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Jiang, J N

Mayo Clinic

The University of Texas Health Science Center at Houston

Citations 1

H&E-Referenced Multiplex Immunofluorescence Interpretation in TMA Cores: Spatial Co-localization, Cell Feature Validation, and Virtual H&E Generation
Jiang, J N, Moore, Raymond M. , Lou, Peiliang , Guo, Ruifeng, Liu, Yuanhang , Liu, H, Goode, Ellen L. , Novotny, Brenna C. , Fogarty, Zachary C. , Winham, Stacey J. , Markovic, Svetomir N. , Wang, Chen

No container title available | July 7, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.21203/rs.3.rs-9802601/v1 Get It

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.