Jinjin Ma

Motivation: Single-cell time-series atlases provide discrete cross-sectional snapshots of continuous developmental processes, making missing-snapshot prediction essential for reconstructing trajectories and dynamic programs. However, destructive scRNA-seq profiling prevents tracking the same cells across time, yielding unpaired population snapshots and making missing-snapshot prediction challenging. Existing methods typically rely on explicit dynamical propagation or transport-based coupling between snapshots, which may ignore the statistical constraints of the overall population distribution and be sensitive to sampling noise and cell-to-cell variability. Results: We introduce scChronos, a hierarchical temporal context learning framework that formulates missing-snapshot prediction as target-conditioned population distribution prediction rather than individual-cell propagation. Unlike cell-centric propagation approaches, scChronos explicitly captures dual-level dynamics: (i) within each observed snapshot, scChronos compresses cell populations into cluster-representative cell representations with a transcriptomic encoder, preserving population information while reducing noise and (ii) across reference snapshots, the dual-branch temporal context integration module fuses information from all available and nearby time points into a temporal representation, which is then decoded with learnable cell-generation seeds to predict the target distribution. Extensive experiments demonstrate that scChronos improves both interpolation-style recovery and extrapolation-style forecasting compared with state-of-the-art methods. Beyond reducing distributional discrepancy, scChronos better preserves developmental topology and maintains cell-type-specific marker and pathway programs. Availability: The code and data are available at https://github.com/yjs193/scChronos and https://doi.org/10.5281/zenodo.20933283.

Rotator cuff disease is a common musculoskeletal disorder of the shoulder that causes pain and functional disability. Despite its high prevalence and substantial clinical burden, clinically validated biomarkers for diagnosis, disease monitoring, treatment management, and outcome prediction remain lacking. High‐throughput omics approaches provide powerful tools for systematically identifying candidate biomarkers by comprehensively characterizing disease‐associated molecular alterations. This systematic review evaluates recent advances in multi‐omics approaches for biomarker identification in rotator cuff disease, with a particular emphasis on integrating findings across genomics, epigenomics, transcriptomics, and proteomics and metabolomics to facilitate biomarker discovery and clinical translation. A total of 46 studies were included in this review. Together, multi‐omics evidence identified recurrent candidate biomarkers, particularly MMP3 , MFAP5 , IL6 , and BMP5 , that converged on extracellular matrix remodeling, inflammatory and immune regulation, tissue repair and fibrosis, metabolic reprogramming, mitochondrial dysfunction, and tissue‐specific remodeling. Integrated analyses further prioritized recurrent candidate biomarkers supported across multiple omics layers, primarily involved in matrix remodeling, inflammation, immune regulation, angiogenesis, and tissue repair, providing potential targets for diagnosis, prognosis, and therapeutic intervention. Future studies incorporating larger prospective cohorts, standardized analytical pipelines, same‐cohort multi‐omics integration, and functional validation will be essential for translating these candidate biomarkers into clinically useful tools for early diagnosis, disease monitoring, outcome prediction, and personalized treatment.

Motivation: For spot-based spatial transcriptomics (ST), accurate cell-type deconvolution is essential for downstream analysis since each spot captures mixtures of multiple cell types. Meanwhile, spatial niches define distinct micro-environmental contexts, also salient for biological interpretation. However, existing deconvolution methods usually rely on fixed reference signatures or mapping single cells onto ST spots, without incorporating niche priors or modeling niche-dependent shifts. Consequently, existing methods remain focused on spot-level proportion estimation, with limited ability to support functional analysis of niche-associated molecular programs. Results: We present NicheDeSig for niche-aware deconvolution. NicheDeSig models each cell type through adaptive signatures, enabling spot deconvolution under context-dependent signatures and supporting niche-aware analysis of cell-state variation across spatial micro-environments. Our method achieves strong deconvolution performance across the simulated benchmark datasets and improves spatial fidelity in the simulated colon dataset. The learned signatures recover laminar and white-matter-associated programs in the human dorsolateral prefrontal cortex (DLPFC), domain-stratified tumor microenvironment patterns in breast cancer (BRCA), and region-associated signatures in pancreatic ductal adenocarcinoma sample A (PDAC-A) and colorectal liver metastasis analyses. Availability and implementation: Source code and the archived code snapshot are available at https://github.com/Davidcoach/NicheDeSig and https://doi.org/10.5281/zenodo.20685597.

Kaplan fibers (KFs) are critical stabilizers of the knee, yet their role in anterior tibial subluxation of the lateral compartment (ATSLC) in anterior cruc

Cyclin‐dependent kinase (CDK) 14 is involved in Wnt signalling, tissue repair, and tumourigenesis, but its function in haematopoiesis remains unexplored. Here, we found that CDK14 was necessary to maintain physiological haematopoiesis under steady‐state and haematopoietic reconstitution after transplantation using genetic and pharmacological interventions. By utilising Cdk14 knockout and haematopoietic‐deletion ( Cdk14 ^∆HC ) mouse models, in combination with the selective CDK14 inhibitor FMF‐04‐159‐2, we showed that ablation of CDK14 resulted in diminished haematopoietic stem and progenitor cells (HSPCs), and disrupted the proportion of mature haematopoietic cells. Single‐cell RNA sequencing analysis of bone marrow from Cdk14 ^∆HC mice revealed impaired cell cycle progression, perturbed HSPC proportion, and altered niche‐HSPC interactions. Furthermore, genetic and pharmacological ablation of CDK14 markedly decreased HSPC proliferation after transplantation in vivo and reduced colony formation in vitro. These findings identify CDK14 as a critical regulator of haematopoietic homeostasis and highlight its essential role in supporting HSPC function under stress conditions.

Osteoporosis is a known risk factor for rotator cuff tears (RCTs), but the causal correlation and underlying mechanisms remain unclear. This study aims to evaluate the impact of osteoporosis on RCT risk and investigate their genetic associations. Using data from the UK Biobank (n = 457 871), cross-sectional analyses demonstrated that osteoporosis was significantly associated with an increased risk of RCTs (adjusted OR [95% CI] = 1.38 [1.25–1.52]). A longitudinal analysis of a subset of patients (n = 268 117) over 11 years revealed that osteoporosis increased the risk of RCTs (adjusted HR [95% CI] = 1.56 [1.29–1.87]), which is notably varied between sexes in sex-stratified analysis. Causal inference methods, including propensity score matching, inverse probability weighting, causal random forest and survival random forest models further confirmed the causal effect, both from cross-sectional and longitudinal perspectives. A colocalization analysis across multiple datasets identified six candidate loci, including the successfully replicated PKDCC rs12996954 variant, which may help explain the shared genetic basis between osteoporosis and RCTs. In conclusion, osteoporosis significantly increases the risk of RCTs, emphasizing the importance of osteoporosis management in preventing RCTs. The identification of shared genetic loci provides new insights into their potential pathogenic mechanisms.

Mechanotransduction is essential for living cells to adapt to their extracellular environment. However, it is unclear how the biophysical adaptation of intracellular organelles responds to mechanical stress or how these adaptive changes affect cellular homeostasis. Here, using the tendon cell as a mechanosensitive cell type within a bioreactor, we show that the tension of the plasma membrane (PM) and the endoplasmic reticulum (ER) adaptively increases in response to repetitive external stimuli. Depletion of stromal interaction molecule 1 (STIM1), the highest expressed PM-ER tether protein, interfered with mechanotransduction from the PM to the ER, and affected the ER tension. We found that an optimized mechanical strain increased ER tension in a homeostatic manner, but excessive strain resulted in ER expansion, as well as activating ER stress. Last, we showed that changes in ER tension were linked with ER-mitochondria interactions and associated with cellular energetics and function. Together, these findings identify a PM-ER mechanotransduction mechanism that dose-dependently regulates cellular metabolism.

Hematopoiesis and the behavior of hematopoietic stem and progenitor cells (HSPCs) are regulated by the bone marrow niche. Here, we introduce the major niche cell types in bone marrow and their response to stress condition. We highlight the hematopoietic response and bone marrow niche adaptation to inflammatory condition and non-hematopoietic diseases, which are not systematically summarized. These emerging data suggest targeting hematopoiesis and bone marrow niche may provide novel therapeutic target to precisely control the progression of the diseases.

The formation of a blood vessel network is crucial for organ development and regeneration. Over the past three decades, the central molecular mechanisms governing blood vessel growth have been extensively studied. Recent evidence indicates that vascular endothelial cells—the specialized cells lining the inner surface of blood vessels—exhibit significant heterogeneity to meet the specific needs of different organs. This review focuses on the current understanding of endothelial cell heterogeneity, which includes both intra-organ and inter-organ heterogeneity. Intra-organ heterogeneity encompasses arterio-venous and tip-stalk endothelial cell specialization, while inter-organ heterogeneity refers to organ-specific transcriptomic profiles and functions. Advances in single-cell RNA sequencing (scRNA-seq) have enabled the identification of new endothelial subpopulations and the comparison of gene expression patterns across different subsets of endothelial cells. Integrating scRNA-seq with other high-throughput sequencing technologies promises to deepen our understanding of endothelial cell heterogeneity at the epigenetic level and in a spatially resolved context. To further explore human endothelial cell heterogeneity, vascular organoids offer powerful tools for studying gene function in three-dimensional culture systems and for investigating endothelial-tissue interactions using human cells. Developing organ-specific vascular organoids presents unique opportunities to unravel inter-organ endothelial cell heterogeneity and its implications for human disease. Emerging technologies, such as scRNA-seq and vascular organoids, are poised to transform our understanding of endothelial cell heterogeneity and pave the way for innovative therapeutic strategies to address human vascular diseases. Graphical Abstract