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1. Biomanufacturing of organ-specific tissues with high cellular density and embedded vascular channels

   https://doi.org/10.1126/sciadv.aaw2459  

   Skylar-Scott, Mark A.; Uzel, Sebastien G.; Nam, Lucy L.; Ahrens, John H.; Truby, Ryan L.; Damaraju, Sarita; Lewis, Jennifer A. (September 2019, Science Advances)

   Engineering organ-specific tissues for therapeutic applications is a grand challenge, requiring the fabrication and maintenance of densely cellular constructs composed of ~10 8 cells/ml. Organ building blocks (OBBs) composed of patient-specific–induced pluripotent stem cell–derived organoids offer a pathway to achieving tissues with the requisite cellular density, microarchitecture, and function. However, to date, scant attention has been devoted to their assembly into 3D tissue constructs. Here, we report a biomanufacturing method for assembling hundreds of thousands of these OBBs into living matrices with high cellular density into which perfusable vascular channels are introduced via embedded three-dimensional bioprinting. The OBB matrices exhibit the desired self-healing, viscoplastic behavior required for sacrificial writing into functional tissue (SWIFT). As an exemplar, we created a perfusable cardiac tissue that fuses and beats synchronously over a 7-day period. Our SWIFT biomanufacturing method enables the rapid assembly of perfusable patient- and organ-specific tissues at therapeutic scales. 

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2. Contextual AI models for single-cell protein biology

   https://doi.org/10.1038/s41592-024-02341-3  

   Li, Michelle M; Huang, Yepeng; Sumathipala, Marissa; Liang, Man Qing; Valdeolivas, Alberto; Ananthakrishnan, Ashwin N; Liao, Katherine; Marbach, Daniel; Zitnik, Marinka (August 2024, Nature Methods)

   Abstract Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas,PINNACLElearns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues.PINNACLE’s embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs’ effects across cell types.PINNACLEoutperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models.PINNACLE’s ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology. 

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3. Gradient Biomolecular Immobilization of 3D Structured Poly‐ ɛ ‐caprolactone Biomaterials toward Functional Engineered Tissue

   https://doi.org/10.1002/mame.202300040  

   Zaeri, Ahmadreza; Zgeib, Ralf; Zhang, Fucheng; Cao, Kai; Chang, Robert C. (October 2023, Macromolecular Materials and Engineering)

   Abstract Additive manufacturing (AM) enables the tailored production of precision fibrous scaffolds toward various engineered tissue models. Moreover, by functionalizing scaffolds in either a uniform or gradient pattern of biomolecules, different target tissues can be fabricated in vitro to capture key characteristics of in vivo cellular microenvironments. However, current engineered tissue models lack the appropriate cellular cues that are needed to deterministically direct cell behavior. Specifically, tunable and reproducible scaffold‐guided stimuli are identified herein as the missing link between biomaterial structure and cellular behavior. Therefore, the bottleneck of precision control is addressed here over the immobilization of patterned biomolecular stimuli with either uniform or gradient distribution over the AM‐enabled 3D biomaterial model as a function of different growth factors exposure variables, protocols, and various scaffold architectural design parameters. The produced study outcomes herein will improve the directing and guiding of biological cell attachment and growth direction in the context of scaffold‐guided stimuli techniques. Therefore, unprecedented control is presented here over 3D structured biomaterial gradient functionalization and immobilization of biomolecules toward biomimetic tissue architectures. 

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4. Cell type–specific cytonuclear coevolution in three allopolyploid plant species

   https://doi.org/10.1073/pnas.2310881120  

   Zhang, Keren; Zhao, Xueru; Zhao, Yue; Zhang, Zhibin; Liu, Zhijian; Liu, Ziyu; Yu, Yanan; Li, Juzuo; Ma, Yiqiao; Dong, Yuefan; et al (October 2023, Proceedings of the National Academy of Sciences)

   Cytonuclear disruption may accompany allopolyploid evolution as a consequence of the merger of different nuclear genomes in a cellular environment having only one set of progenitor organellar genomes. One path to reconcile potential cytonuclear mismatch is biased expression for maternal gene duplicates (homoeologs) encoding proteins that target to plastids and/or mitochondria. Assessment of this transcriptional form of cytonuclear coevolution at the level of individual cells or cell types remains unexplored. Using single-cell (sc-) and single-nucleus (sn-) RNAseq data from eight tissues in three allopolyploid species, we characterized cell type–specific variations of cytonuclear coevolutionary homoeologous expression and demonstrated the temporal dynamics of expression patterns across development stages during cotton fiber development. Our results provide unique insights into transcriptional cytonuclear coevolution in plant allopolyploids at the single-cell level. 

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5. On modeling the multiscale mechanobiology of soft tissues: Challenges and progress

   https://doi.org/10.1063/5.0085025  

   Guo, Yifan; Mofrad, Mohammad R.; Tepole, Adrian Buganza (September 2022, Biophysics Reviews)

   Tissues grow and remodel in response to mechanical cues, extracellular and intracellular signals experienced through various biological events, from the developing embryo to disease and aging. The macroscale response of soft tissues is typically nonlinear, viscoelastic anisotropic, and often emerges from the hierarchical structure of tissues, primarily their biopolymer fiber networks at the microscale. The adaptation to mechanical cues is likewise a multiscale phenomenon. Cell mechanobiology, the ability of cells to transform mechanical inputs into chemical signaling inside the cell, and subsequent regulation of cellular behavior through intra- and inter-cellular signaling networks, is the key coupling at the microscale between the mechanical cues and the mechanical adaptation seen macroscopically. To fully understand mechanics of tissues in growth and remodeling as observed at the tissue level, multiscale models of tissue mechanobiology are essential. In this review, we summarize the state-of-the art modeling tools of soft tissues at both scales, the tissue level response, and the cell scale mechanobiology models. To help the interested reader become more familiar with these modeling frameworks, we also show representative examples. Our aim here is to bring together scientists from different disciplines and enable the future leap in multiscale modeling of tissue mechanobiology. 

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