Significance The clinical utility of mesenchymal stromal/stem cells (MSCs) in mediating immunosuppressive effects and supporting regenerative processes is broadly established. However, the inherent heterogeneity of MSCs compromises its biomedical efficacy and reproducibility. To study how cellular variation affects fate decision-making processes, we perform single-cell RNA sequencing at multiple time points during bipotential matrix-directed differentiation toward soft- and stiff tissue lineages. In this manner, we identify distinctive MSC subpopulations that are characterized by their multipotent differentiation capacity and mechanosensitivity. Also, whole-genome screening highlights TPM1 as a potent mechanotransducer of matrix signals and regulator of cell differentiation. Thus, by introducing single-cell methodologies into mechanobiology, we delineate the complexity of adult stem cell responses to extracellular cues in tissue regeneration and immunomodulation.
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The application of deep learning methods, particularly foundation models, in biological research has surged in recent years. These models can be text-based or trained on underlying biological data, especially omics data of various types. However, comparing the performance of these models consistently has proven to be a challenge due to differences in training data and downstream tasks. To tackle this problem, we developed an architecture-agnostic benchmarking approach that, instead of evaluating the models directly, leverages entity representation vectors from each model and trains simple predictive models for each benchmarking task. This ensures that all types of models are evaluated using the same input and output types. Here we focus on gene properties collected from professionally curated bioinformatics databases. These gene properties are categorized into five major groups: genomic properties, regulatory functions, localization, biological processes, and protein properties. Overall, we define hundreds of tasks based on these databases, which include binary, multi-label, and multi-class classification tasks. We apply these benchmark tasks to evaluate expression-based models, large language models, protein language models, DNA-based models, and traditional baselines. Our findings suggest that text-based models and protein language models generally outperform expression-based models in genomic properties and regulatory functions tasks, whereas expression-based models demonstrate superior performance in localization tasks. These results should aid in the development of more informed artificial intelligence strategies for biological understanding and therapeutic discovery. To ensure the reproducibility and transparency of our findings, we have made the source code and benchmark data publicly accessible for further investigation and expansion at github.com/BiomedSciAI/gene-benchmark.
Abstract Resilin is an elastic rubber‐like protein found in the cuticles of insects. It incorporates outstanding properties of high resilience and fatigue lifetime, where kinetic energy storage is needed for biological functions such as flight and jumps. Since resilin is rich in tyrosine groups, localized photopolymerization is enabled due to the ability to introduce di‐tyrosine bonds by a ruthenium‐based photoinitiator. Using Multiphoton Absorption Polymerization 3D printing process, objects containing 100% recombinant resilin protein are printed in water at a submicron length scale. Consequently, protein‐based hydrogels with complex structures are printed using space positioning voxel polymerization. The objects are characterized by dynamic mechanical analysis using nanoindentation. Printing parameters such as printing speed and laser power are found to enable tuning the mechanical properties of the printed objects. The printed objects are soft and resilient, similar to native resilin, while presenting the highest resolution of a structure made entirely of a protein and better mechanical properties of common hydrogels and poly(dimethylsiloxane). Moreover, topography and mechanical properties enable cell growth and alignment without cell adhesion primers, thus facilitating biological applications. The fabrication of 3D resilin‐based hydrogel will open the way for potential applications based on biomimicking and in creating new functional objects.
In IVF treatments, extended culture to single blastocyst transfer is the recommended protocol over cleavage-stage transfer. However, evidence-based criteria for assessing the heterogeneous implications on implantation outcomes are lacking. The purpose of this work is to estimate the causal effect of blastocyst transfer on implantation outcome.
