Evidence map›Paper›PMID 39095360›Full record

ArticleNature communications2024

Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model.

Bohan Li, Feng Bao, Yimin Hou, Fengji Li, Hongjue Li, Yue Deng, Qionghai Dai

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Bohan Li *School of Artificial Intelligence, Beihang University, Beijing, China.ORCID 0009-0000-2681-5724
Feng Bao *Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China.ORCID 0000-0002-5721-9551
Yimin HouSchool of Artificial Intelligence, Beihang University, Beijing, China.ORCID 0000-0002-1106-8233
Fengji LiSchool of Artificial Intelligence, Beihang University, Beijing, China.ORCID 0009-0009-8169-5484
Hongjue LiSchool of Artificial Intelligence, Beihang University, Beijing, China.ORCID 0000-0002-0504-2555
Yue DengSchool of Artificial Intelligence, Beihang University, Beijing, China. ydeng@buaa.edu.cn.ORCID 0000-0003-2871-8922
Qionghai DaiBeijing National Research Center for Information Science and Technology (BNRist), Beijing, China. qhdai@tsinghua.edu.cn.ORCID 0000-0001-7043-3061

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in spatial omics have expanded the spectrum of profiled molecular categories beyond transcriptomics. However, many of these technologies are constrained by limited spatial resolution, hindering our ability to deeply characterize intricate tissue architectures. Existing computational methods primarily focus on the resolution enhancement of transcriptomics data, lacking the adaptability to address the emerging spatial omics technologies that profile various omics types. Here, we introduce soScope, a unified generative framework designed to enhance data quality and spatial resolution for molecular profiles obtained from diverse spatial technologies. soScope aggregates multimodal tissue information from omics, spatial relations and images, and jointly infers omics profiles at enhanced resolutions with omics-specific modeling through distribution priors. With comprehensive evaluations on diverse spatial omics platforms, including Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq, soScope improves performances in identifying biologically meaningful intestine and kidney architectures, revealing embryonic heart structure that cannot be resolved at the original resolution and correcting sample and technical biases arising from sequencing and sample processing. Furthermore, soScope extends to spatial multiomics technology spatial-CITE-seq and spatial ATAC-RNA-seq, leveraging cross-omics reference for simultaneous multiomics enhancement. soScope provides a versatile tool to improve the utilization of continually expanding spatial omics technologies and resources.

Indexed as

TranscriptomeAnimalsComputational BiologyGene Expression ProfilingGenomicsHumansKidneyMiceRNA-Seq

Identifiers

PMID39095360
PMCPMC11297205

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.