ReviewBMB reports2022
Recent advances in spatially resolved transcriptomics: challenges and opportunities.
Review in BMB reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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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
31 citing papers in PubMed, 19 citations in OpenAlex.
- GRAIL-heart: A graph attention network for inferring ligand-receptor interactions in spatial transcriptomics.MethodsX · 2026Article
- Review
- CTMAP: an adversarial cross-modal learning framework for accurate and robust cell-type annotation in single-cell resolution spatial transcriptomics.Briefings in bioinformatics · 2026Article
- GABACellular and molecular neurobiology · 2026Review
- Global scientific growth and thematic development of spatial transcriptomics in oncology research.Discover oncology · 2026Article
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Spatial Transcriptomics Meets Histochemistry: Insights from Glioblastoma as a Model System.Acta histochemica et cytochemica · 2026Review
- Advances in the pathophysiological study of brain development: application of cerebral organoid combined with Spatial omics technology.Stem cell research & therapy · 2026Review
- High-altitude hypoxic cues and cerebral ischemic tolerance: an evidence-graded translational framework for stroke research.Frontiers in neuroscience · 2026Review
- Advancing Intervertebral Disc Biology via Omics: Implications for Nucleus Pulposus Progenitor Cell-Based Regeneration.JOR spine · 2025Review
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
- Molecular Insights into Human Placentation: From Villous Morphogenesis to Pathological Pathways and Translational Biomarkers.International journal of molecular sciences · 2025Review
- Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Detection of mRNA Transcript Variants.Genes · 2025Review
- Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research.Acta biochimica Polonica · 2025Review
- Application of transcriptomics techniques in skin cancer.Frontiers in oncology · 2025Review
- Spatial visualization provides insight into immune modulation by an L-DBF vaccine formulation againstFrontiers in immunology · 2025Article
- The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma.Cancer immunology research · 2024Article
- Characterization of an orthotopic mouse transplant model reveals early changes in the tumor microenvironment of lung cancer.BMB reports · 2024Article
- BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at singlemolecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capturebased method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization. [BMB Reports 2022; 55(3): 113-124].
Indexed as
Identifiers
35168703PMC8972138W4213392791What 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.