bioRxiv Science⌕ Search

bioRxiv · 10.1101/2021.02.23.432501

Morphological Landscapes from High Content Imaging Identify Optimal Priming Strategies that Enhance MSC Immunosuppression

Abstract

Successful clinical translation of mesenchymal stromal cell (MSC) products has not been achieved in the United States and may be in large part due to MSC functional heterogeneity. Efforts have been made to identify priming conditions that produce MSCs with consistent immunomodulatory function; however, challenges remain with predicting and understanding how priming impacts MSC behavior. The purpose of this study was to develop a high throughput, image-based approach to assess MSC morphology in response to combinatorial priming treatments and establish morphological profiling as an effective approach to screen the effect of manufacturing changes (i.e. priming) on MSC immunomodulation. We characterized the morphological response of multiple MSC lines/passages to an array of Interferon-gamma (IFN-{gamma}) and Tumor Necrosis Factor alpha (TNF-) priming conditions, as well as the effects of priming on MSC modulation of activated T cells and MSC secretome. Although considerable functional heterogeneity, in terms of T cell suppression, was observed between different MSC lines and at different passages, this heterogeneity was significantly reduced with combined IFN-{gamma}/TNF- priming. The magnitude of this change correlated strongly with multiple morphological features and was also reflected by MSC secretion of immunomodulatory factors e.g. PGE2, ICAM-1, and CXCL16. Overall, this study further demonstrates the ability of priming to enhance MSC function, as well as the ability of morphology to better understand MSC heterogeneity and predict changes in function due to manufacturing.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Andrews, S. H., Klinker, M. W., Bauer, S. R., Marklein, R. A.. 2021-02-24. Morphological Landscapes from High Content Imaging Identify Optimal Priming Strategies that Enhance MSC Immunosuppression. https://doi.org/10.1101/2021.02.23.432501

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Synergy-informed bilevel optimization reveals joint-specific benefits for realistic exoskeleton torque prediction

Simulation-driven optimization can guide exoskeleton control design, but its utility is limited by the accuracy with which muscle coordination is predicted. Strict effort-minimization formulations predict non-physiological muscle shutdown under high exoskeleton assistance, leading to peak assistive torques that exceed values reported in human-in-the-loop experiments. We evaluated the accuracy of synergy-informed simulations at predicting assistance across four single-joint exoskeleton configurations: ankle plantarflexion, knee extension, hip flexion, and hip abduction. We implemented a bilevel optimization to predict a parameterized assistive torque that minimizes metabolic cost, with assisted muscle dynamics estimated via either effort-minimization simulations (minimizing muscle effort only) or synergy-informed simulations (minimizing muscle effort constrained to a synergy structure). We used experimental data from five unimpaired adults walking at their preferred speed and compared results descriptively between the two formulations within each subject. Assistance based on effort-minimization simulations shut down agonist muscle activations and overestimated peak assistive torque compared with experimental values. In contrast, constraining muscle controls to a synergy structure prevented non-physiological muscle shutdown and yielded assistive torques closer to those measured in human-in-the-loop experiments. This benefit was not observed equally across all assisted degrees of freedom or subjects, emerging only where no single synergy could be suppressed to unload the assisted muscles. Synergy weights can therefore be interpreted as joint-specific predictors of when simulations can reliably inform wearable robot design.

bioengineering↗

Engineered microvessel networks -- Longitudinal transcriptomic profiling validates chip-grown tissues as a robust model for studying vasculature

Vascularised Organ-on-Chip (OoC) models are promising New Approach Methodologies (NAMs), but their translation requires validation for defined Contexts of Use (CoU). Here, we combine microfluidic microvessel networks (MVNs) with longitudinal bulk and single-cell RNA sequencing to determine how cell provenance influences engineered vascular phenotype. Stromal cell identity produced distinct network architectures and transcriptional trajectories, demonstrating that support cells, commonly treated as interchangeable, represent an important CoU variable. Primary human pancreatic microvascular endothelial cells failed to form stable perfusable networks, but supplementation with 10% Human Umbilical Vein Endothelial Cells (HUVECs) or Blood Outgrowth Endothelial Cells (BOECs) rescued self-assembly. Single-cell transcriptomics identified specialised endothelial populations associated with vascular morphogenesis, including tip, stalk, phalanx, endothelial to mesenchymal transition (EndMT) and pericyte-like endothelial cells. Failed networks lacked coherent endothelial specialisation, whereas rescued networks restored stabilising cell states and revealed FLRT2--UNC5B signalling within pericyte-like HUVECs, recapitulating a pathway implicated in angiogenic development in vivo. These findings establish cell provenance as a critical determinant of microvascular NAM performance and demonstrate how transcriptomic validation can define and strengthen the biological relevance of microvessel-on-chip models for specific CoUs.

bioengineering↗

Benchmarking foundation models for resting-state functional magnetic resonance imaging time series in cognitive aging research

Foundation models trained on large neuroimaging datasets could help smaller studies investigate cognition from brain recordings. However, whether the foundation model-derived representations improve assessment of current cognition, prediction of follow-up cognition, or characterization of cognitive change remains unclear. We evaluated nine configurations from five model families that process regional resting-state functional magnetic resonance imaging time series, using 3,103 scans from 1,653 participants across two observational and five intervention aging cohorts. Primary frozen representations showed no consistent superiority over conventional functional connectivity for concurrent cognitive associations or diagnostic classification. Changing the readout improved selected concurrent cognitive associations without updating model weights, indicating that performance depended partly on how representations were extracted. Additional post-training and embedding alignment yielded gains in selected settings but did not consistently improve performance across outcomes. In the primary longitudinal analyses, imaging representations did not establish added value beyond baseline clinical measures. These findings show that readout choice influences concurrent cognitive associations, while the added value of these representations for longitudinal outcomes remains unestablished.

bioengineering↗