Evidence map›Paper›PMID 42466420›Full record

ArticleResearch square2026

H&E-Referenced Multiplex Immunofluorescence Interpretation in TMA Cores: Spatial Co-localization, Cell Feature Validation, and Virtual H&E Generation.

Jun Jiang, Raymond M Moore, Brenna C Novotny, Peiliang Lou, Ruifeng Guo, Zachary C Fogarty, Yuanhang Liu, Hongfang Liu, Ellen L Goode, Stacey J Winham and 2 more

Abstract readPreprint
In one paragraph

Article in Research square, 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 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Jun JiangDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.
Raymond M MooreDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.
Brenna C NovotnyDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.
Peiliang LouDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.
Ruifeng GuoDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL. USA.
Zachary C FogartyDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.
Yuanhang LiuDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.
Hongfang LiuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX. USA.
Ellen L GoodeDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.
Stacey J WinhamDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.
Svetomir N MarkovicDepartment of Oncology, Mayo Clinic, Rochester MN. USA.
Chen WangDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester MN. USA.

Funding

Use of microfluidic tumor cultures to enable clinical trials of therapies for ovarian cancerP50CA136393 · NCI · MAYO CLINIC ROCHESTER · PI ELLEN L. GOODE · 2009 to 2026
$37.0M
Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
Semi-structured Information Retrieval in Clinical Text for Cohort IdentificationR01LM011934 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HERSH, WILLIAM R, LIU, HONGFANG · 2014 to 2025
$5.1M
Quantitative assessment of pre-metastatic immune subversion as a risk factor for melanoma relapseR01CA260259 · NCI · MAYO CLINIC ROCHESTER · PI MARKOVIC, SVETOMIR NENAD · 2022 to 2025
$3.0M
Facilitate Observational Studies of Alzheimer's Disease and Alzheimer's Disease-Related Dementias Using Ontology and Natural Language ProcessingRF1AG072799 · NIA · YALE UNIVERSITY · PI LIU, HONGFANG, TAO, CUI · 2021 to 2021
$2.4M
NCATS NIH HHS U01 TR002062NCI NIH HHS P50 CA136393NCI NIH HHS R01 CA260259NIA NIH HHS RF1 AG072799NLM NIH HHS R01 LM011934
6 · The paper itself

Abstract

Background: Multiplexed immunofluorescence (MxIF) enables high-dimensional immune cell phenotyping and detailed characterization of the tumor immune microenvironment (TIME), but complex cyclic staining can introduce signal degradation and uncertainty in cell-level measurements. In routine practice, pathologists rely on hematoxylin and eosin (H&E) staining as the primary reference for tissue morphology and frequently cross-reference H&E to interpret MxIF findings. Methods: We present a framework for aligning H&E and MxIF images at the tissue microarray (TMA) core level to support cross-modal analysis. Cell nucleus detections from each modality are used as anchor points, formulating the task as a point-set alignment problem. Coherent Point Drift (CPD) is applied for global alignment, followed by graph-matching-based refinement to improve local consistency. Evaluation on ovarian TMAs demonstrates consistent alignment performance for both restained and serial sections. We further explore the use of restained H&E as a reference for MxIF interpretation and investigate virtual H&E generation from MxIF data as a complementary approach when restained H&E is unavailable. Results: The aligned images enable quantitative assessment of cross-modal feature concordance between MxIF-derived measurements and H&E morphology. The generated virtual H&E demonstrated similar cell population compared to the real H&E. Conclusions: Cell-centric alignment can facilitate integrative multimodal histopathology analysis for TMA cores in spatial co-localization, cell feature validation, and virtual H&E generation.

Indexed as

Cell feature validationMultiplex immunofluorescence interpretationTMA core registration

Identifiers

PMID42466420
PMCPMC13370634

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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.