ReviewPathology2026
Spatial transcriptomics in bone research: navigating hype and hurdles.
Review in Pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- SpatialFlux: an R package for distance gradient analysis in spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Phylogeography of Bone Metastasis: Clonal Evolution, Skeletal Niche Adaptation, and Clinical Implications.International journal of molecular sciences · 2026Review
- RNA Sequencing Technologies in Acute Lymphoblastic Leukemia: A Comparative Technical Review.Current issues in molecular biology · 2026Review
- A transcriptomic-driven segmentation and cell simulation framework for high-resolution spatial transcriptomics and cell-cell communication.bioRxiv : the preprint server for biology · 2026Article
- Bone formation niche dysfunction in osteoporosis: insights from single-cell and spatial transcriptomic studies.Frontiers in endocrinology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Spatial transcriptomics (ST) is a powerful technology that facilitates the measurement of gene expression levels within the native tissue architecture. Spatial-omics applications in musculoskeletal research have proven crucial in elucidating aspects of cellular heterogeneity, molecular mechanisms underlying injury, and gene expression patterning in tissue homeostasis and disease. However, the necessity for bone tissue decalcification and processing presents unique tissue-based challenges for the application of ST. Furthermore, ST intrinsic platform limitations, workflow bottlenecks, and analysis complications impose significant challenges to widespread utilisation. Here, we review current ST platforms and outline their respective advantages and limitations, discuss the current state of bone sample processing for use in omics-based approaches, and summarise basic bioinformatics considerations for downstream analysis. We describe how ST has been applied across different musculoskeletal tissues and animal models in the field so far and, finally, provide some potential future directions for the field in how ST approaches could be used to address lingering and future biological questions.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.