Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research.

Musculoskeletal disorders, including fractures, scoliosis, heterotopic ossification, osteoporosis, osteoarthritis, disc degeneration, and muscular injury, etc., can occur at any stage of human life. Understanding the occurrence and development mechanism of musculoskeletal disorders, as well as the changes in tissues and cells during therapy, might help us find targeted treatment methods. Single-cell techniques provide excellent tools for studying alterations at the cellular level of disorders. However, the application of these techniques in research on musculoskeletal disorders is still limited. This review summarizes the current single-cell and spatial omics used in musculoskeletal disorders. Cell isolation, experimental methods, and feasible experimental designs for single-cell studies of musculoskeletal system diseases have been reviewed based on tissue characteristics. Then, the paper summarizes the latest findings of single-cell studies in musculoskeletal disorders from three aspects: bone and ossification, joint, and muscle and tendon disorders. Recent discoveries about the cell populations involved in these diseases are highlighted. Furthermore, the therapeutic responses of musculoskeletal disorders, especially single-cell changes after the treatments of implants, stem cell therapies, and drugs are described. Finally, the application potential and future development directions of single-cell and spatial omics in research on musculoskeletal diseases are discussed.

A unified model-based framework for doublet or multiplet detection in single-cell multiomics data.

Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.

Rapid regulations of metabolic reactions in Escherichia coli via light-responsive enzyme redistribution.

Protein-based condensates have been proposed to accelerate biochemical reactions by enriching reactants and enzymes simultaneously. Here, we engineered those condensates into a photo-activated switch in Escherichia coli (PhASE) to regulate enzymatic reactions via tuning the spatial correlation of enzymes and substrates. In this system, scaffold proteins undergo liquid-liquid phase separation (LLPS) to form light-responsive compartments. Tethered with a light-responsive protein, enzymes of interest (EOIs) can be recruited by those compartments from cytosol within only a few seconds after a pulse of light induction and fully released in 15 min. Furthermore, we managed to enrich small molecular substrates simultaneously with enzymes in the compartments and achieved the acceleration of luciferin and catechol oxidation by 2.3- and 1.6-folds, respectively. We also developed a quantitative model to guide the further optimization of this demixed regulatory system. Our tool can thus be used to study the rapid redistribution of proteins, and reversibly regulate enzymatic reactions in E. coli.