplays, for example complying with complicated three-dimensional (3D) shapes of biological tissues and recapitulating the ordered cell regulation effects of native cell microenvironments. Benefiting from the recent advances in "intelligent" biomaterials, reconfigurable scaffolds have been emerging, demonstrating great promise in addressing the bio-adaption challenges through altering their macro-shapes and/or micro-structures. This mini-review article presents a brief overview of the cutting-edge research on reconfigurable scaffolds, summarizing the materials for forming reconfigurable scaffolds and highlighting their applications for adaptive tissue regeneration. Finally, the challenges and prospects of reconfigurable scaffolds are also discussed, shedding light on the bright future of next-generation reconfigurable scaffolds with upgrading adaptability.

Modularized dynamic cell culture platform for efficient production of extracellular vesicles and sequential analysis

Extracellular vesicles (EVs) are nanometer-sized particles naturally secreted by cells for intercellular communication that encapsulate bioactive cargo, such as proteins and RNA, with a lipid bilayer. Tumor cell-derived EVs (tdEVs) are particularly promising biomarkers for cancer research because their contents reflect the cell of origin. In most studies, tdEVs have been obtained from cancer cells cultured under static conditions, thus lacking the ability to recapitulate the microenvironment of cells in vivo. Recent developments in perfusable cell culture systems have allowed oxygen and a nutrient gradient to mimic the physiological and cellular microenvironment. However, as these systems are perfused by circulating the culture medium within the unified structure, independently harvesting cells and EVs at each time point for analysis presents a limitation. In this study, a modularized cell culture system is designed for the perfusion and real-time collection of EVs. The system consists of three detachable chambers, one each for fresh medium, cell culture, and EV collection. The fresh medium flows from the medium chamber to the culture chamber at a flow rate controlled by the hydraulic pressure injected with a syringe pump. When the culture medium containing EVs exceeds a certain volume within the chamber, it overflows into the collection chamber to harvest EVs. The compact and modularized chambers are highly interoperable with conventional cell culture modalities used in the laboratory, thus enabling various EV-based assays.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

Sequencing-based spatial transcriptomics (ST) is an emerging technology to study in situ gene expression patterns at the whole-genome scale. Currently, ST data analysis is still complicated by high technical noises and low resolution. In addition to the transcriptomic data, matched histopathological images are usually generated for the same tissue sample along the ST experiment. The matched high-resolution histopathological images provide complementary cellular phenotypical information, providing an opportunity to mitigate the noises in ST data. We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST (TIST), which enables the identification of spatial clusters (SCs) and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images. TIST devises a histopathological feature extraction method based on Markov random field (MRF) to learn the cellular features from histopathological images, and integrates them with the transcriptomic data and location information as a network, termed TIST-net. Based on TIST-net, SCs are identified by a random walk-based strategy, and gene expression patterns are enhanced by neighborhood smoothing. We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods. Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios. TIST is available at http://lifeome.net/software/tist/ and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.

Open Innovation as the Catalyst in the Personalized Medicine to Personalized Digital Medicine Transition

Personalized medicine (PM) bridges several disciplines for understanding and addressing prevalent, complex, or rare situations in human health (e.g., complex phenotyping, risk stratification, etc.); therefore, digital and technological solutions have been integrated in the field to boost innovation and new knowledge generation. The open innovation (OI) paradigm proposes a method by which to respectfully manage disruptive change in biomedical organizations, as experienced by many organizations during digital transformation and the COVID-19 pandemic. In this article, we focus on how this paradigm has catalyzed the transition from PM to personalized digital medicine in a large-volume research hospital. Methods, challenges, and results are discussed. This case study is an endeavor to confirm that OI strategies could help manage urgent needs from the healthcare environment, while achieving sustainability-oriented, accountable innovation.

Microstructured collagen films for 3D corneal stroma modelling

PURPOSE/AIM

Corneal injury is a major cause of impaired vision around the globe. The fine structure of the corneal stroma plays a pivotal role in the phenotype and behavior of the embedded cells during homeostasis and healing after trauma or infection. In order to study healing processes in the cornea, it is important to create culture systems that functionally mimic the natural environment.

MATERIALS AND METHODS

Collagen solution was vitrified on top of a grated film to achieve thin collagen films with parallel microgrooves. Keratocytes (corneal stromal cells) were cultured on the films either as a single layer or as stacked layers of films and cells. SEM and F-actin staining were used to analyze the pattern transference onto the collagen and the cell orientation on the films. Cell viability was analyzed with MTS and live/dead staining. Keratocytes, fibroblasts, and myofibroblasts were cultured to study the pattern's effect on phenotype.

RESULTS

A microstructured collagen film-based culture system that guides keratocytes (stromal cells) to their native, layerwise perpendicular orientation in 3D and that can support fibroblasts and myofibroblasts was created. The films are thin and transparent enough to observe cells at least three layers deep. The cells maintain viability in 2D and 3D cultures and the films can support fibroblast and myofibroblast phenotypes.

CONCLUSIONS

The films provide an easily reproducible stroma model that maintains high cell viability and improves the preservation of the keratocyte phenotype in keratocytes that are differentiated to fibroblasts.

An Appearance Data-Driven Model Visualizes Cell State and Predicts Mesenchymal Stem Cell Regenerative Capacity

Mesenchymal stem cells (MSCs) are widely used in treating various diseases. However, lack of a reliable evaluation approach to characterize the potency of MSCs has dampened their clinical applications. Here, a function-oriented mathematical model is established to evaluate and predict the regenerative capacity (RC) of MSCs. Processed by exhaustive testing, the model excavates four optimal fitted indices, including nucleus roundness, nucleus/cytoplasm ratio, side-scatter height, and ERK1/2 from the given index combinations. Notably, three of them except ERK1/2 are cell appearance-associated features. The predictive power of the model is validated via screening experiments of these indices by predicting the RC of newly enrolled and chemical inhibitor-treated MSCs. Further RNA-sequencing analysis reveals that cell appearance-based indices may serve as major indicators to visualize the results of integration-weighted signals in and out of cells and reflect MSC stemness. In general, this study proposes an appearance data-driven predictive model for the RC and stemness of MSCs.

Delivering Mechanical Stimulation to Cells: State of the Art in Materials and Devices Design

Biochemical signals, such as growth factors, cytokines, and transcription factors are known to play a crucial role in regulating a variety of cellular activities as well as maintaining the normal function of different tissues and organs. If the biochemical signals are assumed to be one side of the coin, the other side comprises biophysical cues. There is growing evidence showing that biophysical signals, and in particular mechanical cues, also play an important role in different stages of human life ranging from morphogenesis during embryonic development to maturation and maintenance of tissue and organ function throughout life. In order to investigate how mechanical signals influence cell and tissue function, tremendous efforts have been devoted to fabricating various materials and devices for delivering mechanical stimuli to cells and tissues. Here, an overview of the current state of the art in the design and development of such materials and devices is provided, with a focus on their design principles, and challenges and perspectives for future research directions are highlighted.

Recyclable Periodic Nanostructure Formed by Sublimable Liquid Crystals for Robust Cell Alignment

We demonstrate a facile method to fabricate a recyclable cell-alignment scaffold using nanogrooves based on sublimable liquid crystal (LC) material. Randomly and uniaxially arranged smectic LC structures are obtained, followed by sublimation and recondensation processes, which directly produce periodic nanogrooves with dimensions of a couple of hundreds of nanometers. After treatment with osmium tetroxide (OsO₄), the nanogroove can serve as a scaffold to efficiently induce directed cell growth without causing cytotoxicity, and it can be used repeatedly. Together, various cell types are applied to the nanogroove, proving the scaffold's broad applicability. Depending on the nanotopography of the LC structures, cells exhibit different morphologies and gene expression patterns, compared to cells on standard glass substrates, according to microscopic observation and qPCR. Furthermore, cell sheets can be formed, which consist of oriented cells that can be repeatedly formed and transferred to other substrates, while maintaining its organization. We believe that our cell-aligning scaffold may pave the way for the soft material field to bioengineering, which can involve fundamentals in cell behavior and function, as well as applications for regenerative medicine.

Leveraging substrate stiffness to promote stem cell asymmetric division via mechanotransduction-polarity protein axis and its Bayesian regression analysis

Asymmetric division of stem cells is an evolutionarily conserved process in multicellular organisms responsible for maintaining cellular fate diversity. Symmetric-asymmetric division pattern of mesenchymal stem cells (MSC) is regulated by both biochemical and biophysical cues. However, modulation of mechanotransduction pathway by varying scaffold properties and their adaptation to control stem cell division fate is not widely established. In present study, we explored the interplay between the mechanotrasduction pathway and polarity protein complex in stem cell asymmetry under varied biophysical stimuli. We hypothesize that variation of scaffold stiffness will impart mechanical stimulus and control the cytoskeleton assembly through RhoA, which will lead to further downstream activation of polarity-related cell signalling and asymmetric division of MSC. To establish the hypothesis, umbilical cord derived MSC were cultured on PCL/collagen scaffolds with varied stiffness and expressions of several important genes (viz. YAP, TAZ, LATS1, LATS2, Par3, Par6, PRKC1 (homolog of aPKC) and RhoA) and biomarkers (viz. YAP, TAZ, F-actin, Numb) were assessed. SVM polarity index was employed to understand the polarization status of the MSC cultured on varied scaffold stiffness. Further, the Bayesian logistic regression model was employed for classifying the asymmetric division of MSC cultured on different scaffold stiffness which showed 91% accuracy. Present study emphasizes the vital role of scaffold properties in modulating the mechanotransduction signalling pathway of MSC and provides mechanistic basis for adopting facile method to control stem cell division pattern towards improving tissue engineering outcome.

Comprehensive multiplexed superfusion system enables physiological emulation in cell culture: exemplification by persistent circadian entrainment

Cells and tissues are routinely cultured in vitro for biological research with findings being extrapolated to their host organ and tissue function. However, most samples are cultured and studied in unphysiological environments, without temporal variation in the biochemical cues that are ubiquitous in vivo. The artificiality of these conditions undermines the predictive value of cell culture studies. We ascribe the prevalence of this suboptimal culture methodology to the lack of practical continuous flow systems that are economical and robust. Here, we design and implement an expandable multiplexed flow system for cell culture superfusion. By expanding on the concept of the planar peristaltic pump, we fabricated a highly compact and multiplexed pump head with up to 48 active pump lines. The pump is incorporated into a custom, open-top superfusion system configured for conventional multi-well culture plates. We then demonstrated the utility of the system for in vitro circadian entrainment using a daily cortisol pulse, generating a sustained circadian amplitude that is essential for physiological emulation and chrono-pharmacological studies. The multiplexed pump is complemented by a package of fluidic interconnection and management methods enabling user-friendly and scalable operation. Collectively, the suite of technologies provides a much-needed improvement in physiological emulation to support the predictive value of in vitro biomedical and biological research.

The cellular response to plasma membrane disruption for nanomaterial delivery

Delivery of nanomaterials into cells is of interest for fundamental cell biological research as well as for therapeutic and diagnostic purposes. One way of doing so is by physically disrupting the plasma membrane (PM). Several methods that exploit electrical, mechanical or optical cues have been conceived to temporarily disrupt the PM for intracellular delivery, with variable effects on cell viability. However, apart from acute cytotoxicity, subtler effects on cell physiology may occur as well. Their nature and timing vary with the severity of the insult and the efficiency of repair, but some may provoke permanent phenotypic alterations. With the growing palette of nanoscale delivery methods and applications, comes a need for an in-depth understanding of this cellular response. In this review, we summarize current knowledge about the chronology of cellular events that take place upon PM injury inflicted by different delivery methods. We also elaborate on their significance for cell homeostasis and cell fate. Based on the crucial nodes that govern cell fitness and functionality, we give directions for fine-tuning nano-delivery conditions.

Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation

Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e., osteogenic convolutional neural network (OCNN), to quantitatively measure the osteogenic differentiation of rat bone marrow mesenchymal stem cells (rBMSCs). rBMSCs stained with F-actin and DAPI during early differentiation (day 0, 1, 4, and 7) were captured using laser confocal scanning microscopy to train OCNN. As a result, OCNN successfully distinguished differentiated cells at a very early stage (24 h) with a high area under the curve (AUC) (0.94 ± 0.04) and correlated with conventional biochemical markers. Meanwhile, OCNN exhibited better prediction performance compared with the single morphological parameters and support vector machine. Furthermore, OCNN successfully predicted the dose-dependent effects of small-molecule osteogenic drugs and a cytokine. OCNN-based online learning models can further recognize the osteogenic differentiation of rBMSCs cultured on several material surfaces. Hence, this study initially demonstrated the foreground of OCNN in osteogenic drug and biomaterial screening for next-generation tissue engineering and stem cell research.

Integrins, cadherins and channels in cartilage mechanotransduction: perspectives for future regeneration strategies

Articular cartilage consists of hyaline cartilage, is a major constituent of the human musculoskeletal system and has critical functions in frictionless joint movement and articular homoeostasis. Osteoarthritis (OA) is an inflammatory disease of articular cartilage, which promotes joint degeneration. Although it affects millions of people, there are no satisfying therapies that address this disease at the molecular level. Therefore, tissue regeneration approaches aim at modifying chondrocyte biology to mitigate the consequences of OA. This requires appropriate biochemical and biophysical stimulation of cells. Regarding the latter, mechanotransduction of chondrocytes and their precursor cells has become increasingly important over the last few decades. Mechanotransduction is the transformation of external biophysical stimuli into intracellular biochemical signals, involving sensor molecules at the cell surface and intracellular signalling molecules, so-called mechano-sensors and -transducers. These signalling events determine cell behaviour. Mechanotransducing ion channels and gap junctions additionally govern chondrocyte physiology. It is of great scientific and medical interest to induce a specific cell behaviour by controlling these mechanotransduction pathways and to translate this knowledge into regenerative clinical therapies. This review therefore focuses on the mechanotransduction properties of integrins, cadherins and ion channels in cartilaginous tissues to provide perspectives for cartilage regeneration.

Modification of the surface nanotopography of implant devices: A translational perspective

There is an increasing need for the development of superior, safe, and more sophisticated implants, especially as our society historically has been moving towards an increasingly aging population. Currently, most research is being focused on the next generation of advanced medical implants, that are not only biocompatible but have modified surfaces that direct specific immunomodulation at cellular level. While there is a plethora of information on cell-surface interaction and how surfaces can be nanofabricated at research level, less is known about how the academic knowledge has been translated into clinical trials and commercial technologies. In this review, we provide a clinical translational perspective on the use of controlled physical surface modification of medical implants, presenting an analysis of data acquired from clinical trials and commercial products. We also evaluate the state-of-the-art of nanofabrication techniques that are being applied for implant surface modification at a clinical level. Finally, we identify some current challenges in the field, including the need of more advanced nanopatterning techniques, the comparatively small number of clinical trials and comment on future avenues to be explored for a successful clinical translation.

The future of bone regeneration: integrating AI into tissue engineering

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

Micro- and nanoscale biophysical cues for cardiovascular disease therapy

After cardiovascular injury, numerous pathological processes adversely impact the homeostatic function of cardiomyocyte, macrophage, fibroblast, endothelial cell, and vascular smooth muscle cell populations. Subsequent malfunctioning of these cells may further contribute to cardiovascular disease onset and progression. By modulating cellular responses after injury, it is possible to create local environments that promote wound healing and tissue repair mechanisms. The extracellular matrix continuously provides these mechanosensitive cell types with physical cues spanning the micro- and nanoscale to influence behaviors such as adhesion, morphology, and phenotype. It is therefore becoming increasingly compelling to harness these cell-substrate interactions to elicit more native cell behaviors that impede cardiovascular disease progression and enhance regenerative potential. This review discusses recent in vitro and preclinical work that have demonstrated the therapeutic implications of micro- and nanoscale biophysical cues on cell types adversely affected in cardiovascular diseases - cardiomyocytes, macrophages, fibroblasts, endothelial cells, and vascular smooth muscle cells.

Red blood cell phenotyping from 3D confocal images using artificial neural networks

The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fine shape details from confocal microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease, namely hereditary spherocytosis. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification. The results show the relation between the particular genetic mutation causing the disease and the shape profile. With the obtained 3D phenotypes, we suggest our method for diagnostics and theragnostics of blood diseases. Besides the application employed in this study, our algorithms can be easily adapted for the 3D shape phenotyping of other cell types and extend their use to other applications, such as industrial automated 3D quality control.

Peak force tapping atomic force microscopy for advancing cell and molecular biology

The advent of atomic force microscopy (AFM) provides an exciting tool to detect molecular and cellular behaviors under aqueous conditions. AFM is able to not only visualize the surface topography of the specimens, but also can quantify the mechanical properties of the specimens by force spectroscopy assay. Nevertheless, integrating AFM topographic imaging with force spectroscopy assay has long been limited due to the low spatiotemporal resolution. In recent years, the appearance of a new AFM imaging mode called peak force tapping (PFT) has shattered this limit. PFT allows AFM to simultaneously acquire the topography and mechanical properties of biological samples with unprecedented spatiotemporal resolution. The practical applications of PFT in the field of life sciences in the past decade have demonstrated the excellent capabilities of PFT in characterizing the fine structures and mechanics of living biological systems in their native states, offering novel possibilities to reveal the underlying mechanisms guiding physiological/pathological activities. In this paper, the recent progress in cell and molecular biology that has been made with the utilization of PFT is summarized, and future perspectives for further progression and biomedical applications of PFT are provided.

Integrating Engineering, Automation, and Intelligence to Catalyze the Biomedical Translation of Organoids

Organoid technology has developed at an impressive speed during the past decade. Still, organoids are not widely used in practical applications as expected. It is believed that this translation can be greatly accelerated with the integration of engineering and artificial intelligence into current research practices. It is proposed that this approach is the missing link to realize key milestones in organoid technology, namely, high-throughput, homogeneous, and standardized production, automated manipulation, and intelligent monitoring, evaluation, and control via integrated on-chip instrumentation and artificial intelligence. It is suggested that organoids-on-a-chip are the ideal platform to achieve these feats. Once these techniques are established and adopted by the scientific community, the rapid translation of organoids may be seen from laboratories to the clinics and pharmaceutical industry.

Biophysical phenotyping of mesenchymal stem cells along the osteogenic differentiation pathway

Mesenchymal stem cells represent an important resource, for bone regenerative medicine and therapeutic applications. This review focuses on new advancements and biophysical tools which exploit different physical and chemical markers of mesenchymal stem cell populations, to finely characterize phenotype changes along their osteogenic differentiation process. Special attention is paid to recently developed label-free methods, which allow monitoring cell populations with minimal invasiveness. Among them, quantitative phase imaging, suitable for single-cell morphometric analysis, and nanoindentation, functional to cellular biomechanics investigation. Moreover, the pool of ion channels expressed in cells during differentiation is discussed, with particular interest for calcium homoeostasis.Altogether, a biophysical perspective of osteogenesis is proposed, offering a valuable tool for the assessment of the cell stage, but also suggesting potential physiological links between apparently independent phenomena.

Tailoring Cellular Function: The Contribution of the Nucleus in Mechanotransduction

Cells sense a variety of different mechanochemical stimuli and promptly react to such signals by reshaping their morphology and adapting their structural organization and tensional state. Cell reactions to mechanical stimuli arising from the local microenvironment, mechanotransduction, play a crucial role in many cellular functions in both physiological and pathological conditions. To decipher this complex process, several studies have been undertaken to develop engineered materials and devices as tools to properly control cell mechanical state and evaluate cellular responses. Recent reports highlight how the nucleus serves as an important mechanosensor organelle and governs cell mechanoresponse. In this review, we will introduce the basic mechanisms linking cytoskeleton organization to the nucleus and how this reacts to mechanical properties of the cell microenvironment. We will also discuss how perturbations of nucleus-cytoskeleton connections, affecting mechanotransduction, influence health and disease. Moreover, we will present some of the main technological tools used to characterize and perturb the nuclear mechanical state.

Application of FRET Biosensors in Mechanobiology and Mechanopharmacological Screening

Extensive studies have shown that cells can sense and modulate the biomechanical properties of the ECM within their resident microenvironment. Thus, targeting the mechanotransduction signaling pathways provides a promising way for disease intervention. However, how cells perceive these mechanical cues of the microenvironment and transduce them into biochemical signals remains to be answered. Förster or fluorescence resonance energy transfer (FRET) based biosensors are a powerful tool that can be used in live-cell mechanotransduction imaging and mechanopharmacological drug screening. In this review, we will first introduce FRET principle and FRET biosensors, and then, recent advances on the integration of FRET biosensors and mechanobiology in normal and pathophysiological conditions will be discussed. Furthermore, we will summarize the current applications and limitations of FRET biosensors in high-throughput drug screening and the future improvement of FRET biosensors. In summary, FRET biosensors have provided a powerful tool for mechanobiology studies to advance our understanding of how cells and matrices interact, and the mechanopharmacological screening for disease intervention.

Emerging Roles of 1D Vertical Nanostructures in Orchestrating Immune Cell Functions

Engineered nano-bio cellular interfaces driven by 1D vertical nanostructures (1D-VNS) are set to prompt radical progress in modulating cellular processes at the nanoscale. Here, tuneable cell-VNS interfacial interactions are probed and assessed, highlighting the use of 1D-VNS in immunomodulation, and intracellular delivery into immune cells-both crucial in fundamental and translational biomedical research. With programmable topography and adaptable surface functionalization, 1D-VNS provide unique biophysical and biochemical cues to orchestrate innate and adaptive immunity, both ex vivo and in vivo. The intimate nanoscale cell-VNS interface leads to membrane penetration and cellular deformation, facilitating efficient intracellular delivery of diverse bioactive cargoes into hard-to-transfect immune cells. The unsettled interfacial mechanisms reported to be involved in VNS-mediated intracellular delivery are discussed. By identifying up-to-date progress and fundamental challenges of current 1D-VNS technology in immune-cell manipulation, it is hoped that this report gives timely insights for further advances in developing 1D-VNS as a safe, universal, and highly scalable platform for cell engineering and enrichment in advanced cancer immunotherapy such as chimeric antigen receptor-T therapy.

Interactive machine learning for fast and robust cell profiling

Automated profiling of cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognitive biases and dependent on user experience. Here, we use interactive machine learning to identify the optimum cell profiling configuration that maximises quality of the cell profiling outcome. The process is guided by the user, from whom a rating of the quality of a cell profiling configuration is obtained. We use Bayesian optimisation, an established machine learning algorithm, to learn from this information and automatically recommend the next configuration to examine with the aim of maximising the quality of the processing or analysis. Compared to existing interactive machine learning tools that require domain expertise for per-class or per-pixel annotations, we rely on users’ explicit assessment of output quality of the cell profiling task at hand. We validated our interactive approach against the standard human trial-and-error scheme to optimise an object segmentation task using the standard software CellProfiler. Our toolkit enabled rapid optimisation of an object segmentation pipeline, increasing the quality of object segmentation over a pipeline optimised through trial-and-error. Users also attested to the ease of use and reduced cognitive load enabled by our machine learning strategy over the standard approach. We envision that our interactive machine learning approach can enhance the quality and efficiency of pipeline optimisation to democratise image-based cell profiling.

Targeting Tunable Physical Properties of Materials for Chronic Wound Care

Chronic wounds caused by infections, diabetes, and radiation exposures are becoming a worldwide growing medical burden. Recent progress highlighted the physical signals determining stem cell fates and bacterial resistance, which holds potential to achieve a better wound regeneration in situ. Nanoparticles (NPs) would benefit chronic wound healing. However, the cytotoxicity of the silver NPs (AgNPs) has aroused many concerns. This review targets the tunable physical properties (i.e., mechanical-, structural-, and size-related properties) of either dermal matrixes or wound dressings for chronic wound care. Firstly, we discuss the recent discoveries about the mechanical- and structural-related regulation of stem cells. Specially, we point out the currently undocumented influence of tunable mechanical and structural properties on either the fate of each cell type or the whole wound healing process. Secondly, we highlight novel dermal matrixes based on either natural tropoelastin or synthetic elastin-like recombinamers (ELRs) for providing elastic recoil and resilience to the wounded dermis. Thirdly, we discuss the application of wound dressings in terms of size-related properties (i.e., metal NPs, lipid NPs, polymeric NPs). Moreover, we highlight the cytotoxicity of AgNPs and propose the size-, dose-, and time-dependent solutions for reducing their cytotoxicity in wound care. This review will hopefully inspire the advanced design strategies of either dermal matrixes or wound dressings and their potential therapeutic benefits for chronic wounds.

Customizable, engineered substrates for rapid screening of cellular cues

Biophysical cues robustly direct cell responses and are thus important tools for in vitro and translational biomedical applications. High throughput platforms exploring substrates with varying physical properties are therefore valuable. However, currently existing platforms are limited in throughput, the biomaterials used, the capability to segregate between different cues and the assessment of dynamic responses. Here we present a multiwell array (3 × 8) made of a substrate engineered to present topography or rigidity cues welded to a bottomless plate with a 96-well format. Both the patterns on the engineered substrate and the well plate format can be easily customized, permitting systematic and efficient screening of biophysical cues. To demonstrate the broad range of possible biophysical cues examinable, we designed and tested three multiwell arrays to influence cardiomyocyte, chondrocyte and osteoblast function. Using the multiwell array, we were able to measure different cell functionalities using analytical modalities such as live microscopy, qPCR and immunofluorescence. We observed that grooves (5 μm in size) induced less variation in contractile function of cardiomyocytes. Compared to unpatterned plastic, nanopillars with 127 nm height, 100 nm diameter and 300 nm pitch enhanced matrix deposition, chondrogenic gene expression and chondrogenic maintenance. High aspect ratio pillars with an elastic shear modulus of 16 kPa mimicking the matrix found in early stages of bone development improved osteogenic gene expression compared to stiff plastic. We envisage that our bespoke multiwell array will accelerate the discovery of relevant biophysical cues through improved throughput and variety.