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Abstract There is a tremendous interest in developing hydrogels as tunable in vitro cell culture platforms to study cell response to mechanical cues in a controlled manner. However, little is known about how common cell culture techniques, such as serial expansion on tissue culture plastic, affect subsequent cell behavior when cultured on hydrogels. In this work, a methacrylated hyaluronic acid hydrogel platform is leveraged to study stromal cell mechanotransduction. Hydrogels are first formed through thiol‐Michael addition to model normal soft tissue (e.g., lung) stiffness (E ≈ 1 kPa). Secondary cross‐linking via radical photopolymerization of unconsumed methacrylates allows matching of early‐ (E ≈ 6 kPa) and late‐stage fibrotic tissue (E ≈ 50 kPa). Early passage (P1) human bone marrow mesenchymal stromal cells (hMSCs) display increased spreading, myocardin‐related transcription factor‐A (MRTF‐A) nuclear localization, and focal adhesion size with increasing hydrogel stiffness. However, late passage (P5) hMSCs show reduced sensitivity to substrate mechanics with lower MRTF‐A nuclear translocation and smaller focal adhesions on stiffer hydrogels compared to early passage hMSCs. Similar trends are observed in an immortalized human lung fibroblast line. Overall, this work highlights the implications of standard cell culture practices on investigating cell response to mechanical signals using in vitro hydrogel models.
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This content will become publicly available on July 27, 2026
Predicting Mechanosensitive T Cell Expansion from Cell Spreading
Abstract Variability in T cell performance presents a major challenge to adoptive cellular immunotherapy (ACT). This includes expansion of a small starting population into therapeutically effective numbers, which can fail due to differences between individuals and disease states. Intriguingly, modulating the mechanical stiffness of materials used to activate T cells can rescue subsequent expansion. However, the magnitude of this effect and the optimal stiffnesses differ between individuals, complicating the use of mechanosensing to improve cell production. The ability to predict this long‐term, substrate‐dependent expansion from a short‐term assay would accelerate the deployment of immunotherapy. Here, it is demonstrated that short‐term cell spreading predicts subsequent, mechanosensitive expansion. As an initial task, cell spreading is used to identify whether a sample of cells came from a healthy donor or a Chronic Lymphocytic Leukemia (CLL) patient. Notably, a deep learning (DL) model outperforms morphometric approaches to this classification task. This system also successfully predicts the long‐term expansion potential of cells as a function of both source and mechanical stiffness of the activating substrate. By predicting long‐term T cell function from small, diagnostic samples, this approach will improve the reliability and efficacy of cell production and immunotherapy.
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- Award ID(s):
- 2416900
- PAR ID:
- 10640280
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Healthcare Materials
- ISSN:
- 2192-2640
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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