Serial Immunohistochemistry for High-Dimensional Single-Cell Spatial Analysis of Human Kidney Biopsies

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Authors: X. J. Yang, Matthew Caleb Marlin, Alessandra Ida Celia, Chen‐Yu Lee, Alexandre Cammarata-Mouchtouris, Tayte Stephens, Majood Haddad, Liz Bradshaw, Daksh Saksena, Jill P. Buyon, Peter Izmirly, Chaim Putterman
+7 more Diane L. Kamen, Michelle Petri, Accelerating Medicines Partnership: RA/SLE Network, Judith A. James, Joel M. Guthridge, Andrea Fava, Avi Z. Rosenberg
Cited by: 1
References: 11

Abstract

Abstract Background Traditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. Methods Formalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. Results This optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. Conclusion We have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

Fields of Study & Subjects

Single-cell and spatial transcriptomics AI in cancer detection Renal and related cancers

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Publication date
12 August 2026
Document type
Preprint