Multiplexed immunohistochemistry @KU Leuven

 

The KULeuven platform for multiplexed immunohistochemistry offers an end-to-end service for your mIHC projects. Our flagship technology makes use of the MILAN method for mIHC, but we keep  expanding towards other technologies as well. Being fully embedded in the pathology department, the strength of our platform is the years of experience to process pathology-grade samples, our ability to optimise novel immunostains, and provide expert advice towards the interpretation of the results. We also developed an integrated software solution that will allow users to perform spatial, single-cell analysis without the need for extended bioinformatics skills.

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Why multiplexed immunohistochemistry?

Advantages and considerations of multiplexed IHC for your research

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What is MILAN?

Learn the main principles of cyclic multiplexed IHC 

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Data analysis?

Our DISSCOvery software solution makes dealing with terabytes of data simple and accessible to non-experts.

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Service offer and pricing

Practical considerations to run a MILAN project

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Meet the team

A team of research and pathology experts to support every MILAN project

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Available markers

Non-exhaustive list of markers that have been included in prior research

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Interested? 

Reach out to our research team!

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Why Multiplexed Immunohistochemistry?

Single-cell omics aim at charting the different types and properties of all cells in the human body in health and disease. Over the past years, myriads of cellular phenotypes have been defined by methods that mostly required cells to be dissociated and removed from their original microenvironment, thus destroying valuable information about their location and interactions. Growing insights, however, are showing that such information is crucial to understand complex disease states. For decades, pathologists have interpreted cells in the context of their tissue using low-plex antibody- and morphology-based methods. Novel technologies for multiplexed immunohistochemistry are now rendering it possible to perform extended single-cell expression profiling using dozens of protein markers in the spatial context of a single tissue section. The combination of these novel technologies with extended data analysis tools allows us now to study cell-cell interactions, define cellular sociology, and describe detailed aberrations in tissue architecture, as such gaining much deeper insights in disease states.

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Searching for novel biomarker combinations

mIHC plays a critical role in scaling up the analysis of the (tumor) microenvironment in larger cohorts of patients, while still maintaining sufficient resolution to explore multiple pathways at single-cell level. Moreover, the usage of antibodies makes this method highly translational towards a clinical/pathology setting. While researchers typically start a project with unbiased (dissociated) single-cell RNAseq measurements, such insights are generally not easily scaled to larger patient cohorts, nor can be measured in patient tissues from which FFPE is the most common sample type. Our platforms offers a solution to translate your scRNAseq data into a scalable MILAN assay, and help in identifying the most important biomarker combination, for instance to stratify responders vs. non-responding patients. A nice example can be found in a recent study from our team on metastatic melanoma


What is MILAN?

 

The MILAN method (acronym for Multiple iterative labeling by antibody neodeposition) is an established cyclic method for multiplexed immunohistochemistry using immunofluorescent methods. It was first published in 2017 by the group of prof. Giorgio Cattoretti at the University of Milan-Bicocca and was made available in our service facility within the KULeuven Department of Imaging and Pathology and Leuven Institute for Single-cell Omics (LISCO) by one of the direct co-developers of the method (prof. Bosisio).

Our team has invested significantly in automating and scaling up the methodology in close collaboration with its original inventor., characterized by:


  1. the use of conventional antibodies for immunofluorescence with secondary antibody amplification of the signal 
  2. the possibility to stain up to 80 markers at single cell level on the same slide
  3. the possibility to simultaneously process batches of 10-15 slides
  4. staining of whole slide samples without the need to limit the analysis to regions of interest
  5. a rapid implementation of any project-specific antibody of interest, given that a suitable working antibody is commercially available. ​​​​

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The MILAN wet lab: staining tissue sections

 

The MILAN method consists of a cyclic procedure. In each round, samples are (i) stained using an optimised immunofluorescent method, (ii) high resolution images are acquired of each fluorescent channel, and (iii) antibodies are removed (“stripped”) from the tissue according to a carefully developed method to maximize antigen preservation. By performing multiple rounds of stain-image-strip, multiplex panels of 10-80 antibodies can be applied to acquire single-cell data of a single FFPE tissue section. In this procedure, we use a combination of unlabeled primary antibodies (see below) and secondary antibodies (e.g. anti-mouse IgG1, anti-mouse IgG2, anti-rabbit, anti-rat, etc) conjugated with spectrally different fluorophores (FITC, TRITC, Cy5, Cy7). This approach has the advantage that the addition of new markers is straightforward and that, for weak markers, this approach offers a level of signal amplification. Once all rounds are completed, all collected images are precisely superposed to obtain a multi-layered image containing the staining of all the different markers before performing the extended data analysis. 

Overall the technique is based on the adaptation of well-known pathological procedures making it robust and versatile. The method is also compatible with whole slide assessments and can easily be scaled by the generation of tissue microarrays, making it compatible with the analysis of large patient-cohorts in an affordable way. Procedures and recommendations on sample preparation are available below in this document. 

An extensive catalogue of validated antibodies compatible with the MILAN multiplex procedure has been assembled and keeps on growing organically with the numerous projects that are processed by our facility. At the moment, there is a focus on extensive immunophenotyping/microenvironmental analysis which has been applied in the context of cancer and other inflammatory indications.

A schematic of the different steps of the MILAN staining is show below: 

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The MILAN dry lab: data analysis

As such, high-dimensional, single-cell technologies such as the MILAN technology offer unprecedented resolution in the analysis of heterogeneous tissues. However, because dozens of parameters are measured simultaneously in millions of single cells across tens-of-thousands of images across numerous areas of a tissue, data analysis became more and more challenging, requiring specific bioinformatics pipeline. To accelerate the processing of the terabytes of data that are typically collected in such projects, we developed an integrated software tool, named DISSCOvery (Data integration of spatial single-cell omics) allowing data interpretation and subsequent spatial analysis by non-bioinformatician biology/pathology experts. Also, to fully exploit the clinical impact of the collected spatial data, specific statistical approaches are being implemented to extract the most robust and informative biomarker combinations. This is particularly challenging because the dimensions of the measured spatial features (range of 100-10 000 features can be collected) is typically several orders of magnitude larger than the number of analysed patients (~20-100), bearing the risk of creating largely overfitted models that are insufficiently robust. 

 

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Below you can find a schematic overview of the required steps for downstream image analysis using the most commonly used fluorescent, cyclic methods for multiplexed IHC. Images are collected across multiple cycles but still need to be cleaned (QC), corrected (PP), registered/aligned (REG), autofluorescence removed (AF), segmented (SEG), feature extracted (FE), phenotypically annotated (PI), and spatially resolved (SA). A review paper on the various methods and data analysis modalities was published by our team and can be found here.


 

Meet the team

 

Prof. Francesca Bosisio

  • MD, (dermato)pathologist, University Hospitals Leuven (UZLeuven)
  • Adjunct Kliniekhoofd Pathologie UZLeuven
  • Associate professor KULeuven
  • MILAN co-developer
  • co-chair of the KULeuven facility for mIHC
  • Expert in melanoma research

 

 

 

Prof. Frederik De Smet

 

to LPCM

 

Asier Antoranz

  • Bio-engineer
  • postdoctoral fellow
  • head of the MILAN bioinformatics team and developer of the DISSCOvery analysis software tool
 

Nikolina Dubroja

  • labmanager MILAN wetlab
  • primary point of contact for practicalities regarding upcoming or ongoing MILAN projects

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Service offer and pricing

The MILAN philosophy: an end-to-end solution to assist from the beginning to the end in your multiplex IHC analysis


Even with a software at disposal that allows non-bioinformaticians to play around with multiplexed images, we are well aware that no software is still available to answer all the project-specific questions that your research wants to answer. In this sense, we have a team of bioinformaticians that keep on developing and implementing novel algorithms. When new approaches are needed for your project, we offer dedicated bioinformatics services on a fee-for-service basis. Data interpretation can also become challenging. Here, we have a team of pathologists and bioinformaticians at your disposition who can provide expert advice regarding the interpretation of cell types and develop ad-hoc algorithms for more specific, in-depth research questions. 

For every project, we take researchers along to create the best panel that will answer their research questions. This means that every project is different. As such, we prepare a tailored price offer for each individual project.

Interested? 

Reach out to our research team

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Available markers for MILAN multiplexed analysis

Human targets

CD3, CD4, CD8, CD14, CD20, CD31, CD56, CD69, CD123, CD163, CD206, CD1c, CD1b, CD45RA, CD45RO, GRB7, Perforin, PD1, PDL1, Ki67, TTF1, TCF7, Pan-CK, S100, OX40, CD11c, CD11b, TIM3, LAG3, HLA-DR, FOXP3, CD68, CD16, CD73, EOMES, TBET, GATA3, CTLA4, TIGIT, VISTA, RORgt, CD38, TMEM119, CD86, Helios, CXCL13, CD34, CD138, MPO, CD36, CD79a, BLIMP1, ELA2, GNLY, SOX2, NESTIN, SOX10, SOX9, GFAP, CD44, OLIG2, VIMENTIN, PDGFRA, CDK6, MUC5AC, OLFM4, CDX2, ACQ5, PERIOSTIN, NEUROFILAMENT, MUC2, HIF1A, HSP70, MELANA, CDKN2A, TP53, FAP, EGFR, EGFRvIII, ATRX, AXL, MITF, SALL2, AR, PR, BCL2, cMYC, CCR2, CCR7, POU3F2, SMA, IRF4, IRF8, HER2, TSPO, HuC/HuD, COL4, IDO, CC3 (cleaved capsase 3).

Mouse targets

CD3, CD4, CD8, CD19, CD31, CD103, CD133, CD163, CD206, FOXP3, TMEM119, CTLA4, OX40, F4/80, PDL1, PD1, TIM3, LAG3, Ki67, SOX2, GRB7, TCF1/7, MECA79, S100, HLA-DR, CD11b, CD11c, PDGFRA, CD68

Adding new markers

Don't find all your desired markers in the list? Don't worry! We constantly optimise new markers that can be added to existing panels. Reach out to our team to further discuss how to do this.

 
 
 

Key references

1.        Bolognesi, M. M. et al. Multiplex Staining by Sequential Immunostaining and Antibody Removal on Routine Tissue Sections. J. Histochem. Cytochem. 65, 431–444 (2017).

2.        Bosisio, F. M. et al. Functional heterogeneity of lymphocytic patterns in primary melanoma dissected through single-cell multiplexing. Elife(2020). doi:10.7554/eLife.53008

3.        Antunes, A. R. P. et al. Transcriptional profiling of glioblastoma-associated myeloid cells across species and disease stage reveals macrophage competition and functional specialization. Nat. Neurosci. accepted, (2021).

4.         De Smet, F., Antoranz Martinez, A. & Bosisio, F. M. Next-Generation Pathology by Multiplexed Immunohistochemistry. Trends in Biochemical Sciences 46, 80–82 (2021).

5.         Vanmechelen, M. et al. PATH-20. SPATIAL MAPPING OF THERAPY-INDUCED, PATHOLOGICAL CHANGES IN GLIOBLASTOMA AT SINGLE-CELL RESOLUTION. Neuro. Oncol. 23, vi119–vi119 (2021).

5.        Naulaerts, S. et al. Immunogenomic, single-cell and spatial dissection of CD8+T cell exhaustion reveals critical determinants of cancer immunotherapy. bioRxiv 2021.11.22.468617 (2021). doi:10.1101/2021.11.22.468617

7.         Bosisio, F. et al. Mapping the immune landscape in metastatic melanoma reveals localized cell-cell interactions correlating to immunotherapy responsiveness. (2022). doi:10.21203/RS.3.RS-1236531/V1

8.