In one paragraphArticle in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
15 authors.
Qionghua ShenLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0000-0002-2808-4408 Tai NgoLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Hanieh Mazloom-FarsibafLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0000-0002-2571-0418 Kelly WongChildren's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, Center for Regenerative Science and Medicine, Children's Research Institute Mouse Genome Engineering Core, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Lin LiChildren's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, Center for Regenerative Science and Medicine, Children's Research Institute Mouse Genome Engineering Core, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Kushal BhattLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Felix Y ZhouLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0000-0003-4463-1165 Bo-Jui ChangLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0000-0002-5513-7106 Xiaoding WangLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Zhiguo ShangLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0000-0002-7600-7554 John HaugLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0000-0001-8247-3271 Hazel M BorgesLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0009-0006-5834-5310 Reto FiolkaLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0000-0002-4636-5000 Hao ZhuChildren's Research Institute, Departments of Pediatrics and Internal Medicine, Simmons Comprehensive Cancer Center, Center for Regenerative Science and Medicine, Children's Research Institute Mouse Genome Engineering Core, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.ORCID 0000-0002-8417-9698 Kevin M DeanLyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.ORCID 0000-0003-0839-2320 Funding
Technical Development Unit 2: Intelligent Hyperspectral Imaging of Subcellular Molecular States at the Whole Organ LevelU54CA268072 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Kevin Michael Dean · 2021 to 2026
$9.1MUTSW-UNC Center for Cell Signaling AnalysisRM1GM145399 · NIGMS · UT SOUTHWESTERN MEDICAL CENTER · PI Kevin Michael Dean, Klaus M. Hahn · 2022 to 2026
$6.2MTransformative microscopes to image across spatiotemporal scalesR35GM133522 · NIGMS · UT SOUTHWESTERN MEDICAL CENTER · PI Reto Paul Fiolka · 2019 to 2026
$3.1MDetermining how chronic ETOH influences the regenerative activities of hepatocyte subpopulationsR01AA028791 · NIAAA · UT SOUTHWESTERN MEDICAL CENTER · PI ZHU, HAO · 2021 to 2025
$2.7MElucidating chronic liver disease pathways using somatic geneticsDP1DK139976 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI Hao Zhu · 2024 to 2026
$2.3MOmni Oblique Plane Microscope to spread light-sheet based imaging in biomedical researchR01EB035538 · NIBIB · UT SOUTHWESTERN MEDICAL CENTER · PI Reto Paul Fiolka · 2024 to 2026
$1.0MData-driven mechanistic models of morphology-controlled Ras oncogenic signalingK99CA283246 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Hanieh Mazloom Farsibaf · 2025 to 2026
$280kNCI NIH HHS K99 CA283246NCI NIH HHS U54 CA268072NIAAA NIH HHS R01 AA028791NIBIB NIH HHS R01 EB035538NIDDK NIH HHS DP1 DK139976NIGMS NIH HHS R35 GM133522NIGMS NIH HHS RM1 GM145399
6 · The paper itselfAbstract
Somatic oncogenic mutations are typically defined by their molecular alterations, yet how they reorganize cellular architecture within intact tissues remain largely unknown. Here, we demonstrate that distinct oncogenic drivers produce unique multiscale architectural phenotypes that can be quantitatively resolved in intact liver tissue. Using iterative expansion microscopy, multiscale light-sheet imaging, and three-dimensional morphometric analysis, we systematically mapped structural remodeling from single-cell morphology to mitochondrial architecture in mosaic mouse models of hepatocellular oncogene activation. NRAS and CTNNB1 induced fundamentally different morphological programs. NRAS activation drove extensive remodeling of cell morphology, membrane curvature, and surface irregularity, whereas CTNNB1 activation largely preserved global cell morphology while selectively altering mitochondrial organization and shape. We first established a zonation-aware reference state for interpreting oncogene-associated organelle remodeling by resolving mitochondrial differences between periportal and pericentral hepatocytes. Within this framework, CTNNB1 activation shifted mitochondrial features toward a pericentral-like state, consistent with the role of Wnt/β-catenin signaling in hepatic zonation and metabolic identity. Furthermore, integrating cellular morphology, membrane geometry, and mitochondrial architecture improved discrimination of oncogenic states beyond any individual structural feature, demonstrating that mutation-specific phenotypes arise through coordinated remodeling across multiple biological scales. Together, these findings establish multiscale structural phenotyping as a framework for linking oncogenic genotype to three-dimensional cellular organization and reveal that distinct oncogenic drivers remodel different architectural compartments during liver oncogene activation.
Identifiers
PMID42523368
PMCPMC13404897
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