Systems pathology by multiplexed immunohistochemistry and whole-slide digital image analysis

Abstract

The paradigm of molecular histopathology is shifting from a single-marker immunohistochemistry towards multiplexed detection of markers to better understand the complex pathological processes. However, there are no systems allowing multiplexed IHC (mIHC) with high-resolution whole-slide tissue imaging and analysis, yet providing feasible throughput for routine use. We present an mIHC platform combining fluorescent and chromogenic staining with automated whole-slide imaging and integrated whole-slide image analysis, enabling simultaneous detection of six protein markers and nuclei, and automatic quantification and classification of hundreds of thousands of cells in situ in formalin-fixed paraffin-embedded tissues. In the first proof-of-concept, we detected immune cells at cell-level resolution (n = 128,894 cells) in human prostate cancer, and analysed T cell subpopulations in different tumour compartments (epithelium vs. stroma). In the second proof-of-concept, we demonstrated an automatic classification of epithelial cell populations (n = 83,558) and glands (benign vs. cancer) in prostate cancer with simultaneous analysis of androgen receptor (AR) and alpha-methylacyl-CoA (AMACR) expression at cell-level resolution. We conclude that the open-source combination of 8-plex mIHC detection, whole-slide image acquisition and analysis provides a robust tool allowing quantitative, spatially resolved whole-slide tissue cytometry directly in formalin-fixed human tumour tissues for improved characterization of histology and the tumour microenvironment.

Figure 1

Multiplexed immunohistochemistry for immune cells in prostate tumour (patient 1). (a) Fluorescence (FL) and brightfield (CHR) images are acquired and (b) registered using nuclei information from both images (Hoechst and haematoxylin). (c) Cell segmentation and classification is based on nuclei, epithelium marker expression (Pan-Epi = Pan-CK + ECad), and cell-type specific marker expression (CD45 for leukocytes). Scale bar 500 µm. CHR, chromogenic; FL, fluorescence; IHC, immunohistochemistry; Pan-CK, pan-cytokeratin; Pan-Epi; pan-epithelium.

Figure 2

Multiplexed immunohistochemistry for epithelial cells in prostate tumour. (a) Fluorescence (FL) and brightfield (CHR) images were acquired and (b) images are registered using nuclei information from both images, namely Hoechst and haematoxylin. (c) Cell segmentation and classification is based on epithelium marker expression (Pan-Epi = Pan-CK + E-cadherin) and cell-type specific classifier marker expression in each prostate gland. A given gland is classified as cancerous if basal cell marker expression (CK5 + p63) is lost. Scale bar 500 µm. AMACR, alpha-methylacyl-CoA racemase; AR, androgen receptor; CK5, cytokeratin 5; CK8, cytokeratin 8; CK18, cytokeratin 18; CHR, chromogenic; FL, fluorescence; IHC, immunohistochemistry; Pan-CK, pan-cytokeratin; Pan-Epi; pan-epithelium.

Figure 3

Tissue cytometry of immune cells (patient 1) in prostate cancer. (a) Cell class distribution and (b) CD45 and Pan-Epi expression in all cells (n = 128,894). (c) The proportion of different T cell classes from all leukocytes and proliferation (Ki67⁺) of T cells (*p < 0.05, ***p < 0.001, X² exact test, two-tailed). Pan-Epi, pan-epithelium.

Figure 4

Tissue cytometry of epithelial cells (patient 2) in prostate cancer. (a) Cell class distribution and (b) AMACR and Pan-Epi expression in all cells (n = 83,558) with AMACR positivity threshold indicated (dashed line). (c) Expression profile of Pan-Epi, AMACR, and AR differs between the four cell classes. (d) AR expression in AMACR⁻ cancer cells, AMACR⁺ cancer cells, and in AMACR⁻ benign luminal epithelial cells (***p < 0.001, non-parametric Kolmogorov-Smirnov test). Boxplots indicate median and quartiles. AMACR, alpha-methylacyl-CoA racemase; AR, androgen receptor; Pan-Epi, pan-epithelium.

Figure 5

Summary of key aspects of the described mIHC platform combining (a) rapid implementation of new antibodies for targets of interest, (b) >5-plex mIHC assay design, (c) high-resolution whole-slide image acquisition, and (d,e) open-source high-resolution whole-slide image analysis.