mIF & IHC image analysis services for digital pathology
Explicyte provides standalone image analysis for digitized mIF and IHC slides, including tissue and cell segmentation, machine learning-assisted phenotyping, quantitative biomarker analysis and spatial analysis.
We can analyze images generated by Explicyte or externally, with pathology input where relevant.
WHY EXPLICYTE
Your digitized slides are in good hands
Fast turnaround
Analysis depth and cohort size determine timeline, with in-depth projects typically scoped around a four-week turnaround.
Pathology input where relevant
Consulting histopathologists can support tissue review, compartment definition and interpretation where appropriate to the study.
Machine learning with expert review
Machine learning accelerates the analysis; a bioinformatician interprets the data.
Explicyte is certified ISO 9001:2015 and ISO 13485:2016 by Euro-Quality System (certificates 260133/1637F/1 and 260133/1637F/2). Scope: Precision Oncology — design and development of tissue biomarkers, and tissue biomarker testing services to support therapeutic decision-making. The certification governs our histopathology workflow; the image-analysis pipeline is delivered by the same team but sits outside the certified scope.
A decade of experience in mIF & IHC image ANALYSISrk
30+ publications featuring our digital pathology data
GABA promotes resistance to immunotherapy in patients with TLS-positive tumors
Adenosine A2B Receptor Promotes Tumor Progression and Metastases in Undifferentiated Pleomorphic Sarcoma
Immune control of functional memory CD8 T cells in normal-appearing vitiligo skin
Fc-optimized CD40 agonistic antibody elicits tertiary lymphoid structure formation and systemic antitumor immunity in metastatic cancer
Reshaping the tumor microenvironment of cold soft-tissue sarcomas with anti-angiogenics: a phase 2 trial of regorafenib combined with avelumab
Regorafenib plus avelumab in advanced gastroenteropancreatic neuroendocrine neoplasms: a phase 2 trial and correlative analysis
Spatially resolved transcriptomics reveal the determinants of primary resistance to immunotherapy in NSCLC with mature tertiary lymphoid structures
Tertiary lymphoid structures and cancer immunotherapy: From bench to bedside
Analysis of PD1, LAG3, TIGIT, and TIM3 expression in human lung adenocarcinoma reveals a 25-gene signature predicting immunotherapy response
Predictive value of tumor microenvironment on pathologic response to neoadjuvant chemotherapy in patients with undifferentiated pleomorphic sarcomas
Identification of microenvironment features associated with primary resistance to anti-PD-1/PD-L1 + antiangiogenesis in gastric cancer through spatial transcriptomics and plasma proteomics
Deciphering the correlation between metabolic activity through 18F-FDG-PET/CT and immune landscape in soft-tissue sarcomas: an insight from the NEOSARCOMICS study
WHAT WE EXTRACT
From digitized slide to quantitative spatial biology
Tissue segmentation
Machine-learning-driven analysis of tissue architecture based on pixel classification.
Tissue segmentation
mIF
NSCLC adenocarcinoma stained with a 5-marker mIF panel (CD8 cyan, CD4 red, CD163 yellow, PanCK green, DAPI blue). Tissue was segmented into superpixels and automatically classified into Tumor (green) and Stroma (yellow) compartments using machine-learning methods trained on histology-annotated regions.
Cell segmentation & phenotyping
Quantitative cell identification and characterization through in-house segmentation workflows
Cell segmentation
mIF
NSCLC adenocarcinoma stained with a 7-marker mIF panel (CD3, CD8, CD20, CD23, CD163, PanCK, DAPI). Cells were segmented, normalized, and phenotyped: macrophages (orange), B cells (white), mature B cells (grey), follicular dendritic cells (green), CD4 T cells (yellow), CD8 T cells (cyan), tumor cells (red).
Spatial analysis
Detailed analyses of spatial relationships, including nearest-neighbor distances, spatial heterogeneity, and cellular colocalization.
An NSCLC adenocarcinoma sample after multiplex mIF phenotyping. Nearest-neighbor distances between PanCK-positive (tumor) and CD8-positive (cytotoxic T) cells are shown as white dashed lines, revealing spatial heterogeneity of the CD8 infiltrate across the tissue.
Custom quantitative analysis
Quantitative analyses tailored to the study question, including marker-expression profiling, object measurements, cell-density analysis, spatial metrics and integration with clinical or biological metadata.
IDO1 expression profiled across TLS Treg-high vs Treg-low subgroups in NSCLC, with progression-free and overall survival stratification. Bessede et al. Clin Cancer Res . 2023
ANALYSIS DEPTH
Choose the level of image analysis you need
| One workflow, three depths Choose the level that matches your project. | Level 1 · QC ~1 week Image quality assessed and QC summarized. | Level 2 · Primary ~2 weeks Cells segmented, phenotyped and quantified. | Level 3 · In-depth ~4 weeks Spatial and comparative biological interpretation. |
|---|---|---|---|
| Slide quality assessment & artifact detection | ✓ | ✓ | ✓ |
| QC report with image metrics | ✓ | ✓ | ✓ |
| Artifact removal & cohort-level image normalization | — | ✓ | ✓ |
| Nucleus & cell segmentation | — | ✓ | ✓ |
| Marker signal extraction | — | ✓ | ✓ |
| Multiplex marker co-expression analysis | — | ✓ | ✓ |
| ML-assisted cell phenotyping | — | ✓ | ✓ |
| Tissue compartment classification (tumor / stroma / immune zones) | — | ✓ | ✓ |
| ROI-level & cell-level quantitative summaries | — | ✓ | ✓ |
| Cell–cell interaction & neighborhood analysis | — | — | ✓ |
| Nearest-neighbor distances & colocalization | — | — | ✓ |
| Comparative analysis across cohorts or conditions | — | — | ✓ |
| Publication-ready figures & full analytical report | — | — | ✓ |
Also available on request
Pathologist-scored biomarkers (TPS · CPS · H-score) · image registration where required · unsupervised phenotype discovery · advanced spatial statistics (Ripley's K, neighborhood enrichment) · multiomic integration.
Paul Marteau, PharmD (study director), Imane Nafia, PhD (CSO), Loïc Cerf, MSc (COO), Alban Bessede, PhD (founder, CEO), Jean-Philippe Guégan, PhD (CTO)
Contact our team
Discuss your image-analysis project
Tell us the image type, staining or marker panel, cohort size and biological question. Our team will review the available data and propose the appropriate analysis depth, timeline and deliverables.
Answers about miF/IHC image analysis
Frequently asked questions
Can Explicyte analyze mIF and IHC slides generated outside Explicyte?
Yes. Our image analysis is a standalone service, so we can support projects even when staining, slide scanning, or assay development was done by another laboratory, CRO, or your internal team.
What types of images do you analyze?
How do I transfer my images?
What are the three levels of image analysis?
We offer three levels of analysis depending on the depth required.
Level 1 — QC assesses image quality, artifacts and dataset consistency and provides a QC summary.
Level 2 — Primary adds image normalization, nucleus and cell segmentation, marker-signal extraction, multiplex co-expression analysis, ML-assisted cell phenotyping, tissue-compartment classification and quantitative cell- and ROI-level outputs.
Level 3 — In-depth adds spatial analyses such as cell–cell interactions, neighborhood analysis, nearest-neighbor distances and colocalization, together with comparative analyses across groups and publication-ready figures and reporting.
Can you quantify spatial relationships between cell populations?
Yes. In-depth analyses can include nearest-neighbor distances, colocalization, neighborhood analysis and other spatial statistics to characterize how cell populations are organized within the tissue microenvironment.
Can you provide tissue segmentation and cell phenotyping?
Yes. We can classify tissue compartments, segment individual cells, extract marker intensities and define cell phenotypes from multiplex marker expression. Machine-learning methods can support tissue classification and cell phenotyping where appropriate, with scientific review of the resulting populations.
Do you support FFPE-based biomarker studies?
Yes. Explicyte has extensive experience analyzing mIF and IHC data from FFPE material, including archived tumor cohorts, preclinical samples and patient biopsies. The analysis workflow is adapted to image quality, staining characteristics and study objectives.
Are your analyses covered by your ISO certification?
Explicyte is certified ISO 9001:2015 and ISO 13485:2016 by Euro-Quality System (certificates 260133/1637F/1 and 260133/1637F/2). The certification scope covers the histopathology workflow — design and development of tissue biomarkers, and tissue biomarker testing services.
Image analysis is delivered by the same team but sits outside the certified scope.
Where a project combines image analysis with activities within our certified histopathology scope, the respective workflows and deliverables are defined separately during study setup.
Do you deliver publication-ready figures and reports?
Yes. Depending on the package, deliverables include quantitative tables, spatial maps, annotated datasets, structured reports, and figures ready for manuscripts, abstracts, or presentations.
Who is this service for?
Biotech and pharmaceutical companies, academic teams, and translational research groups that need expert interpretation of digitized pathology slides.
