Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
0numbers the graph read from it
0cells of the map it votes in
6citing 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.
Angelo AnacletoDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.ORCID 0009-0007-1157-6346
Weiqiu ChengDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, USA.
Qianlu FengDepartment of Chemistry, University of Illinois at Urbana-Champaign, Urbana, USA.ORCID 0009-0001-9790-3497
Anna ParkDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.
Chun-Seok ChoDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.ORCID 0000-0002-9589-5745
Yongha HwangDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.ORCID 0000-0002-4345-581X
Yongsung KimDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.
Yichen SiDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, USA.ORCID 0000-0001-5576-3054
Jer-En HsuDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.ORCID 0000-0002-2968-1853
Qingyang ZhaoDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.
Xiaoya ZhaoDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.
Daniel KimDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.
Mitchell SchrankDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.
Alex William SchraderDepartment of Chemistry, University of Illinois at Urbana-Champaign, Urbana, USA.ORCID 0000-0003-0235-7903
Seokjin YeoDepartment of Chemistry, University of Illinois at Urbana-Champaign, Urbana, USA.
Rosane TelesDivision of Dermatology, Department of Medicine, University of California, Los Angeles, USA.ORCID 0000-0003-2908-3244
Robert L ModlinDivision of Dermatology, Department of Medicine, University of California, Los Angeles, USA.ORCID 0000-0003-4720-031X
Olesya PlazyoDepartment of Dermatology, University of Michigan, Ann Arbor, USA.
Johann E GudjonssonDepartment of Dermatology, University of Michigan, Ann Arbor, USA.ORCID 0000-0002-0080-0812
Myungjin KimDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA.ORCID 0000-0002-3144-5235
Chang H KimDepartment of Pathology and Mary H. Weiser Food Allergy Center, University of Michigan, Ann Arbor, USA.ORCID 0000-0003-4618-9139
Hee-Sun HanDepartment of Chemistry, University of Illinois at Urbana-Champaign, Urbana, USA. hshan@illinois.edu.ORCID 0000-0003-3616-291X
Hyun Min KangDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, USA. hmkang@umich.edu.ORCID 0000-0002-3631-3979
Jun Hee LeeDepartment of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, USA. leeju@umich.edu.ORCID 0000-0002-2200-6011
Funding
Microbiology and Metagenomics CoreP50AR080594 · NIAMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Matteo Pellegrini · 2022 to 2026
$9.2M
The NRF2-FBP1 crossregulatory loop and the control of healthy and diseased liver metabolismR01DK133448 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Michael Karin, Jun Hee Lee · 2022 to 2026
$3.6M
Chemical toolbox for multiscale, integrative imaging: Connecting cellular gene expression to organ-scale phenotypeR35GM147420 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Hee-Sun Han · 2022 to 2026
$2.2M
Sestrins-mediated integration of leucine and exercise benefits for mitochondrial homeostasisR01AG079163 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Myungjin Kim, Jun Hee Lee · 2023 to 2026
$2.1M
Seq-Scope: Microscopic Examination of Spatial Single Cell Transcriptome in Cell and Tissue SenescenceUH3CA268091 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LEE, JUN HEE · 2023 to 2025
$1.7M
Seq-Scope: Microscopic Examination of Spatial Single Cell Transcriptome in Cell and Tissue SenescenceUG3CA268091 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LEE, JUN HEE · 2021 to 2022
$1.1M
Identifying Global Rejuvenation Mechanisms in Tissues that Reverses Age-Related Phenotypes in PlanariansF31AG094300 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Angelo Anacleto · 2025 to 2026
$88k
NCI NIH HHS UG3 CA268091NCI NIH HHS UH3 CA268091NIAMS NIH HHS P50 AR080594NIA NIH HHS F31 AG094300NIA NIH HHS R01 AG079163NIGMS NIH HHS R35 GM147420U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01AG079163U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01DK133448U.S. Department of Health & Human Services | National Institutes of Health (NIH) UH3CA268091
6 · The paper itself
Abstract
Sequencing-based spatial transcriptomics (sST) enables transcriptome-wide gene expression mapping but falls short of reaching the optical resolution (200-300 nm) of imaging-based methods. Here, we present Seq-Scope-X (Seq-Scope-eXpanded), which empowers submicrometer-resolution Seq-Scope with tissue expansion to surpass this limitation. By physically enlarging tissues, Seq-Scope-X minimizes transcript diffusion effects and increases spatial feature density by an additional order of magnitude. In liver tissue, this approach resolves nuclear and cytoplasmic compartments in nearly every single cell, uncovering widespread differences between nuclear and cytoplasmic transcriptome patterns. Independently confirmed by imaging-based methods, these results suggest that individual hepatocytes can dynamically switch their metabolic roles. Seq-Scope-X also works in brain and colon, and can be adapted for spatial proteomics, profiling hundreds of barcode-tagged antibody stains at microscopic resolutions in mouse spleens and human tonsils. Together, these findings establish Seq-Scope-X as a powerful platform for ultra-high-resolution whole-transcriptome and proteome profiling, expanding the spatial precision achievable for studying cellular architecture, function, and disease mechanisms.
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.
Seq-Scope-eXpanded: spatial omics beyond optical resolution. · full record | OpenQuestion