ArticleCancer biomarkers : section A of Disease markers2025
Spatial proteomics and transcriptomics characterization of tissue and multiple cancer types including decalcified marrow.
Article in Cancer biomarkers : section A of Disease markers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Graphical and interactive spatial proteomics image analysis workflow.GigaByte (Hong Kong, China) · 2026Article
- The emerging role of multiomics in aging research.Epigenomics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
BackgroundRecent technologies enabling the study of spatial biology include multiple high-dimensional spatial imaging methods that have rapidly emerged with different capabilities evaluating tissues at different resolutions for different sample formats. Platforms like Xenium (10x Genomics) and PhenoCycler-Fusion (Akoya Biosciences) enable single-cell resolution analysis of gene and protein expression in archival FFPE tissue slides. However, a key limitation is the absence of systematic methods to ensure tissue quality, marker integrity, and data reproducibility.ObjectiveWe seek to optimize the technical methods for spatial work by addressing preanalytical challenges with various tissue and tumor types, including a decalcification protocol for processing FFPE bone marrow core specimens to preserve nucleic acids for effective spatial proteomics and transcriptomics. This study characterizes a multicancer tissue microarray (TMA) and a molecular- and protein-friendly decalcification protocol that supports downstream spatial biology investigations.MethodsWe developed a multi-cancer tissue microarray (TMA) and processed bone marrow core samples using a molecular- and protein-friendly decalcification protocol. PhenoCycler high-plex immunohistochemistry (IHC) generated spatial proteomics data, analyzed with QuPath and single-cell analysis. Xenium provided spatial transcriptomics data, analyzed via Xenium Explorer and custom pipelines.ResultsResults showed that PhenoCycler and Xenium platforms applied to TMA sections of tonsil and various tumor types achieved good marker concordance. Bone marrow decalcification with our optimized protocol preserved mRNA and protein markers, allowing Xenium analysis to resolve all major cell types while maintaining tissue morphology.ConclusionsWe have shared our preanalytical verification of tissues and demonstrate that both the PhenoCycler-Fusion high-plex spatial proteomics and Xenium spatial transcriptomics platforms work well on various tumor types, including marrow core biopsies decalcified using a molecular- and protein-friendly decalcificationprotocol. We also demonstrate our laboratory's methods for systematic quality assessment of the spatial proteomic and transcriptomic data from these platforms, such that either platform can provide orthogonal confirmation for the other.
Indexed as
Identifiers
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.