Evidence map›Paper›PMID 42303047›Full record

ArticleLaboratory investigation; a journal of technical methods and pathology2026

Assessing the Effects of a 3-Dimensional (3D) Pathology Tissue-Processing Workflow on Downstream Molecular Analyses.

Elena Baraznenok, Huai-Ching Hsieh, Lydia Lan, Eric Q Konnick, Sandy Figiel, Srinivasa R Rao, Dan J Woodcock, Ian G Mills, Freddie C Hamdy, Jacob E Valk and 6 more

Abstract read
In one paragraph

Article in Laboratory investigation; a journal of technical methods and pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Prototype-based AI triage for 3D pathology.bioRxiv : the preprint server for biology · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Elena BaraznenokDepartment of Bioengineering, University of Washington, Seattle, Washington; Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Bioengineering, University of California Berkeley, Berkeley, California.
Huai-Ching HsiehDepartment of Bioengineering, Stanford University, Stanford, California; Department of Pathology, Stanford University, Stanford, California.
Lydia LanDepartment of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Biology, University of Washington, Seattle, Washington.
Eric Q KonnickDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington.
Sandy FigielNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
Srinivasa R RaoNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
Dan J WoodcockNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK.
Ian G MillsNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
Freddie C HamdyNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
Jacob E ValkDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington.
Kelly T CarterTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington.
Ming YuTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington.
Thomas G PaulsonTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington.
Suzanne DintzisDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington.
William M GradyTranslational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Medicine, University of Washington, Seattle, Washington.
Jonathan T C LiuDepartment of Bioengineering, University of Washington, Seattle, Washington; Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Bioengineering, Stanford University, Stanford, California; Department of Pathology, Stanford University, Stanford, California; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington. Electronic address: jonliu@stanford.edu.

Funding

Understanding adenoma progression: Interplay among tissue microenvironment, clonal architecture, and gut microbiomeU54CA274374 · NCI · FRED HUTCHINSON CANCER CENTER · PI Neelendu Dey · 2022 to 2026
$10.7M
Biomarkers for optimizing risk prediction and early detection of cancers of the colon and esophagusU2CCA271902 · NCI · FRED HUTCHINSON CANCER CENTER · PI Cecilia C Yeung · 2022 to 2026
$5.3M
Modeling Neoplastic Progression in Barrett's Esophagus - Renewal -2R01CA140657 · NCI · WISTAR INSTITUTE · PI Carlo Maley · 2009 to 2026
$5.0M
Resource Development CoreU54DK137328 · NIDDK · INDIANA UNIVERSITY INDIANAPOLIS · PI Pierre C Dagher · 2023 to 2026
$4.5M
Genetics, Epigenetics, and Risk Prediction for Esophageal AdenocarcinomaR01CA266386 · NCI · FRED HUTCHINSON CANCER CENTER · PI BUAS, MATTHEW FRANK, KOOPERBERG, CHARLES L · 2022 to 2025
$3.6M
Prostate cancer risk stratification via computational 3D pathologyR01CA268207 · NCI · UNIVERSITY OF WASHINGTON · PI Jonathan T.C. Liu, Anant Madabhushi · 2022 to 2026
$3.1M
The role of the senescent microenvironment on cancer initiating cells in the colon.U01AG077920 · NIA · FRED HUTCHINSON CANCER RESEARCH CENTER · PI GRADY, WILLIAM MALLORY · 2021 to 2025
$2.6M
Translational Science of Gastrointestinal Cancer Initiation and ProgressionR50CA233042 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Ming Yu · 2018 to 2026
$2.4M
Multiscale modeling of spatiotemporal evolution in Barrett's esophagusR01CA270235 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Kathleen M. Curtius · 2023 to 2026
$2.3M
Computational 3D pathology for Barrett's esophagus risk stratificationR01DK138948 · NIDDK · UNIVERSITY OF WASHINGTON · PI William Mallory Grady, Jonathan T.C. Liu · 2024 to 2026
$2.2M
The microbiome ecosystem of Barrett's esophagus and progression to cancerR21CA259687 · NCI · FRED HUTCHINSON CANCER CENTER · PI PAULSON, THOMAS G · 2022 to 2023
$407k
NCI NIH HHS R01 CA140657NCI NIH HHS R01 CA266386NCI NIH HHS R01 CA268207NCI NIH HHS R01 CA270235NCI NIH HHS R21 CA259687NCI NIH HHS R50 CA233042NCI NIH HHS U2C CA271902NCI NIH HHS U54 CA274374NIA NIH HHS U01 AG077920NIDDK NIH HHS R01 DK138948NIDDK NIH HHS U54 DK137328Wellcome Trust
6 · The paper itself

Abstract

purposeNondestructive 3-dimensional (3D) pathology methods have emerged in recent years with the potential to enhance standard 2-dimensional histopathology by greatly increasing the amount of tissue sampled by imaging and by providing volumetric morphological context. Another key advantage is that tissues remain intact, allowing re-embedding after imaging for potential long-term storage and future histological or molecular analyses. Here, we aimed to systematically evaluate the impact of 3D pathology protocols on biomolecules-including DNA, RNA, and proteins-and their compatibility with downstream assays. MATERIALS AND

methodsWe applied a previously optimized 3D pathology protocol-involving deparaffinization, fluorescent hematoxylin and eosin-analog staining, optical clearing, and open-top light-sheet microscopy-to formalin-fixed paraffin-embedded specimens of breast, prostate, and head and neck cancer. Following the protocol, tissues were re-embedded in paraffin and compared with paired formalin-fixed paraffin-embedded controls that did not undergo 3D pathology processing. DNA and RNA were extracted and subjected to quality assessments. Amplifiability was tested by PCR and real-time reverse-transcription quantitative PCR (RT-qPCR) of housekeeping genes.

resultsA slight decrease in the average yield and increased fragmentation of both DNA and RNA were observed in the 3D pathology-processed group compared with the control, but PCR amplifiability was largely preserved. Sanger sequencing of the PCR products confirmed accurate sequence determinations, whereas total RNA sequencing indicated that the global transcriptomic profile was largely unchanged. Immunohistochemistry staining of common biomarkers produced comparable signals, suggesting preservation of those proteins after the 3D pathology workflow.

conclusionsThese results demonstrate the basic feasibility of combining 3D pathology with downstream molecular analysis, justifying future work to further explore the integration of 3D pathology with diverse advanced molecular assays.

Indexed as

Imaging, Three-DimensionalBreast NeoplasmsDNAFemaleHumansMaleParaffin EmbeddingProstatic NeoplasmsRNAWorkflowDNARNA3D pathologymolecular integrityopen-top light-sheet microscopytissue processing

Identifiers

PMID42303047
PMCPMC13384737

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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 Socratic graph.