Evidence map›Paper›PMID 42124856›Full record

ArticleJBMR plus2026

Spatial transcriptomic profiling of decalcified murine musculoskeletal samples via Xenium Prime 5K.

Lindsey Lammlin, Huong X Tran, Aanya Mohan, Michael D Newton, Scarlet C Howser, Stephen J Redding, Christina A Capobianco, Kurt D Hankenson, Alexander J Knights, Tristan Maerz

Abstract read
In one paragraph

Article in JBMR plus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Lindsey LammlinDepartment of Health Sciences and Technology, Institute for Biomechanics, ETH Zürich, 8092 Zürich, Switzerland.ORCID https://orcid.org/0000-0003-4126-8292
Huong X TranDepartment of Health Sciences and Technology, Institute for Biomechanics, ETH Zürich, 8092 Zürich, Switzerland.
Aanya MohanDepartment of Health Sciences and Technology, Institute for Biomechanics, ETH Zürich, 8092 Zürich, Switzerland.
Michael D NewtonDepartment of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI 48109, United States.
Scarlet C HowserDepartment of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI 48109, United States.
Stephen J ReddingDepartment of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI 48109, United States.
Christina A CapobiancoDepartment of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI 48109, United States.
Kurt D HankensonDepartment of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI 48109, United States.
Alexander J KnightsDepartment of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI 48109, United States.
Tristan MaerzDepartment of Health Sciences and Technology, Institute for Biomechanics, ETH Zürich, 8092 Zürich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Successful generation of high-quality spatial transcriptomics data from murine musculoskeletal tissues has been impeded by the challenge of preserving RNA integrity through the harsh tissue processing steps required for histological sectioning. In particular, the need to thoroughly fix and decalcify mineralized tissues has proven problematic. We detail a comprehensive sample processing pipeline for 3 common murine musculoskeletal tissue samples, enabling high-quality transcript detection via imaging-based spatial transcriptomics using the Xenium Prime 5K platform from 10x Genomics. Our protocol outlines methodological details for transcardiac perfusion, fixation, decalcification, paraffin processing, and a sample co-embedding strategy facilitating anatomically consistent and simultaneous sectioning of multiple samples onto the spatial transcriptomics slide. Rigorous quality control demonstrates high-quality tissue-specific outcomes across intact knee joints, tibiae, and lumbar spines from adult mice. Our pipeline enabled 70%-91% high-quality transcripts across synovium, meniscus, patellar tendon, articular cartilage, subchondral bone, cortical bone, bone marrow, muscle, fracture callus, and dorsal root ganglion (DRG) tissues. The average number of detected transcripts varied markedly between tissue types: soft tissues, such as synovium, patellar tendon, muscle, bone marrow, and callus exhibited ~200 to 400 transcript per cell; mineralized tissues, such as subchondral bone, meniscus, and cortical bone exhibited ~13 to 150 transcripts per cell; and highly active neuronal tissues, such as DRG neurons yielded 750-1100 transcripts per cell. Canonical cell markers within each tissue confirmed successful identification and representation of key cell types. Through rigorous sample quality assessment at multiple stages of processing, this protocol yields high-quality RNA transcript detection while preserving critical anatomical context and will serve as a valuable tool enabling spatial transcriptomic profiling of intact musculoskeletal tissue samples.

Indexed as

bioengineeringbone–brain–nervous system interactionsbone histomorphometryinjury/fracture healingorthopedicsosteoarthritis

Identifiers

PMID42124856
PMCPMC13160414

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LicenceCC BY
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Registered trials

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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.