CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.

CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, achieving reliable therapeutic effects with improved safety and efficacy requires informed target gene selection. This depends on a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) that regulate cell phenotype and function. Machine learning models have been previously used for GRN reconstruction using RNA-seq data, but current techniques are limited to single cell types and focus mainly on transcription factors. This restriction overlooks many potential CRISPR target genes, such as those encoding extracellular matrix components, growth factors, and signaling molecules, thus limiting the applicability of these models for CRISPR strategies. To address these limitations, we have developed CRISPR-GEM, a multi-layer perceptron (MLP)-based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts towards a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.

Neutrophil Granulopoiesis Optimized Through Ex Vivo Expansion of Hematopoietic Progenitors in Engineered 3D Gelatin Methacrylate Hydrogels.

Neutrophils are the first line of defense of the innate immune system. In response to methicillin-resistant Staphylococcus aureus infection in the skin, hematopoietic stem, and progenitor cells (HSPCs) traffic to wounds and undergo extramedullary granulopoiesis, producing neutrophils necessary to resolve the infection. This prompted the engineering of a gelatin methacrylate (GelMA) hydrogel that encapsulates HSPCs within a matrix amenable to subcutaneous delivery. The authors study the influence of hydrogel mechanical properties to produce an artificial niche for granulocyte-monocyte progenitors (GMPs) to efficiently expand into functional neutrophils that can populate infected tissue. Lin-cKIT+ HSPCs, harvested from fluorescent neutrophil reporter mice, are encapsulated in GelMA hydrogels of varying polymer concentration and UV-crosslinked to produce HSPC-laden gels of specific stiffness and mesh sizes. Softer 5% GelMA gels yield the most viable progenitors and effective cell-matrix interactions. Compared to suspension culture, 5% GelMA results in a twofold expansion of mature neutrophils that retain antimicrobial functions including degranulation, phagocytosis, and ROS production. When implanted dermally in C57BL/6J mice, luciferase-expressing neutrophils expanded in GelMA hydrogels are visualized at the site of implantation for over 5 days. They demonstrate the potential of GelMA hydrogels for delivering HSPCs directly to the site of skin infection to promote local granulopoiesis.

Solvent-cast 3D printing with molecular weight polymer blends to decouple effects of scaffold architecture and mechanical properties on mesenchymal stromal cell fate.

The biochemical and physical properties of a scaffold can be tailored to elicit specific cellular responses. However, it is challenging to decouple their individual effects on cell-material interactions. Here, we solvent-cast 3D printed different ratios of high and low molecular weight (MW) poly(caprolactone) (PCL) to fabricate scaffolds with significantly different stiffnesses without affecting other properties. Ink viscosity was used to match processing conditions between inks and generate scaffolds with the same surface chemistry, crystallinity, filament diameter, and architecture. Increasing the ratio of low MW PCL resulted in a significant decrease in modulus. Scaffold modulus did not affect human mesenchymal stromal cell (hMSC) differentiation under osteogenic conditions. However, hMSC response was significantly affected by scaffold stiffness in chondrogenic media. Low stiffness promoted more stable chondrogenesis whereas high stiffness drove hMSC progression toward hypertrophy. These data illustrate how this versatile platform can be used to independently modify biochemical and physical cues in a single scaffold to synergistically enhance desired cellular response.

Immunomodulatory Strategies for Cartilage Regeneration in Osteoarthritis.

Osteoarthritis (OA) is the most prevalent musculoskeletal disorder and a leading cause of disability globally. Although many efforts have been made to treat this condition, current tissue engineering (TE) and regenerative medicine strategies fail to address the inflammatory tissue environment that leads to the rapid progression of the disease and prevents cartilage tissue formation. First, this review addresses in detail the current anti-inflammatory therapies for OA with a special emphasis on pharmacological approaches, gene therapy, and mesenchymal stromal cell (MSC) intra-articular administration, and discusses the reasons behind the limited clinical success of these approaches at enabling cartilage regeneration. Then, we analyze the state-of-the-art TE strategies and how they can be improved by incorporating immunomodulatory capabilities such as the optimization of biomaterial composition, porosity and geometry, and the loading of anti-inflammatory molecules within an engineered structure. Finally, the review discusses the future directions for the new generation of TE strategies for OA treatment, specifically focusing on the spatiotemporal modulation of anti-inflammatory agent presentation to allow for tailored patient-specific therapies. Impact statement Osteoarthritis (OA) is a prevalent and debilitating musculoskeletal disorder affecting millions worldwide. Despite significant advancements in regenerative medicine and tissue engineering (TE), mitigating inflammation while simultaneously promoting cartilage tissue regeneration in OA remains elusive. In this review article, we discuss current anti-inflammatory therapies and explore their potential synergy with cutting-edge cartilage TE strategies, with a special focus on novel spatiotemporal and patient-specific anti-inflammatory strategies.