ReviewNature biotechnology2022
Spatial components of molecular tissue biology.
Review in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 193 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
193 citing papers in PubMed.
- Spatial chromatin architecture and accessibility co-profiling of mammalian tissues.Nature methods · 2026Article
- Insights Into Spatial Transcriptomics: Exploring Recent Technical Developments and Their Diverse Applications.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- Chemokine Landscapes of the Tumor Microenvironment.bioRxiv : the preprint server for biology · 2026Article
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Mapping and engineering the human cell-cell interactome.Nature biotechnology · 2026Review
- DSSMST: A Deterministic State Space Model for Self-Supervised Spatial Domain Identification in Spatial Transcriptomics.Biochemical genetics · 2026Article
- Spatial multi-omics analyses of the tumor immune microenvironment: advances and novel therapeutic avenues.Molecular cancer · 2026Review
- NicheProt: Cell-type-resolved proteomics of tissue compartments.Science advances · 2026Article
- Integrative spatial multi-omics reveals the tumor ecosystem: From mechanistic insights to therapeutic strategies.iScience · 2026Review
- Dissecting human organ development using spatial technologies.Developmental cell · 2026Review
- PromptSTG: prototype-guided prompting for few-shot spatial transcriptomics annotation.Briefings in bioinformatics · 2026Article
- Cross-Modality Alignment of Spatial Transcriptomics, Multiplexed Imaging, and Histology with PHARAOH.Research square · 2026Article
- Decoding the causal drivers of spatial cellular topology.iScience · 2026Article
- In situ graphene-seq: spatial transcriptomics and chronic electrophysiological characterization of tissue microenvironments.Nature communications · 2026Article
- KIF23 in disease pathogenesis and its therapeutic and diagnostic potential.Discover oncology · 2026Review
- DeSpotX: Identifiability-Based Decontamination for Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Topography-aware optimal transport for alignment of spatial omics data.Cell reports methods · 2026Article
- Comparison and optimization of cellular neighbor preference methods for quantitative tissue analysis.Nature communications · 2026Article
133 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.
Indexed as
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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.